What do surrogate safety metrics measure? Understanding driving safety as a continuum.

Road safety is an important public health issue; technology, policy, and educational interventions to prevent crashes are of significant interest to researchers and policymakers. In particular, there is significant ongoing research to proactively evaluate the safety of new technologies, including autonomous vehicles, before enough crashes occur to directly measure their impact. We analyze the distributional form of five diverse datasets that approximate motor vehicle safety incident severity, including one dataset of hard braking events that characterizes the severity of non-crash incidents. Our empirical analysis finds that all five datasets closely fit a lognormal distribution (Kolmogorov-Smirnov distance < 0.013; significance of loglikelihood ratio with other distributions < 0.000029). We demonstrate a linkage between two well-known but largely qualitative safety frameworks and the severity distributions observed in the data. We create a formal model of the Swiss Cheese Model (SCM) and show through analysis and simulations that this formalization leads to a lognormal distribution of the severity continuum of safety-critical incidents. This finding is not only consistent with the empirical data we examine, but represents a quantitative restatement of Heinrich's Triangle, another heretofore largely qualitative framework that hypothesizes that safety events of increasing severity have decreasing frequency. Our results support the use of more frequent, low-severity events to rapidly assess safety in the absence of less frequent, high-severity events for any system consistent with our formalization of SCM. This includes any complex system designed for robustness to single-point failures, including autonomous vehicles.

Plant Cysteine Oxidase Oxygen-Sensing Function Is Conserved in Early Land Plants and Algae.

All aerobic organisms require O2 for survival. When their O2 is limited (hypoxia), a response is required to reduce demand and/or improve supply. A hypoxic response mechanism has been identified in flowering plants: the stability of certain proteins with N-terminal cysteine residues is regulated in an O2-dependent manner by the Cys/Arg branch of the N-degron pathway. These include the Group VII ethylene response factors (ERF-VIIs), which can initiate adaptive responses to hypoxia. Oxidation of their N-terminal cysteine residues is catalyzed by plant cysteine oxidases (PCOs), destabilizing these proteins in normoxia; PCO inactivity in hypoxia results in their stabilization. Biochemically, the PCOs are sensitive to O2 availability and can therefore act as plant O2 sensors. It is not known whether oxygen-sensing mechanisms exist in other phyla from the plant kingdom. Known PCO targets are only conserved in flowering plants, however PCO-like sequences appear to be conserved in all plant species. We sought to determine whether PCO-like enzymes from the liverwort, Marchantia polymorpha (MpPCO), and the freshwater algae, Klebsormidium nitens (KnPCO), have a similar function as PCO enzymes from Arabidopsis thaliana. We report that MpPCO and KnPCO show O2-sensitive N-terminal cysteine dioxygenase activity toward known AtPCO ERF-VII substrates as well as a putative endogenous substrate, MpERF-like, which was identified by homology to the Arabidopsis ERF-VIIs transcription factors. This work confirms functional and O2-dependent PCOs from Bryophyta and Charophyta, indicating the potential for PCO-mediated O2-sensing pathways in these organisms and suggesting PCO O2-sensing function could be important throughout the plant kingdom.

Measuring racial and ethnic disparities in traffic enforcement with large-scale telematics data.

Past studies have found that racial and ethnic minorities are more likely than White drivers to be pulled over by the police for alleged traffic infractions, including a combination of speeding and equipment violations. It has been difficult, though, to measure the extent to which these disparities stem from discriminatory enforcement rather than from differences in offense rates. Here, in the context of speeding enforcement, we address this challenge by leveraging a novel source of telematics data, which include second-by-second driving speed for hundreds of thousands of individuals in 10 major cities across the United States. We find that time spent speeding is approximately uncorrelated with neighborhood demographics, yet, in several cities, officers focused speeding enforcement in small, demographically nonrepresentative areas. In some cities, speeding enforcement was concentrated in predominantly non-White neighborhoods, while, in others, enforcement was concentrated in predominately White neighborhoods. Averaging across the 10 cities we examined, and adjusting for observed speeding behavior, we find that speeding enforcement was moderately more concentrated in non-White neighborhoods. Our results show that current enforcement practices can lead to inequities across race and ethnicity.

Decibel: The Relational Dataset Branching System.

As scientific endeavors and data analysis become increasingly collaborative, there is a need for data management systems that natively support the versioning or branching of datasets to enable concurrent analysis, cleaning, integration, manipulation, or curation of data across teams of individuals. Common practice for sharing and collaborating on datasets involves creating or storing multiple copies of the dataset, one for each stage of analysis, with no provenance information tracking the relationships between these datasets. This results not only in wasted storage, but also makes it challenging to track and integrate modifications made by different users to the same dataset. In this paper, we introduce the Relational Dataset Branching System, Decibel, a new relational storage system with built-in version control designed to address these shortcomings. We present our initial design for Decibel and provide a thorough evaluation of three versioned storage engine designs that focus on efficient query processing with minimal storage overhead. We also develop an exhaustive benchmark to enable the rigorous testing of these and future versioned storage engine designs.

SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics.

Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose SeeDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SeeDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most "useful" or "interesting". The two major obstacles in recommending interesting visualizations are (a) scale: evaluating a large number of candidate visualizations while responding within interactive time scales, and (b) utility: identifying an appropriate metric for assessing interestingness of visualizations. For the former, SeeDB introduces pruning optimizations to quickly identify high-utility visualizations and sharing optimizations to maximize sharing of computation across visualizations. For the latter, as a first step, we adopt a deviation-based metric for visualization utility, while indicating how we may be able to generalize it to other factors influencing utility. We implement SeeDB as a middleware layer that can run on top of any DBMS. Our experiments show that our framework can identify interesting visualizations with high accuracy. Our optimizations lead to multiple orders of magnitude speedup on relational row and column stores and provide recommendations at interactive time scales. Finally, we demonstrate via a user study the effectiveness of our deviation-based utility metric and the value of recommendations in supporting visual analytics.