A Framework for Robust Assimilation of Potentially Malign Third-Party Data, and its Statistical Meaning

This paper presents a model-based method for fusing data from multiple sensors with a hypothesis-test-based component for rejecting potentially faulty or otherwise malign data. Our framework is based on an extension of the classic particle filter algorithm for real-time state estimation of uncertain systems with nonlinear dynamics with partial and noisy observations. This extension, based on classical statistical theories, utilizes statistical tests against the system's observation model. We discuss the application of the two major statistical testing frameworks, Fisherian significance testing and Neyman-Pearsonian hypothesis testing, to the Monte Carlo and sensor fusion settings. The Monte Carlo Neyman-Pearson test we develop is useful when one has a reliable model of faulty data, while the Fisher one is applicable when one may not have a model of faults, which may occur when dealing with third-party data, like GNSS data of transportation system users. These statistical tests can be combined with a particle filter to obtain a Monte Carlo state estimation scheme that is robust to faulty or outlier data. We present a synthetic freeway traffic state estimation problem where the filters are able to reject simulated faulty GNSS measurements. The fault-model-free Fisher filter, while underperforming the Neyman-Pearson one when the latter has an accurate fault model, outperforms it when the assumed fault model is incorrect.Comment: IEEE Intelligent Transportation Systems Magazine, special issue on GNSS-based positionin

DEIMOS Observations of WISE-Selected, Optically Obscured AGNs

While there are numerous criteria for photometrically identifying active galactic nuclei (AGNs), searches in the optical and UV tend to exclude galaxies that are highly dust obscured. This is problematic for constraining models of AGN evolution and estimating the AGN contribution to the cosmic X-ray and IR backgrounds, as highly obscured objects tend to be underrepresented in large-scale surveys. To address this, we identify potentially obscured AGNs using mid-IR color colors from the Wide-field Infrared Survey Explorer (WISE) catalog. This paper presents the results of optical spectroscopy of obscured AGN candidates using Keck DEIMOS, and their physical properties derived from these spectra. We find that a $W1-W2>0.8$ color criterion effectively selects AGNs with a higher median level of $E(B-V)$ extinction compared to the AGNs found in the SDSS DR7 survey. This optical extinction can be measured using SED modeling or by using $r-W1$ as a measure of optical to IR flux. We find that specific, targeted observations are necessary to find the most highly optically obscured AGNs, and that additional far-IR photometry is necessary to further constrain the dust properties of these AGNs.Comment: 20 pages, 25 figures, accepted by MNRA

Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs

Deep neural networks can be powerful tools, but require careful application-specific design to ensure that the most informative relationships in the data are learnable. In this paper, we apply deep neural networks to the nonlinear spatiotemporal physics problem of vehicle traffic dynamics. We consider problems of estimating macroscopic quantities (e.g., the queue at an intersection) at a lane level. First-principles modeling at the lane scale has been a challenge due to complexities in modeling social behaviors like lane changes, and those behaviors' resultant macro-scale effects. Following domain knowledge that upstream/downstream lanes and neighboring lanes affect each others' traffic flows in distinct ways, we apply a form of neural attention that allows the neural network layers to aggregate information from different lanes in different manners. Using a microscopic traffic simulator as a testbed, we obtain results showing that an attentional neural network model can use information from nearby lanes to improve predictions, and, that explicitly encoding the lane-to-lane relationship types significantly improves performance. We also demonstrate the transfer of our learned neural network to a more complex road network, discuss how its performance degradation may be attributable to new traffic behaviors induced by increased topological complexity, and motivate learning dynamics models from many road network topologies.Comment: To appear at 2019 IEEE Conference on Intelligent Transportation System

Constructional Volcanic Edifices on Mercury: Candidates and Hypotheses of Formation

