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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Frog: Asynchronous Graph Processing on GPU with Hybrid Coloring Model

GPUs have been increasingly used to accelerate graph processing for complicated computational problems regarding graph theory. Many parallel graph algorithms adopt the asynchronous computing model to accelerate the iterative convergence. Unfortunately, the consistent asynchronous computing requires locking or atomic operations, leading to significant penalties/overheads when implemented on GPUs. As such, coloring algorithm is adopted to separate the vertices with potential updating conflicts, guaranteeing the consistency/correctness of the parallel processing. Common coloring algorithms, however, may suffer from low parallelism because of a large number of colors generally required for processing a large-scale graph with billions of vertices. We propose a light-weight asynchronous processing framework called Frog with a preprocessing/hybrid coloring model. The fundamental idea is based on Pareto principle (or 80-20 rule) about coloring algorithms as we observed through masses of realworld graph coloring cases. We find that a majority of vertices (about 80%) are colored with only a few colors, such that they can be read and updated in a very high degree of parallelism without violating the sequential consistency. Accordingly, our solution separates the processing of the vertices based on the distribution of colors. In this work, we mainly answer three questions: (1) how to partition the vertices in a sparse graph with maximized parallelism, (2) how to process large-scale graphs that cannot fit into GPU memory, and (3) how to reduce the overhead of data transfers on PCIe while processing each partition. Here, we conduct experiments on real-world data (Amazon, DBLP, YouTube, RoadNet-CA, WikiTalk and Twitter) to evaluate our approach and make comparisons with well-known non-preprocessed (such as Totem, Medusa, MapGraph and Gunrock) and preprocessed (Cusha) approaches, by testing four classical algorithms (BFS, PageRank, SSSP and CC). On all the tested applications and datasets, Frog is able to significantly outperform existing GPU-based graph processing systems except Gunrock and MapGraph. MapGraph gets better performance than Frog when running BFS on RoadNet-CA. The comparison between Gunrock and Frog is inconclusive. Frog can outperform Gunrock more than 1.04X when running PageRank and SSSP, while the advantage of Frog is not obvious when running BFS and CC on some datasets especially for RoadNet-CA.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Generating Euler Diagrams Through Combinatorial Optimization

Abstract Can a given set system be drawn as an Euler diagram? We present the first method that correctly decides this question for arbitrary set systems if the Euler diagram is required to represent each set with a single connected region. If the answer is yes, our method constructs an Euler diagram. If the answer is no, our method yields an Euler diagram for a simplified version of the set system, where a minimum number of set elements have been removed. Further, we integrate known wellformedness criteria for Euler diagrams as additional optimization objectives into our method. Our focus lies on the computation of a planar graph that is embedded in the plane to serve as the dual graph of the Euler diagram. Since even a basic version of this problem is known to be NP‐hard, we choose an approach based on integer linear programming (ILP), which allows us to compute optimal solutions with existing mathematical solvers. For this, we draw upon previous research on computing planar supports of hypergraphs and adapt existing ILP building blocks for contiguity‐constrained spatial unit allocation and the maximum planar subgraph problem. To generate Euler diagrams for large set systems, for which the proposed simplification through element removal becomes indispensable, we also present an efficient heuristic. We report on experiments with data from MovieDB and Twitter. Over all examples, including 850 non‐trivial instances, our exact optimization method failed only for one set system to find a solution without removing a set element. However, with the removal of only a few set elements, the Euler diagrams can be substantially improved with respect to our wellformedness criteria.

