Intelligence Advanced Research Projects Activity Research Articles

Audio recording with textiles – no longer science fiction!

Under a contract from the Intelligence Advanced Research Projects Activity (IARPA), an expert team led by SRI (Menlo Park, California) research engineers Marcus Bagnell and Nicole Heidel, specialists in fibre technologies and collaborators at North Carolina State University and International Fabric Machines (IFM), a textile maker, will seek to incorporate a piezoelectric material into a fabric that acts like a microphone – a textile that can record audio.

Citrullination of a phage-displayed human peptidome library reveals the fine specificities of rheumatoid arthritis-associated autoantibodies.

BackgroundPost-translational modifications (PTMs) on proteins can be targeted by antibodies associated with autoimmunity. Despite a growing appreciation for their intrinsic role in disease, there is a lack of highly multiplexed serological assays to characterize the fine specificities of PTM-directed autoantibodies.MethodsIn this study, we used the programmable phage display technology, Phage ImmunoPrecipitation Sequencing (PhIP-Seq), to profile rheumatoid arthritis (RA) associated anti-citrullinated protein antibody (ACPA) reactivities.FindingsUsing both unmodified and peptidylarginine deiminase (PAD)-modified phage display libraries consisting of ~250,000 overlapping 90 amino acid peptide tiles spanning the human proteome, PTM PhIP-Seq robustly identified antibodies to citrulline-dependent epitopes.InterpretationPTM PhIP-Seq was used to quantify key differences among RA patients, including PAD isoform specific ACPA profiles, and thus represents a powerful tool for proteome-scale antibody-binding analyses.FundingThis research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the US Government. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. This study was made possible by a National Institute of General Medical Sciences (NIGMS) grant R01 GM136724 (HBL). MFK was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) grant T32AR048522. ED was supported by the Rheumatology Research Foundation.

Improved Transmission Line Parameter Calculation Through TCAD Process Modeling for Superconductor Integrated Circuit Interconnects

The Florida Object-Oriented Device, Process and Reliability Simulator (FLOOXS) technology computer-aided design process modeling tools developed at the University of Florida have been adapted under the Intelligence Advanced Research Projects Activity SuperTools program to support the MIT Lincoln Laboratory SFQ5ee fabrication process. We use FLOOXS to build meshed models of passive transmission lines from superconductor integrated circuit layouts. We have previously developed a numerical solver that extracts transmission line parameters from the meshed model. In this work, we convert a layout slice to FLOOXS inputs, generate two-dimensional meshes of cross-sectional geometries from simulated process steps, and then extract the transmission line parameters from the meshes. Results are shown compared against the results for simplified transmission lines that do not utilize process modeling. The results confirm that process modeling alters the extracted parameter values and suggest that process modeling will become more important as cell density of superconducting electronics increases. We conclude with a discussion on the necessity of process modeling for high-quality parameter extraction.

A novel approach to predicting exceptional growth in research.

The prediction of exceptional or surprising growth in research is an issue with deep roots and few practical solutions. In this study, we develop and validate a novel approach to forecasting growth in highly specific research communities. Each research community is represented by a cluster of papers. Multiple indicators were tested, and a composite indicator was created that predicts which research communities will experience exceptional growth over the next three years. The accuracy of this predictor was tested using hundreds of thousands of community-level forecasts and was found to exceed the performance benchmarks established in Intelligence Advanced Research Projects Activity's (IARPA) Foresight Using Scientific Exposition (FUSE) program in six of nine major fields in science. Furthermore, 10 of 11 disciplines within the Computing Technologies field met the benchmarks. Specific detailed forecast examples are given and evaluated, and a critical evaluation of the forecasting approach is also provided.

