Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “explainability”

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.

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

At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

CARES Helps Explain Secondary Organic Aerosols

What happens when urban man-made pollution mixes with what we think of as pristine forest air? To know more about what this interaction means for the climate, the Carbonaceous Aerosol and Radiative Effects Study, or CARES, field campaign was designed in 2010. The sampling strategy during CARES was coordinated with CalNex 2010, another major field campaign that was planned in California in 2010 by the California Air Resources Board (CARB), the National Oceanic and Atmospheric Administration (NOAA), and the California Energy Commission (CEC). "We found two things. When urban pollution mixes with forest pollutions we get more secondary organic aerosols," said Rahul Zaveri, FCSD scientist and project lead on CARES. "SOAs are thought to be formed primarily from forest emissions but only when they interact with urban emissions. The data is saying that there will be climate cooling over the central California valley because of these interactions." Knowledge gained from detailed analyses of data gathered during the CARES campaign, together with laboratory experiments, is being used to improve existing climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

NREL Explains the Higher Cellulolytic Activity of a Vital Microorganism

The discovery of a new mode of action by C. thermocellum to convert biomass to biofuels is significant because the bacterium is already recognized as one of the most effective in the biosphere. Researchers found that, in addition to using common cellulase degradation mechanisms attached to cells, C. thermocellum also uses a new category of cell-free scaffolded enzymes. The new discovery will influence the strategies used to improve the cellulolytic activity of biomass degrading microbes going forward. Better understanding of this bacterium could lead to cheaper production of ethanol and drop-in fuels. Also, this discovery demonstrates that nature's biomass conversion behaviors are not fully understood and remain as opportunities for future microbial/enzyme engineering efforts.

09 BIOMASS FUELS↗
DOE OSTI2016

Dark Matter and Super Symmetry: Exploring and Explaining the Universe with Simulations at the LHC

The Large Hadron Collider (LHC) at CERN in Geneva, Switzerland, is one of the largest machines on this planet. It is built to smash protons into each other at unprecedented energies to reveal the fundamental constituents of our universe. The 4 detectors at the LHC record multi-petabyte datasets every year. The scientific analysis of this data requires equally large simulation datasets of the collisions based on the theory of particle physics, the Standard Model. The goal is to verify the validity of the Standard Model or of theories that extend the Model like the concepts of Supersymmetry and an explanation of Dark Matter. I will give an overview of the nature of simulations needed to discover new particles like the Higgs boson in 2012, and review the different areas where simulations are indispensable: from the actual recording of the collisions to the extraction of scientific results to the conceptual design of improvements to the LHC and its experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Mechanistic and Data-Driven Agent-Based Models to Explain Human Behavior in Online Networked Group Anagram Games

In anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goalsetting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data-driven models of human reasoning to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human reasoning and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Explaining Multimodal Deceptive News Prediction Models

Social media plays a valuable role in rapid news dissemination, but it also serves as a vehicle to propagate unverified information. For example, news shared on Facebook or Twitter may actually contain disinformation, propaganda, hoaxes, conspiracies, clickbait or satire. This paper presents an in-depth analysis of the behavior of suspicious news classification models including error analysis and prediction confidence. We consider five deep learning architectures that leverage combinations of text, linguistic and image input signals from tweets. The behavior of these models is analyzed across four suspicious news prediction tasks. Our findings include that models leveraging only the text of tweets outperform those leveraging only the image (by 3-13% absolute in F-measure), and that models that combine image and text signals with linguistic cues e.g., biased and subjective language markers can, but do not always, perform even better. Finally, our main contribution is a series of analyses, in which we characterize text and image traits of our classes of suspicious news and analyze patterns of errors made by the various models to inform the design of future deceptive news prediction models.

deep learning, social media, deception↗