Computer Science↗
DOE OSTI2015

TU-CD-304-11: Veritas 2.0: A Cloud-Based Tool to Facilitate Research and Innovation

Purpose: We introduce Veritas 2.0, a cloud-based, non-clinical research portal, to facilitate translation of radiotherapy research ideas to new delivery techniques. The ecosystem of research tools includes web apps for a research beam builder for TrueBeam Developer Mode, an image reader for compressed and uncompressed XIM files, and a trajectory log file based QA/beam delivery analyzer. Methods: The research beam builder can generate TrueBeam readable XML file either from scratch or from pre-existing DICOM-RT plans. DICOM-RT plan is first converted to XML format and then researcher can interactively modify or add control points to them. Delivered beam can be verified via reading generated images and analyzing trajectory log files. Image reader can read both uncompressed and HND-compressed XIM images. The trajectory log analyzer lets researchers plot expected vs. actual values and deviations among 30 mechanical axes. The analyzer gives an animated view of MLC patterns for the beam delivery. Veritas 2.0 is freely available and its advantages versus standalone software are i) No software installation or maintenance needed, ii) easy accessibility across all devices iii) seamless upgrades and iv) OS independence. Veritas is written using open-source tools like twitter bootstrap, jQuery, flask, and Python-based modules. Results: In the first experiment, an anonymized 7-beam DICOM-RT IMRT plan was converted to XML beam containing 1400 control points. kV and MV imaging points were inserted into this XML beam. In another experiment, a binary log file was analyzed to compare actual vs expected values and deviations among axes. Conclusions: Veritas 2.0 is a public cloud-based web app that hosts a pool of research tools for facilitating research from conceptualization to verification. It is aimed at providing a platform for facilitating research and collaboration. I am full time employee at Varian Medical systems, Palo Alto.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Data depth based clustering analysis

Here, this paper proposes a new algorithm for identifying patterns within data, based on data depth. Such a clustering analysis has an enormous potential to discover previously unknown insights from existing data sets. Many clustering algorithms already exist for this purpose. However, most algorithms are not affine invariant. Therefore, they must operate with different parameters after the data sets are rotated, scaled, or translated. Further, most clustering algorithms, based on Euclidean distance, can be sensitive to noises because they have no global perspective. Parameter selection also significantly affects the clustering results of each algorithm. Unlike many existing clustering algorithms, the proposed algorithm, called data depth based clustering analysis (DBCA), is able to detect coherent clusters after the data sets are affine transformed without changing a parameter. It is also robust to noises because using data depth can measure centrality and outlyingness of the underlying data. Further, it can generate relatively stable clusters by varying the parameter. The experimental comparison with the leading state-of-the-art alternatives demonstrates that the proposed algorithm outperforms DBSCAN and HDBSCAN in terms of affine invariance, and exceeds or matches the ro-bustness to noises of DBSCAN or HDBSCAN. The robust-ness to parameter selection is also demonstrated through the case study of clustering twitter data.

58 GEOSCIENCES↗
DOE OSTI2016

Facility Detection and Popularity Assessment from Text Classification of Social Media and Crowdsourced Data

Advances in technology have continually progressed our understanding of where people are, how they use the environment around them, and why they are at their current location. Having a better knowledge of when various locations become popular through space and time could have large impacts on research fields like urban dynamics and energy consumption. In this paper, we discuss the ability to identify and locate various facility types (e.g. restaurant, airport, stadiums) using social media, and assess methods in determining when these facilities become popular over time. We use natural language processing tools and machine learning classifiers to interpret geotagged Twitter text and determine if a user is seemingly at a location of interest when the tweet was sent. On average our classifiers are approximately 85% accurate varying across multiple facility types, with a peak precision of 98%. By using these methods to classify unstructured text, geotagged social media data can be an extremely useful tool to better understanding the composition of places and how and when people use them.

crowd-sourced data↗
DOE OSTI2019

Using Social Media to Geo-Target Emergency Management Efforts

The ubiquity of social media data has increased their use during emergency management (EM) in near real-time. For instance, during Haiti Earthquake (2010) and Hurricane Harvey (2017), Ushahidi and Twitter were used respectively for emergency response and rescue operations. Nonetheless, social media data tend to contain [ir]relevant information to be useful for disaster analytics for EM efforts. In this study, geo-tagged tweets obtained for 2013 Colorado flooding were analyzed to determine (i) what kind of situational awareness (SA) information could be extracted from tweets for emergency response and (ii) what is the spatio-temporal distribution of such information. The results indicate that tweets generated before September 12th (day of heavy precipitation) were non-relevant, but tweets generated on and following September 12th contained crisis information (about the event and its impacts), warnings, preparatory information. Next phase of this study will focus on developing a framework to integrate SA information with physical risk and social vulnerability to geo-target EM efforts.

