TOPIC MODELING WITH NATURAL LANGUAGE PROCESSING FOR IDENTIFICATION OF NUCLEAR PROLIFERATION-RELEVANT SCIENTIFIC AND TECHNICAL PUBLICATIONS.
Abstract not provided.
Bisila, Jonathan↗
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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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A Survey Protocol to Assess Meaningfulness and Usefulness of Automated Topic Finding in the NASA Aviation Safety Reporting System
Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants and other involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in a number of ways including entry into an online database, Safety Alert Bulletins and For Your Information Notices, and the CALLBACK newsletter. Key to these publications are the timely processing (de-identification, coding and summarization) of new reports, which is currently done by ASRS expert safety analysts. Thus, we believe topic modelling could decrease effort in ASRS, if topics are comprehensible. Aim: We propose a methodology to evaluate whether automated topic finding using topic modelling provides meaningful and useful topics. Method: We extend the total error survey methodology to evaluate user topic comprehension of machine learning outputs. To accomplish this we performed a literature review to identify existing methods and define a construct for topic comprehension, utilizing existing ASRS synopsis writing practices to more precisely define meaningfulness and usefulness. Results: A survey protocol was created that addresses the limitations of other survey protocols found in the literature review, which we found lacking in rationale and clear protocol definition. Conclusion: The surveying of user understanding in machine learning outputs presents challenges due to the explosion of parameters to control for and the lack of systematic approach presented in the literature. More reproducible work and survey protocols are needed in the literature and our work is one step towards that direction.
topic finding↗
A Software Risk Model
Topics on the slide include:quality, test coverage, functional criticality, exposure, criticality, and risk index.
Lee, Alice↗
Visualizing Temporal Topic Embeddings with a Compass
—Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.
Cluster analysis↗
STExNMF: Spatio-Temporally Exclusive Topic Discovery for Anomalous Event Detection
Understanding newly emerging events or topics associated with a particular region of a given day can provide deep insight on the critical events occurring in highly evolving metropolitan cities. We propose herein a novel topic modeling approach on text documents with spatio-temporal information (e.g., when and where a document was published) such as location-based social media data to discover prevalent topics or newly emerging events with respect to an area and a time point. We consider a map view composed of regular grids or tiles with each showing topic keywords from documents of the corresponding region. To this end, we present a tilebased spatio-temporally exclusive topic modeling approach called STExNMF, based on a novel nonnegative matrix factorization (NMF) technique. STExNMF mainly works based on the two following stages: (1) first running a standard NMF of each tile to obtain general topics of the tile and (2) running a spatiotemporally exclusive NMF on a weighted residual matrix. These topics likely reveal information on newly emerging events or topics of interest within a region. We demonstrate the advantages of our approach using the geo-tagged Twitter data of New York City. We also provide quantitative comparisons in terms of the topic quality, spatio-temporal exclusiveness, topic variation, and qualitative evaluations of our method using several usage scenarios. In addition, we present a fast topic modeling technique of our model by leveraging parallel computing.
Shin, Sungbok↗
Natural Language Processing Techniques for Intelligent Knowledge Management of Safety Reports
Safety, failure, and incident reports are common artifacts across various domains, including aviation and wildfire response. These reports are often mandatory to submit, resulting in the culmination of large repositories of text-based documents. Simultaneously, these reports and corresponding repositories are often only manually analyzed and queried by users via out-of-date search engines. As a consequence, we have been developing the Manager for Intelligent Knowledge Access (MIKA) toolkit, which uses natural language processing to improve information access and reuse. In this presentation, we discuss natural language processing techniques for knowledge discovery and apply these methods to a repository of aerial wildfire mishap reports. Two methods are used for knowledge discovery: topic modeling and named-entity recognition. We use topic modeling to identify hazards and perform a trend analysis to produce a data-driven risk matrix. A custom named-entity recognition model, build from fine tuning a pre-trained language model, is used to identify failure modes, failure causes, failure effects, control processes, and recommendations to aid in failure modes and effects analysis (FMEA). Throughout the presentation, we discuss and apply natural language processing techniques to better leverage the vast amount of information contained in report repositories.
Machine learning↗
SNAP 8 refractory boiler development program - Shell side hydraulic characteristics of a full scale SNAP 8 multiple tube model boiler Topical report no. 5
Shell side hydraulic characteristics of full-scale SNAP 8 multiple-tube model boiler over turbulent Reynolds number from 18,000 to 38,000
Fuller, R. A.↗
Improved large perturbation propulsion models for control system design (1988-1989) and large perturbation models of high velocity propulsion systems (1989-1990) and reduced order propulsion models for control system design (1990-1991)
Methods for modeling high speed propulsion systems will be discussed. Included in this category are internal flow propulsion systems without rotating machinery, such as inlets, ramjets, and scramjets. Among the modeling topics discussed are modeling of linear isentropic flow, heat exchange, gasdynamics, lumped parameter systems, and infinite dimensional systems. Furthermore, a generalized overview of modeling high speed propulsion systems is presented in this collection of papers.
