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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

EV-ELM (Electric Vehicle Policies with the Energy Language Model) [SWR-25-156]

Electric Vehicle Policies with the Energy Language Model (EV-ELM) leverages previous work using Large Language Models (LLMs) to find, download, and parse policy information related to energy infrastructure. In this application, we use LLMs to find policy documents related to the permitting and installation of electric vehicle charging infrastructure. This software contains the code to find, download, and parse these documents, while a related data record in the Open Energy Data Initiative (OEDI) will include the resulting output dataset that can be used for downstream analysis. The EV-ELM repository contains code for the EV-ELM project, which focuses on retrieving and processing EV permitting processes using large language models. The project is composed of two pipelines: (1) a web scraping pipeline for discovering and downloading EV permitting documents, and (2) a document parsing and extraction pipeline that processes the downloaded files to produce structured data. The web scraping pipeline is designed to extract relevant information from various websites, while the document parsing pipeline processes and analyzes the extracted documents to derive meaningful insights. Both pipelines depend on the NLR elm repository, which provides essential tools and functionalities for handling and processing the data. The web scraping pipeline is a modified version of the ordinance_gpt example within the elm repository. It has been adapted to fit the specific requirements of the EV-ELM project, ensuring that it effectively captures and processes the necessary information related to EV permitting.

Olson, Reid [National Laboratory of the Rockies (N↗
DOE OSTI2026

Genomic Language model for Annotation of Repetitive Elements (GLARE) v1.0

GLARE (Genomic Language model for Annotation of Repetitive Elements) is a tool that classifies transposable elements (TEs)—the mobile, repetitive DNA sequences that make up large fractions of eukaryotic genomes. GLARE fine-tunes the NTv3-650M genomic language model on a harmonized collection of curated TE sequences from the PanTEon and Repbase reference databases, assigning each input sequence to one of 11 orders and 32 superfamilies in a Wicker-compatible taxonomy. Features. From nucleotide FASTA input, GLARE outputs per-sequence predictions, class summaries, composition figures, and an annotated FASTA. It provides calibrated confidence scores with optional abstention and runs on CPU or GPU. Uses. GLARE serves as a classification component in genome-annotation pipelines, downstream of TE discovery, supporting genome annotation and comparative and evolutionary genomics. Advantages. GLARE is the first repeat-element classifier to leverage a pretrained genomic language model. Combined with multi-database training, this approach outperformed all nine classifiers in the PanTEon benchmark, generalized better to unseen taxonomic clades, and remained robust to sequence orientation—a common failure mode of existing tools.

Bruna, Tomas [Lawrence Berkeley National Laborator↗
DOE OSTI2020

Examining Perceptions of Spanish Language Health Information Among Hispanics Living in the United States: A Qualitative Study Assessing Videos, Brochures, and Websites

Background Nationally, Hispanics experience health disparities manifested as a higher prevalence of chronic diseases in comparison to non-Hispanic Whites. Factors that influence health disparities in this population include inadequate dissemination of culturally and linguistically appropriate health information. Method Our qualitative analysis is based on data obtained from three focus groups with Spanish-speaking Hispanic adults conducted at a clinic in Nebraska. Participants were asked about their perceptions of health information in the form of videos, brochures, and websites. We followed the consolidated criteria for reporting qualitative research and used the theoretical framework of content analysis. Results For videos, attributes associated with higher levels of trust included a friendly health professional, the logo or name of a health care institute, and a reference specifying “se habla Español.” For brochures, attributes associated with trust included use of visual aids, use of numerous colors and big font sizes, the year of printing, and a health care agency logo. For websites, characteristics associated with trust were inclusion of plain language, use of pictures and videos, and date of last update. In all focus groups, participants perceived the use of mixed English/Spanish language in health information from pamphlets and websites as unprofessional. The use of unknown governmental logos in health promotional videos and websites was perceived as untrustworthy information. Conclusions Spanish-speaking Hispanics prefer health information supported by a health care agency or delivered by a health care professional. Health communication strategies should avoid the use of mixed English/Spanish language in sensitive information especially when combined with governmental logos.