Associations Between Peaks of Foodborne Infections and Food Recalls

Abstract Objectives Food delivery and supply chains represent complex dynamic systems susceptible to pathogen introduction. However, seasonal infections are rarely compared with the timing of food recalls. Gaps between peak infections and recalls illustrate inefficient food inspection and monitoring. We use seasonality features (peak timing and amplitude) to examine associations between peaks of pathogen infectiousness and food recalls. Methods Monthly counts of 3 foodborne infections (Salmonella, Listeria, and Shiga toxin-producing E.coli) were abstracted from the CDC's public database, FoodNet Fast, for 10 representative states (CA, CO, CT, GA, MD, MN, NM, NY, OR, TN) from 1996–2018. We applied Negative Binomial harmonic regression models with the δ-method to derive peak timing and amplitude estimates (with confidence intervals). Food recalls were extracted using the United States Department of Agriculture's (USDA) Food Safety and Inspection Service (FSIS) Recall Case Archive. We extracted recall dates and durations for each infection in each state. We compared lagged associations between infection peaks and recall dates using Spearman correlations. Results Seasonal peaks of Salmonella and Shiga Toxin-producing E.Coli (STEC) are tightly clustered (7.79 [7.71, 7.87], and 7.80 [7.65, 7.95], respectively) while Listeria falls later in August (8.35 [8.05, 8.65]). Peak timing estimates were positively synchronized between New York and Connecticut for Listeria (ρ = 0.780, P = 0.001), and between Georgia and Tennessee for STEC (ρ = 0.666, P = 0.009) indicating possible regional hotspots. From 1996–2018, inspection recalls tended to lag behind peaks of confirmed infections for all infections by 1–2 months, with most recalls occurring during August and September annually. Conclusions Measurements of infection seasonality coupled with food recall information shows lagged associations between population infectiousness and supply chain reporting. This suggests the need for improved food inspection scheduling and monitoring to minimize fiscal and food waste losses. Funding Sources The Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via 2017–17,072,100,002.

Advanced Convolutional Neural Network-Based Hybrid Acoustic Models for Low-Resource Speech Recognition

Deep neural networks (DNNs) have shown a great achievement in acoustic modeling for speech recognition task. Of these networks, convolutional neural network (CNN) is an effective network for representing the local properties of the speech formants. However, CNN is not suitable for modeling the long-term context dependencies between speech signal frames. Recently, the recurrent neural networks (RNNs) have shown great abilities for modeling long-term context dependencies. However, the performance of RNNs is not good for low-resource speech recognition tasks, and is even worse than the conventional feed-forward neural networks. Moreover, these networks often overfit severely on the training corpus in the low-resource speech recognition tasks. This paper presents the results of our contributions to combine CNN and conventional RNN with gate, highway, and residual networks to reduce the above problems. The optimal neural network structures and training strategies for the proposed neural network models are explored. Experiments were conducted on the Amharic and Chaha datasets, as well as on the limited language packages (10-h) of the benchmark datasets released under the Intelligence Advanced Research Projects Activity (IARPA) Babel Program. The proposed neural network models achieve 0.1–42.79% relative performance improvements over their corresponding feed-forward DNN, CNN, bidirectional RNN (BRNN), or bidirectional gated recurrent unit (BGRU) baselines across six language collections. These approaches are promising candidates for developing better performance acoustic models for low-resource speech recognition tasks.

The Analysis of Knowledge Information Research and Development Activities for the Fourth Industrial Revolution: Focusing on the U.S. Intelligence Advanced Research Projects Activity(IARPA)

The Analysis of Knowledge Information Research and Development Activities for the Fourth Industrial Revolution: Focusing on the U.S. Intelligence Advanced Research Projects Activity(IARPA)

DNA Script gets grant for data storage

A team including DNA Script, a Paris-based synthetic nucleic acid manufacturer, has been awarded a contract worth up to $23 million from the US Intelligence Advanced Research Projects Activity for DNA-based data storage. With partners Illumina, Robert Nicol of Broad Institute of MIT and Harvard, and Harvard University’s Donhee Ham, the group will develop sequence-controlled polymers that can store exabytes of data. The goal is reduced size, power, and cost requirements compared with conventional storage technologies.