Hartley, Tom T.↗
Evaluation of Engineered Barrier Systems in the Disposition of Spent Nuclear Fuel
This document is a summary of the R&D activities associated with the Engineered Barrier Systems Work Package. Multiple facets of Engineered Barrier Systems (EBS) research were examined in the course of FY18 activities. This report is focused on delivering an update on the status and progress of modelling tools and experimental methods, both of which are essential to understanding and predicting long-term repository performance as part of the safety case. Specifically, the work described herein aims to improve understanding of EBS component evolution and interactions. Utlimately, the EBS Work Package is working towards producing process models for distinct processes that can either be incorporated into performance assessment (PA), or provide critical information for implementing better constraints on barrier performance The main objective of this work is that the models being developed and refined will either be implemented directly into the Generic Disposal System Analysis platform (GDSA), or can otherwise be indirectly linked to the performance assessment by providing improved bounding conditions. In either the case, the expectation is that validated modelling tools will be developed that provide critical input to the safety case. This report covers a range of topics — modelling topics include: thermal-hydrologic-mechanical-chemical coupling (THMC) in buffer materials, comparisons of modelling approaches to optimize computational efficiency, thermal analysis for EBS/repository design, benchmarking of thermal analysis tools, and a preliminary study of buffer re-saturation processes. Experimental work reported, includes: chemical evolution and sorption behavior of clay-based buffer materials and high-pressure, high temperature studies of EBS material interactions. The work leverages international collaborations to ensure that the DOE program is active and abreast of the latest advances in nuclear waste disposal. This includes participation in the HotBENT Field Test, aimed at understanding near-field effects on EBS materials at temperatures above 100 °C, and the analysis of data and characterization of samples from the FEBEX Field Test. Both the FEBEX and HotBENT Field Tests utilize/utilized the Grimsel Test Site in Switzerland, which is situated in a granite host rock. These tests offer the opportunity to understand near field evolution of bentonite buffer at in situ conditions for either a relatively long timescale (18 years for FEBEX) or temperature above 100 °C (HotBENT). Overall, this report provides in depth descriptions of tools and capabilities to investigate nearfield performance of EBS materials (esp. bentonite buffer), as well as tools for drift-scale thermal and thermal-hydrologic analysis critical to EBS and repository design. For a more detailed description of work contained herein, please see Section 10 ("Conclusions") of this document.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Inverse Reinforcement Learning based Bayesian Goal Inference Method for Early Nuclear Proliferation Detection
Traditional methods for detection of nuclear proliferation indicators are usually applied after nuclear proliferation has already occurred. There is a need to advance these methods to perform early detection of nuclear proliferation indicators. In this project, we formulated an early detection problem as a sequential, decision-making, goal inference problem based on research publications of authors, to determine whether it is possible to infer whether an author will publish on a research activity before it has occurred. To develop and test our approach, we selected a civil nuclear activity for our case study. We constructed a state-action-state transition graph from publications of authors associated with the activity and the co-authors of their publications, using titles, abstracts, and author publication sequences. We then used inverse reinforcement learning to model the goal-directed behavior of authors in trajectories that terminate at selected goal states. Using a Bayesian formulation, we computed the probability that authors would reach each selected state from partially observed trajectories of their state transitions in their research topic space. The state with the highest probability was selected as the most probable goal state. Based on our results, we found that 60% of the times we can infer the correct goal state early; sometimes the inference is either delayed, or multiple states could be inferred as goal states. Overall, our results show that it is possible to perform early detection of research activities of authors in a nuclear technology area. Further research is necessary to establish a more accurate understanding of how topic modeling, topic space grid discretization, and the extent of overlap among trajectories of different goal states, affect the goal inference results. The methods developed in this work may be used to enhance data-driven methods for early detection of nuclear proliferation indicators.
97 MATHEMATICS AND COMPUTING↗
ML for microbiomes
The software provides machine learning analysis and visualization to detect patterns in microbiome data, including topic modeling, probabilistic graphical modeling, conventional machine learning methods, and deep learning. The software is written in python and R, it uses some python and R libraries as well as big open-source libraries like sklearn, networkX, pytorch (python), pgmpy (python), and bnlearn (R). It also has a script to use for MALLET and DTM (open-source packages for topic modeling, written in Java).
