SEARCH · Engineering Papers

Results for “semantic”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29

All sources NASA NTRS DOE OSTI

DOE OSTI2016

Oak Ridge Graph Analytics for Medical Innovation (ORiGAMI)

In this era of data-driven decisions and discovery where Big Data is producing Bigger Data, data scientists at the Oak Ridge National Laboratory are leveraging unique leadership infrastructure (e.g., Urika XA and Urika GD appliances) to develop scalable algorithms for semantic, logical and statistical reasoning with Big Data (i.e., data stored in databases as well as unstructured data in documents). ORiGAMI is a next-generation knowledge-discovery framework that is: (a) knowledge nurturing (i.e., evolves seamlessly with newer knowledge and data), (b) smart and curious (i.e. using information-foraging and reasoning algorithms to digest content) and (c) synergistic (i.e., interfaces computers with what they do best to help subject-matter-experts do their best. ORiGAMI has been demonstrated using the National Library of Medicine's SEMANTIC MEDLINE (archive of medical knowledge since 1994).

Roberts, Larry W.↗

DOE OSTI2017

MarFS, a Near-POSIX Interface to Cloud Objects

The engineering forces driving development of “cloud” storage have produced resilient, cost-effective storage systems that can scale to 100s of petabytes, with good parallel access and bandwidth. These features would make a good match for the vast storage needs of High-Performance Computing datacenters, but cloud storage gains some of its capability from its use of HTTP-style Representational State Transfer (REST) semantics, whereas most large datacenters have legacy applications that rely on POSIX file-system semantics. MarFS is an open-source project at Los Alamos National Laboratory that allows us to present cloud-style object-storage as a scalable near-POSIX file system. We have also developed a new storage architecture to improve bandwidth and scalability beyond what’s available in commodity object stores, while retaining their resilience and economy. Additionally, we present a scheme for scaling the POSIX interface to allow billions of files in a single directory and trillions of files in total.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Merged Requests for Better Performance and Productivity in Multithreaded OpenSHMEM

A merged request is a handle representing a group of Remote Memory Access (RMA), Atomic or Collective operations. The merged request can be created either by combining multiple outstanding merged request handles or using the same merged request handle for additional operations. We show that introducing such simple yet powerful semantics in OpenSHMEM provides many productivity and performance advantages. In this paper, we first introduce the interfaces and semantics for creating and using merged request handles. Then, we demonstrate with a merge request that we can achieve better performance characteristics in multithreaded OpenSHMEM application. Particularly, we show one can achieve higher message rate, a higher bandwidth for smaller message, and better computation-communication overlap. Further, we use merged request to realize multithreaded collectives, where multiple threads co-operate to complete the collective operation. Our experimental results show that in a multithreaded OpenSHMEM program, the merged request based RMA operations achieve over 100 Million Messages Per Second (MMPS). It achieves over 10 MMPS compared to 4.5 MMPS with default RMA operations in a single threaded environment. Also, we achieve higher bandwidth for smaller message sizes, close to 100% overlap, and reduce the latency by 60%.

Gorentla Venkata, Manjunath↗

DOE OSTI2018

Analyzing Citation-Distance Networks for Evaluating Publication Impact

Studying citation patterns of scholarly articles has been of interest to many researchers from various disciplines. While the relationship of citations and scientific impact has been widely studied in the literature, in this paper we develop the idea of analyzing the semantic distance of scholarly articles in a citation network (citation-distance network) to uncover patterns that reflect scientific impact. More specifically, we compare two types of publications in terms of their citation-distance patterns, seminal publications and literature reviews, and focus on their referencing patterns as well as on publications which cite them. We show that seminal publications are associated with a larger semantic distance, measured using the content of the articles, between their references and the citing publications, while literature reviews tend to cite publications from a wider range of topics. Our motivation is to understand and utilize this information to create new research evaluation metrics which would better reflect scientific impact.

Herrmannova, Dasha↗

DOE OSTI2019

Behavior specification, finding main, and call graph visualizations

A process transforms compiled software into a semantic form. The process transforms the code into a semantic form. The process analyzes behavior functionality by processing precise programming behavior abstractions stored in a memory and classifies the code as malware based on the code behavior. Another method identifies the starting point of execution of a compiled program. The method calculates a complexity measure by calculating the number of potential execution paths of local functions; identifies the number of arguments passed to local functions; and identifies the starting point of execution of the compiled program. Another method provides interactive, dynamic visualization of a group of related functions wherein a user can explore the rendered graph and select a specific function and display functions that are color coded by their ancestral relation and their function call distance to the selected function.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