Kar, Bandana↗
DOE OSTI2019

Learning from Dynamic User Interaction Graphs to Forecast Diverse Social Behavior

Most of the existing graph analytics for understanding social be- havior focuses on learning from static rather than dynamic graphs using hand-crafted network features or recently emerged graph embeddings learned independently from a downstream predictive task, and solving predictive (e.g., link prediction) rather than fore- casting tasks directly. To address these limitations, we propose (1) a novel task – forecasting user interactions over dynamic social graphs, and (2) a novel deep learning, multi-task, node-aware at- tention model that focuses on forecasting social interactions, going beyond recently emerged approaches for learning dynamic graph embeddings. Our model relies on graph convolutions and recurrent layers to forecast future social behavior and interaction patterns in dynamic social graphs. We evaluate our model on the ability to forecast the number of retweets and mentions of a specific news source on Twitter (focusing on deceptive and credible news sources) with R2 of 0.79 for retweets and 0.81 for mentions. An additional evaluation includes model forecasts of user-repository interactions on GitHub and comments to a specific video on YouTube with a mean absolute error close to 2% and R2 exceeding 0.69. Our results demonstrate that learning from connectivity information over time in combination with node embeddings yields better forecasting results than when we incorporate the state-of-the-art graph em- beddings e.g., Node2Vec and DeepWalk into our model. Finally, we perform in-depth analyses to examine factors that influence model performance across tasks and different graph types e.g., the influence of training and forecasting windows as well as graph topological properties.

Shrestha, Prasha↗
DOE OSTI2016

Environmental and Molecular Science Laboratory Arrow

Arrows is a software package that combines NWChem, SQL and NOSQL databases, email, and social networks (e.g. Twitter, Tumblr) that simplifies molecular and materials modeling and makes these modeling capabilities accessible to all scientists and engineers. EMSL Arrows is very simple to use. The user just emails chemical reactions to arrows@emsl.pnnl.gov and then an email is sent back with thermodynamic, reaction pathway (kinetic), spectroscopy, and other results. EMSL Arrows parses the email and then searches the database for the compounds in the reactions. If a compound isn't there, an NWChem calculation is setup and submitted to calculate it. Once the calculation is finished the results are entered into the database and then results are emailed back.

Source record↗
DOE OSTI2011

Crowdsourcing, citizen sensing and Sensor Web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples

The PIE Activity Awareness Environment is designed to be an adaptive data triage and decision support tool that allows role and activity based situation awareness through a dynamic, trainable filtering system. This paper discusses the process and methodology involved in the application as well as some of its capabilities. 'Wikification of GIS by the masses' is a phrase-term first coined by Kamel Boulos in 2005, two years earlier than Goodchild's term 'Volunteered Geographic Information'. Six years later (2005-2011), OpenStreetMap and Google Earth (GE) are now full-fledged, crowdsourced 'Wikipedias of the Earth' par excellence, with millions of users contributing their own layers to GE, attaching photos, videos, notes and even 3-D (three dimensional) models to locations in GE. From using Twitter in participatory sensing and bicycle-mounted sensors in pervasive environmental sensing, to creating a 100,000-sensor geo-mashup using Semantic Web technology, to the 3-D visualisation of indoor and outdoor surveillance data in real-time and the development of next-generation, collaborative natural user interfaces that will power the spatially-enabled public health and emergency situation rooms of the future, where sensor data and citizen reports can be triaged and acted upon in real-time by distributed teams of professionals, this paper offers a comprehensive state-of-the-art review of the overlapping domains of the Sensor Web, citizen sensing and 'human-in-the-loop sensing' in the era of the Mobile and Social Web, and the roles these domains can play in environmental and public health surveillance and crisis/disaster informatics. We provide an in-depth review of the key issues and trends in these areas, the challenges faced when reasoning and making decisions with real-time crowdsourced data (such as issues of information overload, 'noise', misinformation, bias and trust), the core technologies and Open Geospatial Consortium (OGC) standards involved (Sensor Web Enablement and Open GeoSMS), as well as a few outstanding project implementation examples from around the world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Ten simple rules for drawing scientific comics