Kim, Anastasiia↗
Properties of jet fragmentation using charged particles measured with the ATLAS detector in pp collisions at root s=13 TeV
Explore the source record for details and available documents.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information
Detecting and anticipating global proliferation expertise and capability evolution from unstructured, noisy, and incomplete public data streams is a highly desired, but extremely challenging task. Here, in this article, we present our pioneering data-driven approach to support the non-proliferation mission to detect and explain the evolution of proliferation expertise and capability development globally from terabytes of publicly available information (PAI), focusing on our knowledge extraction pipeline and descriptive analytics. We first discuss how we fuse nine open-source data streams, including multilingual data, to convert 4 TB of unstructured data to structured knowledge and encode dynamically evolving proliferation expertise representations—content and context graphs. For this, we rely on natural language processing (NLP) and deep learning (DL) models to perform information extraction, topic modeling, and distributed text representation (aka embedding) learning. We then present interactive, usable, and explainable descriptive analytics to refine domain knowledge and present it in a human-understandable form. Finally, we introduce future work avenues that will leverage our dynamic knowledge representations and descriptive analytics to enable predictive and prescriptive inferences to achieve real-time domain understanding and contextual reasoning about global proliferation expertise and capability evolution.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
From BERTopic to SysML: Informing Model-Based Failure Analysis With Natural Language Processing for Complex Aerospace Systems
The development of emerging complex aerospace systems will require new approaches for capturing safety incident scenarios as early as possible in the design phase. However, for novel systems, relevant data available is limited. In this work, we propose a framework informing model-based mission assurance activities with historical incident reports, lessons learned, or other relevant engineering documents using natural language processing. In doing so, we investigate whether there is useful information in data sets that are relevant, if not identical, to the system under design and whether, through rigorous systems engineering practice, this information can be effectively leveraged through model-based failure analysis. In a worked case study, we apply state-of-the-art topic modeling techniques to two data sets, a mission relevant data set and a system relevant data set. The sets of topics are merged and interpreted to form a preliminary list of failure topics that can be used to inform the identification of off-nominal modes in the model-based failure modes and effects analysis development. Once data from the system in operation is available, it can be used to update the topics identified. By extracting information about likely failures from relevant historical data sets and utilizing model-based mission assurance to ensure relevance and rigor, unanticipated failures can be reduced, and projects can more effectively learn from past missions.
Failure Analysis↗
From BERTopic to SysML: Informing Model-Based Failure Analysis With Natural Language Processing for Complex Aerospace Systems
The development of emerging complex aerospace systems will require new approaches for capturing safety incident scenarios as early as possible in the design phase. However, for novel systems, relevant data available is limited. In this work, we propose a framework informing model-based mission assurance activities with historical incident reports, lessons learned, or other relevant engineering documents using natural language processing. In doing so, we investigate whether there is useful information in data sets that are relevant, if not identical, to the system under design and whether, through rigorous systems engineering practice, this information can be effectively leveraged through model-based failure analysis. In a worked case study, we apply state-of-the-art topic modeling techniques to two data sets, a mission relevant data set and a system relevant data set. The sets of topics are merged and interpreted to form a preliminary list of failure topics that can be used to inform the identification of off-nominal modes in the model-based failure modes and effects analysis development. Once data from the system in operation is available, it can be used to update the topics identified. By extracting information about likely failures from relevant historical data sets and utilizing model-based mission assurance to ensure relevance and rigor, unanticipated failures can be reduced, and projects can more effectively learn from past missions.
Failure Analysis↗
Zika discourse in the Americas: A multilingual topic analysis of Twitter
Article Authors Metrics Comments Media Coverage Abstract Introduction Materials and methods Results Discussion Acknowledgments References Reader Comments (0) Media Coverage (0) Figures Abstract This work examines Twitter discussion surrounding the 2015 outbreak of Zika, a virus that is most often mild but has been associated with serious birth defects and neurological syndromes. We introduce and analyze a collection of 3.9 million tweets mentioning Zika geolocated to North and South America, where the virus is most prevalent. Using a multilingual topic model, we automatically identify and extract the key topics of discussion across the dataset in English, Spanish, and Portuguese. We examine the variation in Twitter activity across time and location, finding that rises in activity tend to follow to major events, and geographic rates of Zika-related discussion are moderately correlated with Zika incidence (ρ = .398).
60 APPLIED LIFE SCIENCES↗
Structure and Performance of GFDL's CM4.0 Climate Model
We illustrate the Geophysical Fluid Dynamics Laboratory's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the preindustrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top-of-atmosphere fluxes, surface temperature, and precipitation; reduced double Intertropical Convergence Zone bias; dramatically improved representation of ocean boundary currents; a high-quality simulation of climatological Arctic sea ice extent and its recent decline; and excellent simulation of the El Niño-Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas; an inaccurate Antarctic sea ice simulation; precipitation and wind composites still affected by the equatorial cold tongue bias; muted variability in the Atlantic Meridional Overturning Circulation; strong 100 year quasiperiodicity in Southern Ocean ventilation; and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.
54 ENVIRONMENTAL SCIENCES↗
The present state and the future direction of eddy viscosity models
Information is given in viewgraph form on the present state and future direction of eddy viscosity models. Topics covered include the eddy viscosity dilemma, two-equation models, equations of motion, free shear flows, incompressible free shear flows, model-predicted boundary layer structure, defect layer analysis, the effects of pressure gradients, viscous sublayer structure, wall functions and viscous damping, viscous damping for kappa-omega, the effects of compressibility, perturbation analysis of the wall layer, an alternative compressibility term, unsteady boundary layers, incompressible separation, backstep results, and compressible separation.
Wilcox, David C.↗