Accelerometer-Measured Sustained MVPA Is Related To Higher Decision-Making Competence Among Young Adults

More competent decision makers report greater success in avoiding negative decision outcomes irrespective of general cognitive ability. While physically active young adults show more optimal executive functions, the relationship between daily moderate-to-vigorous physical activity (MVPA) patterns and decision-making competence (DMC) remains under-examined. PURPOSE: In this study, we assessed the relationship between accelerometer-measured sporadic and sustained MVPA to DMC in young adults. METHODS: We analyzed pre-intervention data from 220 participants (115 (52%) females, Mage=24.3 ±5.4 yrs, BMI=24.4 ± 4.0 kg/m2) from the INSIGHT randomized controlled trial. MVPA was measured over 7 days with a hip-worn wGT3X-BT accelerometer. Valid wear time was defined as ≥4 days, ≥10 hrs/d. Daily (min/d), bouts of sporadic (<10 consecutive min) and sustained MVPA (≥10 consecutive min; frequency and min/d) were estimated using NHANES cut points. DMC was measured with the Adult-Decision Making Competence (A-DMC) battery and expressed as individual subtest scores and an A-DMC index (z-score). The relationships between MVPA and A-DMC variables were assessed with Spearman’s rho controlling for wear time, age, sex, education, intelligence, fat free VO2max, BMI and sedentary time (ST; <100 counts/min). RESULTS:After controlling for daily MVPA, frequency and time spent in sustained MVPA bouts were positively related to the ability to recognize social norms (ρs=[0.15; 0.16], Ps≤0.04) and ignore unrecoverable costs when considering future decision outcomes (ρs=[0.14; 0.15], Ps≤0.04). In contrast, neither sporadic nor daily MVPA were related to A-DMC subtests (Ps≤0.08). CONCLUSION: The quality of decision making varied primarily as a function of sustained MVPA. Young adults who engaged in more sustained MVPA were able to use their experience more effectively and make more rational choices to optimize decision outcomes. Our data reveal a novel relationship between MVPA patterns and a distinct set of higher order cognitive skills which are relevant to real-world decisions. Funding: Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA); Contract 2014-13121700004

Improving Insider Threat Detection Through Multi-Modelling/Data Fusion

Insider threats within organizations can take a variety of forms including fraud, theft of classified or proprietary data, and workplace violence. Insiders can be overt individuals who commit deliberate acts against their organization, or inadvertent insiders who provide access to organizational IT systems. The damage from insider threats was estimated at an annualized average of $4.3M per company across a variety of industries in a 2016 study, which does not include the damage to an organization’s reputation, loss of business, and decrease in company value. The Scientific advances to Continuous Insider Threat Evaluation (SCITE) program sponsored by the Intelligence Advanced Research Projects Activity (IARPA) was created to improve insider threat detection. In the first phase of this effort, three competing teams were provided aggregated individual performance data and asked to answer system performance questions on a range of complex challenge problems. The purpose was to be able to detect a subset of individuals from within the overall population of an organization that displayed a behavior or belonged to a group. Teams were scored on three metrics: mean squared error, calibrated certainty interval, and interval score. The team led by Innovative Decisions, Inc. (IDI) used a multiple model approach with data fusion to model these problems. This approach proved far superior to the methods used by the two other teams across all three metrics. The results of this research are applicable to any area where the objective is to identify a very small subset of a much larger group using incomplete, noisy data. Specific areas benefitting education and research include airline passenger screening, vetting of immigration or visa applications, and security clearance reviews.

An indicator of technical emergence

Developing useful intelligence on scientific and technological emergence challenges those who would manage R&D portfolios, assess research programs, or manage innovation. Recently, the U.S. Intelligence Advanced Research Projects Activity Foresight and Understanding from Scientific Exposition Program has explored means to detect emergence via text analyses. We have been involved in positing conceptual bases for emergence, framing candidate indicators, and devising implementations. We now present a software script to generate a family of Emergence Indicators for a topic of interest. This paper offers some background, then discusses the development of this script through iterative rounds of testing, and then offers example findings. Results point to promising and actionable intelligence for R&D decision-makers.