De Alba Rosales, Armando↗
DOE OSTI2017

Domain Specific Language Support for Exascale

A multi-institutional project known as D-TEC (short for “Domain- specific Technology for Exascale Computing”) set out to explore technologies to support the construction of Domain Specific Languages (DSLs) to map application programs to exascale architectures. DSLs employ automated code transformation to shift the burden of delivering portable performance from application programmers to compilers. Two chief properties contribute: DSLs permit expression at a high level of abstraction so that a programmer’s intent is clear to a compiler and DSL implementations encapsulate human domain-specific optimization knowledge so that a compiler can be smart enough to achieve good results on specific hardware. Domain specificity is what makes these properties possible in a programming language. If leveraging domain specificity is the key to keep exascale software tractable, a corollary is that many different DSLs will be needed to encompass the full range of exascale computing applications; moreover, a single application may well need to use several different DSLs in conjunction. As a result, developing a general toolkit for building domain-specific languages was a key goal for the D-TEC project. Different aspects of the D-TEC research portfolio were the focus of work at each of the partner institutions in the multi-institutional project. D-TEC research and development work at Rice University focused on on three principal topics: understanding how to automate the tuning of code for complex architectures, research and development of the Rosebud DSL engine, and compiler technology to support complex execution platforms. This report provides a summary of the research and development work on the D-TEC project at Rice University.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Physiological Characterization of Language Comprehension

In this project, our goal was to develop methods that would allow us to make accurate predictions about individual differences in human cognition. Understanding such differences is important for maximizing human and human-system performance. There is a large body of research on individual differences in the academic literature. Unfortunately, it is often difficult to connect this literature to applied problems, where we must predict how specific people will perform or process information. In an effort to bridge this gap, we set out to answer the question: can we train a model to make predictions about which people understand which languages? We chose language processing as our domain of interest because of the well- characterized differences in neural processing that occur when people are presented with linguistic stimuli that they do or do not understand. Although our original plan to conduct several electroencephalography (EEG) studies was disrupted by the COVID-19 pandemic, we were able to collect data from one EEG study and a series of behavioral experiments in which data were collected online. The results of this project indicate that machine learning tools can make reasonably accurate predictions about an individual?s proficiency in different languages, using EEG data or behavioral data alone.

42 ENGINEERING↗
DOE OSTI2022

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Identifying Disinformation Using Rhetorical Devices in Natural Language Models

Foreign disinformation campaigns are strategically organized, extended efforts using disinformation – false or misleading information deliberately placed by an adversary – to achieve some goal. Disinformation campaigns pose severe threats to our nation’s security by misinforming decision makers and negatively influencing their actions when they are operating on limited amounts of evidence. Current efforts rely on subject matter experts to manually identify disinformation, or on computers and traditional natural language processing algorithms to identify patterns in data to calculate the probability that something is disinformation or not. While both have their merits and successes, subject matter experts are unable to keep up with the high volumes of global information and traditional natural language algorithms do not do well in identifying “why” something is disinformation or not. Our hypothesis is that we can identify disinformation by looking at the way someone speaks, in the rhetorical devices they use. We have curated and annotated a dataset designed for multiple natural language processing tasks, but specifically useful for disinformation detection algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Emergent Behaviors and the Limits of Large Language Model Generalization

Over the past couple years, large language models (LLMs) have rapidly grown in size and performance across wide numbers of tasks, leading some to herald them as machine learning models with truly “general” capabilities. We address some of the ways that the language used to describe these models can be both deceptive and a poor framing for making progress towards understanding their capabilities, and also review arguments on to what extent these models can represent and interact with the meaning of language. Emergent behaviors in these highly complex models are unpredictable and research into understanding them is still in early phases, which limits the strength of claims of generality about them. Moreover, while these models demonstrate surprising emergent reasoning capabilities on many tasks, there are still many limitations in generalization that overall high benchmark performance can hide. We review the literature showcasing limitations of LLMs as well as techniques to mitigate or overcome these challenges, while also highlighting how fundamental problems in model evaluation may prevent true claims of generality. We conclude with a practical section on using LLMs on real world problems while appropriately evaluating and validating these tasks to prevent or mitigate the downstream impacts of incorrect results.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1970

Development of a KSC test and flight engineering oriented computer language, Phase 1

Ten, primarily test oriented, computer languages reviewed during the phase 1 study effort are described. Fifty characteristics of ATOLL, ATLAS, and CLASP are compared. Unique characteristics of the other languages, including deficiencies, problems, safeguards, and checking provisions are identified. Programming aids related to these languages are reported, and the conclusions resulting from this phase of the study are discussed. A glossary and bibliography are included. For the reports on phase 2 of the study, see N71-35027 and N71-35029.

Case, C. W.↗
NASA NTRS1972

Advanced software techniques for data management systems. Volume 3: Programming language characteristics and comparison reference

A comparative evaluation was made of eight higher order languages of general interest in the aerospace field: PL/1; HAL; JOVIAL/J3; SPL/J6; CLASP; ALGOL 60; FORTRAN 4; and MAC360. A summary of the functional requirements for a language for general use in manned aerodynamic applications is presented. The evaluation supplies background material to be used in assessing the worth of each language for some particular application.

