Integration of Naturalistic Driving Characteristics into Crash Forecasting Models

While highway safety has steadily improved throughout the United States, highway crashes and the resulting losses continue to be a significant concern in Louisiana. Louisiana consistently lags behind the country in many key areas of highway safety. To improve the conditions of roads in Louisiana, the Louisiana Department of Transportation and Development (LADOTD) has begun to implement the Highway Safety Manual (HSM) to evaluate existing and expected safety conditions and how to allocate limited improvement funds. However, as the HSM was developed using aggregated national statistics, it is not always able to reflect the conditions present on specific Louisiana roadways. The goal of this research was to address the limitations of applying the HSM predictive method in Louisiana, by creating and testing an HSM crash modification factor (CMF) founded on naturalistic driving behavior. The intent of this new CMF was to identify abrupt braking and evasive maneuvers in specific freeway segments because these conditions have been demonstrated to be strong predictors of high crash potential. The CMF was applied to the HSM predictive method to more accurately and reliably forecast crashes on Louisiana freeways. This research was conducted on freeway segments in Baton Rouge and showed that naturalistic driving behavior correlated with the HSM predicted crash frequency and also demonstrated that use of the crash modification factor affects the predicted crash frequency

Next generation of Exascale-class systems: ExaNeSt project and the status of its interconnect and storage development

The ExaNeSt project started on December 2015 and is funded by EU H2020 research framework (call H2020-FETHPC-2014, n. 671553) to study the adoption of low-cost, Linux-based power-efficient 64-bit ARM processors clusters for Exascale-class systems. The ExaNeSt consortium pools partners with industrial and academic research expertise in storage, interconnects and applications that share a vision of an European Exascale-class supercomputer. The common goal is designing and implementing a physical rack prototype together with its cooling system, the non-volatile memory (NVM) architecture and a unified low-latency interconnect able to test different options for network and storage. Furthermore, the consortium goal is to provide real HPC applications to validate the system. In this paper we describe the unified data and storage network architecture, reporting on the status of development of different testbeds and highlighting preliminary benchmark results obtained through the execution of scientific, engineering and data analytics scalable application kernels

Air Force Institute of Technology Research Report 1999

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

MINIMUM TIME CONTROL OF PARALLELED BOOST CONVERTERS

Demand for electrification is booming in both, traditional and upcoming generations of technological advancements. One of the constituent blocks of these electrified systems is Power conversion. Power conversion systems are often constructed by paralleling multiple power converter blocks for high performance and reliability of overall system. An advanced control technique is developed with an aim to optimize system states of heterogeneous power converters within minimum time while maintaining feasible stress level on individual power converter blocks. Practical implementation of real-time controller and performance improvement strategies are addressed. Experimental results validating high performance control scheme, and sensitivity analysis of system states as measure of system robustness are also presented

Deep Learning for Face Anti-Spoofing: A Survey

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, traditional FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

Should I trust you? Investigating trustworthiness judgements of painful facial expressions

Past research indicates that patients' reports of pain are often met with skepticism and that observers tend to underestimate patients' pain. The mechanisms behind these biases are not yet fully understood. One relevant domain of inquiry is the interaction between the emotional valence of a stranger's expression and the onlooker's trustworthiness judgment. The emotion overgeneralization hypothesis posits that when facial cues of valence are clear, individuals displaying negative expressions (e.g., disgust) are perceived as less trustworthy than those showing positive facial expressions (e.g., happiness). Accordingly, we hypothesized that facial expressions of pain (like disgust) would be judged more untrustworthy than facial expressions of happiness. In two separate studies, we measured trustworthiness judgments of four different facial expressions (i.e., neutral, happiness, pain, and disgust), displayed by both computer-generated and real faces, via both explicit self-reported ratings (Study 1) and implicit motor trajectories in a trustworthiness categorization task (Study 2). Ratings and categorization findings partly support our hypotheses. Our results reveal for the first time that when judging strangers' facial expressions, both negative expressions were perceived as more untrustworthy than happy expressions. They also indicate that facial expressions of pain are perceived as untrustworthy as disgust expressions, at least for computer-generated faces. These findings are relevant to the clinical setting because they highlight how overgeneralization of emotional facial expressions may subtend an early perceptual bias exerted by the patient's emotional facial cues onto the clinician's cognitive appraisal process