Developing a Dual-Track Modeling Approach for Increased Understanding of Sensors and their Forecasting Capabilities

Event Abstract Back to Event Developing a Dual-Track Modeling Approach for Increased Understanding of Sensors and their Forecasting Capabilities Raquel C. Galvan-Garza1*, Peter B. Bryan1*, Amanda E. Kraft1, Alison M. Perez1, Matthew J. Pava1, William D. Casebeer1 and Matthias D. Ziegler1 1 Lockheed Martin (United States), United States Forecasting human behavior from sensor-derived biometrics poses a modeling challenge encountered across statistical domains: balancing interpretability against model power. On one hand, polynomial models derived from literature review and a priori assumptions of feature interactions are transparent (i.e., explained variance can be meaningfully interpreted in the original input space) but fail to capture higher-order interactions that were not explicitly predicted. On the other, black-box methods from machine learning are equipped to successfully capture this variance but sacrifice explainability. Here, we evaluate a dual-track approach for modeling of human state. The first track (theory-driven) consists of white-box modeling methodologies while the second track (data-driven) consists of a black-box model, namely a recurrent neural network. The data sets we use in this presentation focus on physiological, interpersonal, and environmental factors; however, we are beginning to use the same methodology with multi-sensor studies that include neuroimaging techniques such as fNIRS and EEG. We will discuss details on how we 1) create theory-driven models for estimating variables such as subjective stress, anxiety, affect, personality, and job performance, 2) create data-driven models using complex neural networks in parallel, and 3) adapt the theory-driven model to include new connections discovered by the data-driven model using a systematic automated approach. Using methods including local sensitivity analysis, we attempt to extract variance-capturing, low-dimensional feature combinations from the data-driven track to augment theory-driven models. We conclude with a discussion of the respective advantages and disadvantages of each track, a summary of challenges intrinsic to this method, and an evaluation of the successes and failures of our complementary modeling approach. The research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via 2017-17042800004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Keywords: biometrics, forecasting human behavior, Model selection, theory-driven modeling, Data-driven Modeling, machine learning applied to neuroscience Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018. Presentation Type: Oral Presentation Topic: Neuroergonomics Citation: Galvan-Garza RC, Bryan PB, Kraft AE, Perez AM, Pava MJ, Casebeer WD and Ziegler MD (2019). Developing a Dual-Track Modeling Approach for Increased Understanding of Sensors and their Forecasting Capabilities. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00029 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 30 Mar 2018; Published Online: 27 Sep 2019. * Correspondence: PhD. Raquel C Galvan-Garza, Lockheed Martin (United States), Bethesda, United States, raquel.c.galvan@lmco.com Mr. Peter B Bryan, Lockheed Martin (United States), Bethesda, United States, peterbbryan@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Raquel C Galvan-Garza Peter B Bryan Amanda E Kraft Alison M Perez Matthew J Pava William D Casebeer Matthias D Ziegler Google Raquel C Galvan-Garza Peter B Bryan Amanda E Kraft Alison M Perez Matthew J Pava William D Casebeer Matthias D Ziegler Google Scholar Raquel C Galvan-Garza Peter B Bryan Amanda E Kraft Alison M Perez Matthew J Pava William D Casebeer Matthias D Ziegler PubMed Raquel C Galvan-Garza Peter B Bryan Amanda E Kraft Alison M Perez Matthew J Pava William D Casebeer Matthias D Ziegler Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Joint Learning of Distance Metric and Query Model for Posteriorgram-Based Keyword Search

In this paper, we propose a novel approach to keyword search (KWS) in low-resource languages, which provides an alternative method for retrieving the terms of interest, especially for the out of vocabulary (OOV) ones. Our system incorporates the techniques of query-by-example retrieval tasks into KWS and conducts the search by means of the subsequence dynamic time warping (sDTW) algorithm. For this, text queries are modeled as sequences of feature vectors and used as templates in the search. A Siamese neural network-based model is trained to learn a frame-level distance metric to be used in sDTW and the proper query model frame representations for this learned distance. Experiments conducted on Intelligence Advanced Research Projects Activity Babel Program's Turkish, Pashto, and Zulu datasets demonstrate the effectiveness of our approach. In each of the languages, the proposed system outperforms the large vocabulary continuous speech recognition (LVCSR) based baseline for OOV terms. Furthermore, the fusion of the proposed system with the baseline system provides an average relative actual term weighted value (ATWV) improvement of 13.9% on all terms and, more significantly, the fusion yields an average relative ATWV improvement of 154.5% on OOV terms. We show that this new method can be used as an alternative to conventional LVCSR-based KWS systems, or in combination with them, to achieve the goal of closing the gap between OOV and in-vocabulary retrieval performances.