A Knowledge Graph Approach for the Secondary Use of Cancer Registry Data

Population-based central cancer registries collect valuable structured and unstructured data primarily for cancer surveillance and research, enhancing insights into clinical features associated with cancer occurrence, cancer treatment, and cancer outcomes to guide interventions which reduce the cancer burden. Cancer registries primarily collect data on (1) cancer type (case or tumor); (2) patient demographics such as age, gender, and residential address at time of diagnosis; (3) planned first course of treatment; and (4) date of last contact, vital status, and cause of death. Cancer registry data is dynamic, structured data, which is extracted from many unstructured sources such as electronic healthcare records, and consolidated for reporting and other purposes. While available advanced analytic tools such as SEER*Stat have the ability to build SAS queries, we, however, explore an innovative knowledge graph approach to organizing cancer registry data for advanced analytics and visualization, which has unique advantages over approaches of existing tools. This innovative knowledge graph approach semantically enriches the data and easily enables linkage with third-party data, which can better explain variation in outcomes. We have developed a prototype knowledge graph based on data from the Louisiana Tumor Registry and other publicly available datasets including Behavioral Risk Factor Surveillance System, Clinical Trials, DBpedia, GeoNames, Rural-Urban Continuum Codes, and Semantic MEDLINE. The resource description framework (RDF) data model was selected to represent our knowledge graph, which contains more than 25 billion triples and is ~4TB in storage size. To exhibit the benefits of the knowledge graph approach, we used scenario specific queries, which find the relationships between cancer treatment sequences and outcomes. To illustrate its ease of use in iterative analysis, the knowledge graph was linked to external datasets for performing complex queries across multiple datasets. In addition, we used knowledge graphs to identify data discrepancies and to handle schema changes. Finally, we visualized the knowledge graph to discover data patterns. Our results demonstrate this graph-based solution enables cancer researchers to execute complex queries and more easily perform iterative analyses to improve understanding of cancer registry data. In the future, we would like to use high-performance computing (HPC) resources for faster-generating hypotheses with clinical potential from our knowledge graph.

Hasan, S M Shamimul↗

DOE OSTI2019

Modeling Building Use and Population Distribution Opportunity Using Open Geosocial Data in Urban Areas

Knowing where people are and knowing how the land in urban areas is being used is critical for assessing human mobility patterns, monitoring population health, planning for transportation services, and responding to natural disasters and emergency situations. While geophysical phenomena like land cover are more easily observed through remote sensing, social phenomena like how a city block or building is being used by the population is generally unobservable through standard remote sensing practices. Current methods and land use data by their nature are also difficult to scale to national or global levels. While land use data are often available in large cities, there are abundant data gaps globally. Furthermore, land use data can often appear relatively coarse in its spatial and semantic resolution (i.e. 1km grid cells rather than building level, and "commercial" rather than "coffee shop"). In particular, there is a need to harvest scalable data and develop methods to create global land use maps. To this end, in this work we combine building footprints and Points of Interest (POI) to estimate land use at the building level, including its mixed-use intensity. Next, we propose a method for defining semantic temporal zones referring to Morning, Day, Evening, and Night times unique to a region, based on the cumulative hourly change in the POIs' hours of operation. We then use these POIs' hours of operation to estimate non-residential population distribution opportunity and land use change over a 24-hour period. We validate our results through manual checks of randomly sampled individual buildings using multiple sources. Finally, we discuss the impact of our results on identifying population distribution opportunity and the reliability of estimating building use.

Sparks, Kevin↗

DOE OSTI2021

Athena: High-Performance Sparse Tensor Contraction Sequence on Heterogeneous Memory

Sparse tensor contraction sequence has been widely employed in many fields, such as chemistry and physics. However, how to efficiently implement the sequence faces multiple challenges, such as redundant computations and memory operations, massive memory consumption, and inefficient utilization of hardware. To address the above challenges, we introduce Athena, a high-performance framework for SpTC sequences. Athena introduces new data structures, leverages emerging Optane-based heterogeneous memory (HM) architecture, and stage parallelism. In particular, Athena introduces shared hash table-represented sparse accumulator to eliminate unnecessary input processing and data migration; Athena uses a novel data-semantic guided dynamic migration solution to make the best use of the Optane-based HM for high performance; Athena also co-runs execution phases with different characteristics to enable high hardware utilization. Evaluating with 12 datasets, we show that Athena brings 327-7362× speedup over the state-of-the-art SpTC algorithm. With the dynamic data placement guided by data semantics, Athena brings performance improvement on Optane-based HM over a state-of-the-art software-based data management solution, a hardware-based data management solution, and PMM-only by 1.58×, 1.82×, and 2.34× respectively.