James, T. A.↗
NASA NTRS1972

FGRAAL: FORTRAN extended graph algorithmic language

The FORTRAN version FGRAAL of the graph algorithmic language GRAAL as it has been implemented for the Univac 1108 is described. FBRAAL is an extension of FORTRAN 5 and is intended for describing and implementing graph algorithms of the type primarily arising in applications. The formal description contained in this report represents a supplement to the FORTRAN 5 manual for the Univac 1108 (UP-4060), that is, only the new features of the language are described. Several typical graph algorithms, written in FGRAAL, are included to illustrate various features of the language and to show its applicability.

Basili, V. R.↗
NASA NTRS1972

Representations of the language recognition problem for a theorem prover

Two representations of the language recognition problem for a theorem prover in first order logic are presented and contrasted. One of the representations is based on the familiar method of generating sentential forms of the language, and the other is based on the Cocke parsing algorithm. An augmented theorem prover is described which permits recognition of recursive languages. The state-transformation method developed by Cordell Green to construct problem solutions in resolution-based systems can be used to obtain the parse tree. In particular, the end-order traversal of the parse tree is derived in one of the representations. An inference system, termed the cycle inference system, is defined which makes it possible for the theorem prover to model the method on which the representation is based. The general applicability of the cycle inference system to state space problems is discussed. Given an unsatisfiable set S, where each clause has at most one positive literal, it is shown that there exists an input proof. The clauses for the two representations satisfy these conditions, as do many state space problems.

Minker, J.↗
NASA NTRS1973

Shuttle avionics and the goal language including the impact of error detection and redundancy management

The relationship is examined between the space shuttle onboard avionics and the ground test computer language GOAL when used in the onboard computers. The study is aimed at providing system analysis support to the feasibility analysis of a GOAL to HAL translator, where HAL is the language used to program the onboard computers for flight. The subject is dealt with in three aspects. First, the system configuration at checkout, the general checkout and launch sequences, and the inventory of subsystems are described. Secondly, the hierarchic organization of onboard software and different ways of introducing GOAL-derived software onboard are described. Also the flow of commands and test data during checkout is diagrammed. Finally, possible impact of error detection and redundancy management on the GOAL language is discussed.

Flanders, J. H.↗
NASA NTRS1973

Ground Operations Aerospace Language (GOAL) textbook

The textbook provides a semantical explanation accompanying a complete set of GOAL syntax diagrams, system concepts, language component interaction, and general language concepts necessary for efficient language implementation/execution.

Dickison, L. R.↗
NASA NTRS1973

Ground Operations Aerospace Language (GOAL). Volume 5: Application Studies

The Ground Operations Aerospace Language (GOAL) was designed to be used by test oriented personnel to write procedures which would be executed in a test environment. A series of discussions between NASA LV-CAP personnel and IBM resulted in some peripheral tasks which would aid in evaluating the applicability of the language in this environment, and provide enhancement for future applications. The results of these tasks are contained within this volume. The GOAL vocabulary provides a high degree of readability and retainability. To achieve these benefits, however, the procedure writer utilizes words and phrases of considerable length. Brief form study was undertaken to determine a means of relieving this burden. The study resulted in a version of GOAL which enables the writer to develop a dialect suitable to his needs and satisfy the syntax equations. The output of the compiler would continue to provide readability by printing out the standard GOAL language. This task is described.

Source record↗
NASA NTRS1974

HAL/S language specification

A programming language for the flight software of the NASA space shuttle program was developed and identified as HAL/S. The language is intended to satisfy virtually all of the flight software requirements of the space shuttle. The language incorporates a wide range of features, including applications-oriented data types and organizations, real time control mechanisms, and constructs for systems programming tasks.

Newbold, P. M.↗
NASA NTRS1975

Use of information-retrieval languages in automated retrieval of experimental data from long-term storage

Problems and methods are discussed of automating information retrieval operations in a data bank used for long term storage and retrieval of data from scientific experiments. Existing information retrieval languages are analyzed along with those being developed. The results of studies discussing the application of the descriptive 'Kristall' language used in the 'ASIOR' automated information retrieval system are presented. The development and use of a specialized language of the classification-descriptive type, using universal decimal classification indices as the main descriptors, is described.

Khovanskiy, Y. D.↗
NASA NTRS1976

A study of systems implementation languages for the POCCNET system

The results are presented of a study of systems implementation languages for the Payload Operations Control Center Network (POCCNET). Criteria are developed for evaluating the languages, and fifteen existing languages are evaluated on the basis of these criteria.

Basili, V. R.↗