Institutions around the world are fighting to improve science communication all the time. From calls for journal papers to be simplified to encouraging scientists to take more of an active role through community engagement, there is an impetus to demystify and improve public understanding and engagement with science. Technology has greatly helped expand the range of learning styles that a lecturer can call on to reach people in new ways. Social media outlets like Twitter, Facebook, Instagram, and Tumblr have expanded the reach of science communication within and across scientific disciplines and to the lay public. Here, with all the videos, interactive quizzes, and instant feedback it can be easy to overlook one of the simplest methods for communicating complex ideas: comics.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Predicting Foreign Language Usage from English-Only Social Media Posts

Social media is known for its multicultural and multilingual interactions, a natural product of which is code-mixing. Multilingual speakers mix languages they tweet to address a different audience, express certain feelings, or attract attention. This paper presents a large-scale analysis of 6 million tweets produced by 23 thousand multilingual users speaking 11 other languages besides English. We rely on this multilingual corpus to build predictive models for a novel task – inferring non- English languages that users speak exclusively from their English tweets. We contrast the predictive power of different linguistic signals and report that lexical content and syntactic structure of English tweets are the most predictive of non-English languages that users speak on Twitter. By analyzing cross-lingual transfer – the influence of non-English languages on various levels of linguistic performance in English, we present novel findings on stylistic and syntactic variations across speakers of 11 languages.

code-swicthing↗
DOE OSTI2017

Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast

People around the globe respond to major real world events through social media. To study targeted public sentiments across many languages and geographic locations, we introduce multilingual connotation frames: an extension from English connotation frames of Rashkin et al. (2016) with 10 additional European languages, focusing on the implied sentiments among event participants engaged in a frame. As a case study, we present large scale analysis on targeted public sentiments using 1.2 million multilingual connotation frames extracted from Twitter. We rely on connotation frames to build models to forecast country-specific connotation dynamics – perspective change over time towards salient entities and events. Our results demonstrate that connotation dynamics can be accurately predicted up to half a week in advance.

connotation frames↗
DOE OSTI2011

Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

TweetAurora.com – Citizen Scientists Experiencing the Extremes of Space Weather

On the evening of October 24, 2011 a geomagnetic storm raged across Europe and the United States. Aurora was visible as far south as Alabama in the US, and social media networks helped transmit the visibility of the lights. The purpose of this proposal is a proof of concept demonstration for collecting, analyzing, and interpreting data on the dynamics and evolution of auroral events via real-time social media, which acts as a sensor array distributed in both time and space. We will validate the efficacy of this observational framework by comparison to measurements and predictions of geomagnetic activity as provided by the NOAA Space Weather Prediction Center. We hypothesize that time-domain volunteered information (VI) from the Twitter social network can be derived with sufficient integrity to track events as they unfold and possibly predict events and their impending severity. By comparing the derived information with other space-based and ground-based measurements of an event, we further hypothesize that the derived information can be used as a complementary data source for scientific research of the event.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

State-of-the-Art of Social Media Analytics Research

Social media consists of the tools, techniques, and technologies that use the internet to facilitate communication in an open environment. Types of social media include social networking sites (Facebook), microblogs (Twitter), blogs (Blogspot), chat (AIM), open-source mapping (Wikimapia), and photo and video sharing (Flikr, Picasa, YouTube). Current research in social media analytics tends to focus around three main domains: content analysis (popular topics, and sentiment/mood), group/network analysis (define users within a group, characterize how group members interact, and identify influential users), and prediction of real-world events or characteristics. Overviews of the most recent capabilities in each area are presented below. A brief explanation of “social media as a sensor” and challenges in future social media analytics research is included at the end.