Liu, Jiawen↗

DOE OSTI2022

Modelica-json: Transforming energy models to digitize the control delivery process

Building simulation models are typically not used to generate the documentation required for bidding and project delivery of commercial building systems, or for their semantic modeling and commissioning. This paper presents a software tool that aids in digitizing the control delivery process, spanning simulation during design to implementation and formal verification during commissioning. The tool can generate from Modelica models digital documentation of control sequences. This digital documentation, along with other project drawings and specifications can be used for project bidding. It can also be used for implementation of control sequences through machineto-machine translation to commercial legacy control products, for which we are currently developing the proposed ASHRAE Standard 231P based on the presented work. Moreover, as-installed control sequences can be formally verified against the design specification, and a semantic model in Brick can be exported to aid in configuration of building analytics and fault detection. The paper presents what we believe is the first translation of a Modelica-implemented control sequence to a native implementation on a commercial control platform, using the webCTRL product line from Automated Logic. The paper also shows how a webCTRL implementation can be formally verified against its Modelica specification. These use cases have all been demonstrated with a prototype implementation that is now being further developed.

Wetter, Michael↗

DOE OSTI2022

Teaching AI when to care about gender

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) concerned with solving language tasks by modeling large amounts of textual data. Some NLP techniques use word embeddings which are semantic models where machine learning (ML) is used to learn to cluster semantically related words by learning about word co-occurrences in the original training text. Unfortunately, these models tend to reflect or even exaggerate biases that are present in the training corpus. Here we describe the Word Embedding Navigator (WEN), which is a tool for exploring word embedding models. We examine a specific potential use case for this tool: interactive discovery and neutralization of gender bias in word embedding models, and compare this human-in-the-loop approach to reducing bias in word embeddings with a debiasing post-processing technique.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

A Data Processing Pipeline for Adversarial Socio-Technical Network Analysis

With the rapid adoption of emerging technologies, there is a need to catalog and model sociotechnical interdependencies that have been historically used to influence the operation of Critical Infrastructure networks including the impacts of mergers and acquisitions, hostile takeovers, and foreign investment. Our research intends to address this need with two primary contributions. First, we have developed a data curation and processing pipeline to generate sociotechnical networks extracted from a variety of data sources including SEC filings and infrastructure asset databases. The pipeline, implemented in Apache Airflow, extracts and normalizes the representation of entities and relations, specified within ontologies. Our intent is to provide an extensible, machine-actionable approach to quickly communicate such models, reproduce previous results, and adapt them to new, unanticipated situations. Second, networks produced by our pipeline enable the development of graph-theoretic metrics that consider the properties of network components in addition to its topology. Metadata associated with network components---whether semantic, temporal, or geospatial---affects the alignment of generated networks with assumptions underlying complexity metrics. Validation of generated networks relative to component types defined by an ontology, may allow the research community to adapt metrics to the semantics of the domains being studied. Generated networks may be processed as knowledge, dynamic, or spatial graphs and enables a variety of analyses including automated reasoning and measures of network complexity. Automated reasoning views extracted entities and relations as a knowledge graph; this enables application of inference rules that represent historically-attested adversarial business methods and applies that behavior to a specific geographic context. Measures of network complexity, including degree distribution, reachability analyses, temporal analysis, and community detection can be adapted to indicate adversarial organizational influence.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Formalization of the functional analysis methodology to improve NPP I and C system design process - 244

Functional analysis is a universal methodology, which is able to support NPP and I and C design process. The paper proposes an approach to combining the functional analysis process and I and C design process into a single I and C functional design process. Operation of a system is considered as a set of functions providing not only the achievement of goal, but also keeping material and energy balances. A notation for graphical representation of semantic network (hierarchy of functions) describing a system is suggested. This network includes four levels, namely functional goals, abstract functions level, process functions level, and equipment level. The elements of the hierarchy are connected by three kinds of relationships, namely: parent-child, main-support, and cause-consequence. Then the semantic network (hierarchy) of functions is converted into a functional network which looks as a directed graph. This graph is used for development of control algorithm and for building of instrumentation and control functions hierarchy. Farther analysis of process functions reveals a set of criteria indicating real-time status and effectiveness of functions. The criteria are used for allocation of instruments and actuators. The process of functional analysis and design is illustrated in application to a simple heat generation system. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments [https://arxiv.org/abs/2403.11872]. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Towards Content Authenticity: Multimodal Fake News Detection and AI-Generated Text Identification