Mercury, a planet with a predominantly volcanic crust, has perplexingly few, if any, constructional volcanic edifices, despite their common occurrence on other solar system bodies with volcanic histories. Using image and topographical data from the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft, we describe two small (< 15 km‐diameter) prominences with shallow summit depressions associated with volcanically flooded impact features. We offer both volcanic and impact‐related interpretations for their formation, and then compare these landforms with volcanic features on Earth and the Moon. Though we cannot definitively conclude that these landforms are volcanic, the paucity of constructional volcanic edifices on Mercury is intriguing in itself. We suggest that this lack is because volcanic eruptions with sufficiently low eruption volumes, rates, and flow lengths, suitable for edifice construction, were highly spatiotemporally restricted during Mercury's geological history. We suggest that volcanic edifices may preferentially occur in association with late‐stage, post‐impact effusive volcanic deposits. The ESA/JAXA BepiColombo mission to Mercury will be able to investigate further our candidate volcanic edifices, search for other, as‐yet unrecognized edifices beneath the detection limits of MESSENGER data, and test our hypothesis that edifice construction is favored by late‐stage, low‐volume effusive eruptions

A Dynamic-System-Based Approach to Modeling Driver Movements Across General-Purpose/Managed Lane Interfaces

To help mitigate road congestion caused by the unrelenting growth of traffic demand, many transportation authorities have implemented managed lane policies, which restrict certain freeway lanes to certain types of vehicles. It was originally thought that managed lanes would improve the use of existing infrastructure through demand-management behaviors like carpooling, but implementations have often been characterized by unpredicted phenomena that are sometimes detrimental to system performance. The development of traffic models that can capture these sorts of behaviors is a key step for helping managed lanes deliver on their promised gains. Towards this goal, this paper presents an approach for solving for driver behavior of entering and exiting managed lanes at the macroscopic (i.e., fluid approximation of traffic) scale. Our method is inspired by recent work in extending a dynamic-system-based modeling framework from traffic behaviors on individual roads, to models at junctions, and can be considered a further extension of this dynamic-system paradigm to the route/lane choice problem. Unlike traditional route choice models that are often based on discrete-choice methods and often rely on computing and comparing drivers' estimated travel times from taking different routes, our method is agnostic to the particular choice of physical traffic model and is suited specifically towards making decisions at these interfaces using only local information. These features make it a natural drop-in component to extend existing dynamic traffic modeling methods.Comment: 2018 ASME Dynamic Systems and Control Conference (DSCC 2018

Associations of Perirenal Fat Thickness with Renal and Systemic Calcified Atherosclerosis.

BackgroundWe investigated associations between perirenal fat thickness and atherosclerotic calcification in six different vascular beds.MethodsUsing a community-based cohort (n=3,919), perirenal fat thickness was estimated from computed tomography scans. It was classified as Q1 (the lowest quartile) to Q4 (the highest quartile) in each sex. Calcification in the carotid arteries, coronary arteries, thoracic aorta, abdominal aorta, iliac arteries, and renal arteries was evaluated.ResultsPerirenal fat thickness was associated with older age (P&lt;0.01) and a higher prevalence of obesity, hypertension, and dyslipidemia (P&lt;0.01 for all). Perirenal fat thickness was independently associated with renal arterial calcification even after adjustment for age, sex, body mass index, hypertension, dyslipidemia, smoking history, and family history of heart diseases in first-degree relatives (odds ratio [OR] per quartile of perirenal fat thickness, 1.25; 95% confidence interval [CI], 1.09 to 1.44). Compared to Q1, the odds of renal arterial calcification in Q4 was about two times higher (OR, 2.05; 95% CI, 1.29 to 3.25). After adjustment for renal arterial calcification and atherosclerotic risk factors, the only other vascular bed where perirenal fat thickness showed a significant association with calcification was the abdominal aorta (OR, 1.11; 95% CI, 1.00 to 1.23; P=0.045).ConclusionPerirenal fat thickness was independently associated with vascular calcification in the renal artery and abdominal aorta