Robust forecast aggregation: Fourier L2E regression

AbstractThe Good Judgment Team led by psychologists P. Tetlock and B. Mellers of the University of Pennsylvania was the most successful of five research projects sponsored through 2015 by the Intelligence Advanced Research Projects Activity to develop improved group forecast aggregation algorithms. Each team had at least 10 algorithms under continuous development and evaluation over the 4‐year project. The mean Brier score was used to rank the algorithms on approximately 130 questions concerning categorical geopolitical events each year. An algorithm would return aggregate probabilities for each question based on the probabilities provided per question by thousands of individuals, who had been recruited by the Good Judgment Team. This paper summarizes the theorized basis and implementation of one of the two most accurate algorithms at the conclusion of the Good Judgment Project. The algorithm incorporated a number of pre‐ and postprocessing steps, and relied upon a minimum distance robust regression method called L2E. The algorithm was just edged out by a variation of logistic regression, which has been described elsewhere. Work since the official conclusion of the project has led to an even smaller gap.

Recent progress in deep end-to-end models for spoken language processing

End-to-end models (or sequence-to-sequence models) based on deep neural networks have recently become popular within the machine learning community. These techniques are also increasingly used in automatic speech recognition as an alternative to the state-of-the-art, hybrid HMM-DNN (hidden Markov model, deep neural network) system. The end-to-end systems contain a purely neural architecture that eliminates the need of any time alignment between the input acoustic feature vector sequence and output phone sequence. In this paper, we present progress within the IBM Watson Multimodal Group on end-to-end models for spoken language processing. We present our work on two types of end-to-end models applied to speech-to-text and keyword search tasks, namely, 1) recurrent neural networks (RNNs) based on connectionist temporal classification loss, and 2) attention-based encoder–decoder RNNs. We present results on several languages (such as Pashto, Mongolian, Javanese, Amharic, Guarani, Dholuo, Igbo, and Georgian) from the Intelligence Advanced Research Projects Activity funded Babel Program. We also present a detailed analysis of some salient characteristics of these models compared with the state-of-the-art HMM-DNN hybrid systems, and also discuss future challenges in using such models for spoken language processing.

IARPA Director Jason Matheny advances tech tools for US espionage

ABSTRACTThe Intelligence Advanced Research Projects Activity, or IARPA, develops research and technology to serve the US intelligence community. In this interview, IARPA Director Jason Matheny explains how the 10-year-old agency does its work. With a modus operandi similar to the one used by the Defense Advanced Research Projects Agency, IARPA comes up with specific problems to solve, then signs up university labs, private companies, and other institutions to carry out the research. Best known for its research programs in quantum computing and machine learning, IARPA has recently increased the amount it invests in biotechnology research. Biotechnology, cybertechnology, and artificial intelligence are the three areas of defense technology that concern Matheny most as potential threats. In this wide-ranging interview with Bulletin contributing editor Elisabeth Eaves, he also talks about the latest research on whistle-blowers, how to get the world’s best scientists working on a problem, how to predict a cyber attack, and why IARPA seems surprisingly transparent compared to intelligence agencies of yore.

Teaching Decision Making With Serious Games

Game-based training may have different characteristics than other forms of instruction. The independent validation of the Intelligence Advanced Research Projects Activity (IARPA) Sirius program evaluated game-based cognitive bias training across several games with a common set of control groups. Control groups included a professionally produced video that taught the same cognitive biases and an unrelated video that did not teach any biases. Knowledge was tested immediately after training and after a delay. This article presents the results from the two phases of the Sirius program. Game-based training showed advantages in teaching bias mitigation skills (procedural knowledge) but had no advantage over video instruction in teaching people to answer explicit questions about biases (declarative knowledge). Overall, training effects persisted over time, and games performed as well as and in some cases better than the video-based instruction for knowledge retention. Our results suggest that serious games can be an effective training tool, particularly for teaching procedural knowledge.

Lessons Learned About Serious Game Design and Development

The development of a serious game combines the skills of numerous disciplines, from subject matter experts on the topic being taught; to story developers, game designers, and software developers; to instructional designers, educational assessment scientists, and others. This section provides commentary on the Intelligence Advanced Research Projects Activity’s unique game development program, Sirius, where multiple games with the same training goal were independently developed and tested by different teams. We compare the experience of two of these teams not only in game design but also in how skills of various disciplines were woven together to produce and validate their games. Lessons learned are reviewed to provide guidelines and takeaway points to assist game development practitioners in their future efforts to create effective serious games.