In today’s digital world, the spread of fake news and the rise of AI-generated text have become major threats to content authenticity and public trust. This thesis addresses both challenges through two complementary research directions: detecting fake news using multimodal features, and identifying AI-generated text using semantic and structural reasoning. The first part of the work focuses on fake news detection by introducing a novel model that combines text and image features through a unique rotational attention mechanism. Unlike traditional attention methods, this approach rotates the roles of query, key, and value across modalities to capture deeper interactions. Additionally, the model incorporates external domain information by linking news posts to top-ranked websites from Google search results, which helps assess the credibility of content based on its broader web context. This results in a more reliable and accurate fake news detection system that outperforms existing state-of-the-art methods. The second part presents SGG-ATD, a new framework for detecting AI-generated text. It uses masked language modeling to measure sentence coherence, followed by constructing a graph where keywords—both original and predicted—are connected based on semantic and contextual similarity. A Graph Convolutional Network (GCN) is then used to learn structural relationships within the text for final classification. Experimental results demonstrate that SGG-ATD achieves high F1-scores and consistently outperforms strong baselines. This method contributes to robust AI text detection, supporting accountability and resilience against AI-driven misinformation.

Gupta, Nidhi↗

DOE OSTI2025

LinkML: an open data modeling framework

Background Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, nonstandardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult. Findings LinkML (Linked Data Modeling Language) is an open framework that simplifies the process of authoring, validating, and sharing data. LinkML can describe a range of data structures, from flat, list-based models to complex, interrelated, and normalized models that utilize polymorphism and compound inheritance. It offers an approachable syntax that is not tied to any one technical architecture and can be integrated seamlessly with many existing frameworks. The LinkML syntax provides a standard way to describe schemas, classes, and relationships, allowing modelers to build well-defined, stable, and optionally ontology-aligned data structures. Once defined, LinkML schemas may be imported into other LinkML schemas. These key features make LinkML an accessible platform for interdisciplinary collaboration and a reliable way to define and share data semantics. Conclusions LinkML helps reduce heterogeneity, complexity, and the proliferation of single-use data models while simultaneously enabling compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) data standards. LinkML has seen increasing adoption in various fields, including biology, chemistry, biomedicine, microbiome research, finance, electrical engineering, transportation, and commercial software development. In short, LinkML makes implicit models explicitly computable and allows data to be standardized at their origin. LinkML documentation and code are available at https://linkml.io/.

AI-ready data↗

DOE OSTI2025

Replacing non-biomedical concepts improves embedding of biomedical concepts

Embeddings are semantically meaningful representations of words in a vector space, commonly used to enhance downstream machine learning applications. Traditional biomedical embedding techniques often replace all synonymous words representing biological or medical concepts with a unique token, ensuring consistent representation and improving embedding quality. However, the potential impact of replacing non-biomedical concept synonyms has received less attention. Embedding approaches often employ concept replacement to replace concepts that span multiple words, such as non-small-cell lung carcinoma, with a single concept identifier (e.g., D002289). Also, all synonyms of each concept are merged into the same identifier. Here, we additionally leveraged WordNet to identify and replace sets of non-biomedical synonyms with their most common representatives. This combined approach aimed to reduce embedding noise from non-biomedical terms while preserving the integrity of biomedical concept representations. We applied this method to 1,055 biomedical concept sets representing molecular signatures or medical categories and assessed the mean pairwise distance of embeddings with and without non-biomedical synonym replacement. A smaller mean pairwise distance was interpreted as greater intra-cluster coherence and higher embedding quality. Embeddings were generated using the Word2Vec algorithm applied to a corpus of 10 million PubMed abstracts. Our results demonstrate that the addition of non-biomedical synonym replacement reduced the mean intra-cluster distance by an average of 8%, suggesting that this complementary approach enhances embedding quality. Future work will assess its applicability to other embedding techniques and downstream tasks. Python code implementing this method is provided under an open-source license.

algorithms↗

DOE OSTI2022

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switching and phase transition mechanisms. However, identification of these mechanisms from observational data sets has remained a problem due to a large variety of possible configurations, many of which are degenerate. Here, an approach based on a combination of deep learning-based semantic segmentation, rotationally invariant variational autoencoder (VAE), and non-negative matrix factorization to enable learning of a latent space representation of the data with multiple real-space rotationally equivalent variants mapped to the same latent space descriptors is introduced. By varying the size of training sub-images in the VAE, the degree of complexity in the structural descriptors is tuned from simple domain wall detection to the identification of switching pathways. Importantly, this yields a powerful tool for the exploration of the dynamic data in mesoscopic electron, scanning probe, optical, and chemical imaging. Moreover, this work adds to the growing body of knowledge of incorporating physical constraints into the machine and deep-learning methods to improve learned descriptors of physical phenomena.

36 MATERIALS SCIENCE↗