Social Media Analytics↗
DOE OSTI2014

Validating agent based models through virtual worlds.

As the US continues its vigilance against distributed, embedded threats, understanding the political and social structure of these groups becomes paramount for predicting and dis- rupting their attacks. Agent-based models (ABMs) serve as a powerful tool to study these groups. While the popularity of social network tools (e.g., Facebook, Twitter) has provided extensive communication data, there is a lack of ne-grained behavioral data with which to inform and validate existing ABMs. Virtual worlds, in particular massively multiplayer online games (MMOG), where large numbers of people interact within a complex environ- ment for long periods of time provide an alternative source of data. These environments provide a rich social environment where players engage in a variety of activities observed between real-world groups: collaborating and/or competing with other groups, conducting battles for scarce resources, and trading in a market economy. Strategies employed by player groups surprisingly re ect those seen in present-day con icts, where players use diplomacy or espionage as their means for accomplishing their goals. In this project, we propose to address the need for ne-grained behavioral data by acquiring and analyzing game data a commercial MMOG, referred to within this report as Game X. The goals of this research were: (1) devising toolsets for analyzing virtual world data to better inform the rules that govern a social ABM and (2) exploring how virtual worlds could serve as a source of data to validate ABMs established for analogous real-world phenomena. During this research, we studied certain patterns of group behavior to compliment social modeling e orts where a signi cant lack of detailed examples of observed phenomena exists. This report outlines our work examining group behaviors that underly what we have termed the Expression-To-Action (E2A) problem: determining the changes in social contact that lead individuals/groups to engage in a particular behavior. Results from our work indicate that virtual worlds have the potential for serving as a proxy in allocating and populating behaviors that would be used within further agent-based modeling studies.

Lakkaraju, Kiran↗
DOE OSTI2016

EERE Showcase Event (Solar Decathlon 2015)