The Sexual Dimorphic Relationship Between Dietary Fiber Intake and Visceral Adipose Tissue

Accumulation of visceral adipose tissue (VAT) has been mechanistically implicated in compromised metabolic health and increased risk for cardiovascular disease. However, sex is an important determinant of fat tissue compartmentalization or distribution. Substantial differences in VAT between males and females emerge early in life and are sustained throughout adulthood. These differences are thought to contribute to the disproportionate prevalence and incidence rates of cardiovascular diseases between males and females. Thus, it is plausible that relationships between dietary behaviors and VAT are influenced by sex as well. Specifically, meeting dietary guidelines for fiber intake remains a prominent feature of public health policy towards obesity prevention. Although the recommendations for fiber intake are greater among males (38g/d) compared to females (25g/d), the extent to which meeting the sex‐specific dietary fiber recommendations protects against VAT remains unclear. This study aimed to elucidate the differential association between dietary fiber intake and VAT among male and female adults. Healthy adults (N=318; 155 females, aged 18–44 y) underwent Dual Energy X‐ray Absorptiometry (DXA) to estimate VAT. 3‐day food records were used to assess daily fiber intake. The proportion of daily sex‐specific fiber recommendation met (%Fiber) was used as the primary dietary variable. Physical activity was recorded among a subsample of participants (n=269) and was assessed as steps/day using an accelerometer (Actigraph GT3X+) worn for at least 4 days (minimum 8 hours/day). As expected, VAT was significantly greater among males (P&lt;0.01). Total fiber intake was not different between males and females (P=0.62). However, only 6% of participants met their recommended fiber intake. Nevertheless, %Fiber intake was greater among females (P&lt;0.01) by 23%, indicating that males consumed significantly less fiber, relative to their daily recommended intake. Among males, VAT was inversely related to %Fiber (r=−0.18, P=0.01) intake. This relationship persisted even following adjustment of age and physical activity using partial correlations. However, no significant associations between fiber intake and VAT (r=0.06, P=0.23) were observed among females. In conclusion, these results suggest that fiber was independently and inversely related to visceral fat accumulation in a sexually dimorphic manner. These findings have important public health implications because they suggest that chronically lower fiber intake or failure to meet fiber recommendations may disproportionately compromise metabolic health among adult men. Future research is needed to determine the source of these differential associations and whether interventions targeted at meeting dietary fiber intake recommendations provide adequate protective effects against visceral fat accumulation.Support or Funding InformationThe research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via Contract 2014‐13121700004 to the University of Illinois at Urbana‐Champaign. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

Assimilative modeling of ionospheric dynamics for nowcasting of HF propagation channels in the presence of TIDs

AbstractThe ionospheric data assimilation algorithm called GPS Ionospheric Inversion (GPSII; pronounced “gypsy”) has been extended and employed to model the dynamic ionosphere, including medium‐scale traveling ionospheric disturbances (MS‐TIDs). MS‐TIDs are characterized by periods of 10–30 min. GPSII can assimilate many forms of ionospheric‐related data, including ionogram data and GPS L1/L2 beacon data. For this present effort, GPSII was extended to assimilate propagation time delay, integrated Doppler shift, and angle‐of‐arrival (AoA) measurements of HF transmissions from known reference points (KRPs). GPSII applies a regularization technique that constrains the solver to find the smoothest 3‐D ionosphere model that still reproduces the input data to within their respective errors of measurement. A companion paper documents the development of the assimilation capability for KRPs. In this paper we show test results of the model's performance in reproducing measured AoA variations in the presence of medium‐scale traveling ionospheric disturbances (MS‐TIDs) using near vertical incidence skywave data collected at White Sands Missile Range by the Intelligence Advanced Research Projects Activity HFGeo Program Government team. We find that using three KRPs within approximately 50 km of reference/check/nonassimilated transmitters, we can reproduce the measured AoAs of the nonassimilated transmitters to within 1.9° with 90% confidence even in the presence of highly dynamic MS‐TIDs.