The goal of the Orange County Great Park Corporation (Great Park) is to successfully host the U.S. Department of Energy Solar Decathlon 2015. In furtherance of that goal, tasks to be performed within the current reporting period include the following: Task 1.0 Arrange Site Team Visits for January 2015 The Great Park arranged appropriate meeting space for the site team visits over a three-day period, January 8, 2015 through January 10, 2015. Instead of a meeting in Hanger 244, the DOE requested a different meeting space. The working team met in the Operations offices on January 8th. The student teams were welcomed at the City of Irvine’s Lakeview Senior Center on January 9th, and came back on January 10th for breakout sessions. Task 2.0 Outreach Activities The following outreach activities related to the U.S. Department of Energy Solar Decathlon 2015 occurred during and prior to the event: • Promoted the return of the Solar Decathlon 2015 on the City’s website. (cityofirvine.org) • Promoted the return of the Solar Decathlon 2015 through the City’s and Great Park’s social media channels, including Facebook and Twitter. (facebook.com/cityofirvine, facebook.com/orangecountygreatpark, twitter.com/City_of_Irvine, twitter.com/ocgreatpark) • Promoted the return of the Solar Decathlon 2015 and student visit through a City Council Announcement. • Worked to set-up meetings between the U.S. Department of Energy team and potential donors/key stakeholders in Irvine. • Began ICTV filming and coverage of the Solar Decathlon 2015 teams. This includes student team interviews, interview with Richard King and b-roll footage. • Facilitated an interview with Sarah Farrar and the Orange County Register during the recent student visit in January • Information in May was provided to Irvine Unified School District and Tustin Unified School District promoting the three Education Days that the DOE will host during the event. More DOE information is due in August, which will be forwarded to the school districts that will provide important information for school tours. • A promotional ICTV video has been sent through the Irvine Chamber of Commerce and to dozens of businesses in Irvine promoting the Solar Decathlon 2015 and inviting attendance. • Cover story of Fall Inside Irvine magazine detailed teams competing in Solar Decathlon. Magazine goes to more than 100,000 Irvine residences. • Produced public service announcement with KPCC radio to air 9/28-10/16. Outreach also included a web banner on the station’s website. • Full-page advertisements in special sections of the Orange County Register, including UCI 50th Anniversary magazine that went to over 1 million readers of the Register, the Riverside Press Enterprise and the L.A. Times; Best of Orange County magazine; and Solar Decathlon special section. • Full-page ad in Urban Land magazine Sept./Oct. issue. • Produced ad for Irvine Global Village Festival brochure (tens of thousands in attendance at event.) • Ten posters displayed at Irvine Company properties throughout the City, including the Irvine Spectrum Center. • Rack cards promoting Solar Decathlon displayed at Irvine Spectrum Center, Discovery Science Center, Orange County Farm Bureau (at farmers markets) and at City facilities. • Tote bags promoting Solar Decathlon filled with magnets and rack cards on the event distributed at Irvine Global Village Festival, Great Park farmers market and UCI Festival of Discovery; some 8,000 bags handed out. • E-blast from City of Irvine Community Services Department included information on Solar Decathlon. (List contains 51,000 recipients.) • E-blast to Irvine Co. mailing list sent out 9/30. Web banner posted at shopirvinespectrumcenter.com. • E-blast sent to Orange County Register mailing list on 10/6. • Web banner posted on Orange County Register’s homepage. • E-blast sent by Irvine Chamber of Commerce on 10/9. • E-blast using City’s GovDelivery to 2,100 on 10/12. • Produced additional ads for the Orange County Register to fulfill the in-kind agreement between the DOE and the Register: Friday, Oct. 9, Local front page strip ad; Saturday, Oct. 10, half-page Home & Garden section ad; Sunday, Oct. 11, full page Local section ad; Wednesday, Oct. 14, ½ page Main or Local ad; Friday, Oct. 16, full Local or Main section ad; Saturday, Oct. 17, half-page Home & Garden section ad; Sunday Oct. 18, full page Local or Main ad. • Produced two additional Register ads promoting final days of the event: Full page Main or Local ad for Thursday, Oct. 15 and full page ad in Irvine World News weekly publication. • Produced separate press releases on Solar Decathlon, Volunteer Effort, Children’s Activities Area and Final Days. • Produced and distributed Children’s Activities Days rack cards. • Continued to promote the event on the City’s webpage, Great Park webpage and social media channels. • ICTV produced the “Solar Decathlon Minute” videos, which were posted on the City’s YouTube channel and the solardecathlon.gov website. • Four-minute video promoting Solar Decathlon shown on iShuttles in the City in weeks leading up to event. • Promoted a “Business Day” to local businesses in which businesses could sign up for tour led by Solar Decathlon docents. • Access Irvine Special Event Button running 9/28-10/18/15. • Access Irvine Push Notification on 10/15/15. • Facebook ad boost 10/13-10/18.

14 SOLAR ENERGY↗
DOE OSTI2017

Social Media Account Resolution and Verification

In this SAND report, we discuss various authorship attribution techniques for social media platforms to identify separate accounts controlled by one user. We look at three main categories of techniques: stylometric, semantic, and temporal, and we test several algorithms in each category. We then combine these techniques to increase predictive power, and perform cross-platform account verification on Twitter and Stack Exchange datasets. Additionally, we briefly examine graphical techniques, and analyze the predictive power of unsupervised approaches. Finally, we apply our techniques to unbalanced data sets. Our experiments reveal that normalized compression distance shows great promise in analyzing social media.

97 MATHEMATICS AND COMPUTING↗