75%: grading scale interpretations from students and teachers at Sun Prairie High School

Includes bibliographical references

Measuring sustained attention after traumatic brain injury: Differences in key findings from the sustained attention to response task (SART)

Clinical reports after traumatic brain injury (TBI) suggest frequent difficulties with sustained attention, but their objective measurement has proved difficult. In 1997, Robertson and colleagues reported on a new sustained attention assessment tool, the sustained attention to response task (SART). Individuals with TBI were reported to produce more errors of commission on the SART than control participants, and both groups showed a relationship between SART errors and everyday lapses of attention as measured by the cognitive failures questionnaire (CFQ). Although few direct replications of these findings have been reported, the SART has been used widely as a measure of sustained attention in TBI, in normal controls, and in various other clinical samples. As part of a program of research on attention in TBI, we administered the SART and the CFQ to a sample of 34 survivors of moderate to severe TBI and to 35 control participants. CFQ scores reported by significant others showed clear group differences in everyday lapses of attention. Despite this, group differences in SART errors of commission were small and non-significant, and the correlations between SART errors and CFQ scores were small within both groups. Further analyses excluding participants with invalid score profiles, or restricting the analysis to the first performance of the SART failed to alter the results. These findings suggest that more research is needed to establish the validity of the SART as a measure of sustained attention after TBI, and to determine under what circumstances the original findings hold

Use of an Observational Coding System with Families of Adolescents: Psychometric Properties among Pediatric and Healthy Populations

Objective: To examine reliability and validity data for the Family Interaction Macro-coding System (FIMS) with adolescents with spina bifida (SB), adolescents with type 1 diabetes mellitus (T1DM), and healthy adolescents and their families.Methods: Sixty-eight families of children with SB, 58 families of adolescents with T1DM, and 68 families in a healthy comparison group completed family interaction tasks and self-report questionnaires. Trained coders rated family interactions using the FIMS.Results: Acceptable interrater and scale reliabilities were obtained for FIMS items and subscales. Observed FIMS parental acceptance, parental behavioral control, parental psychological control, family cohesion, and family conflict scores demonstrated convergent validity with conceptually similar self-report measures.Conclusions: Preliminary evidence supports the use of the FIMS with families of youths with SB and T1DM and healthy youths. Future research on overall family functioning may be enhanced by use of the FIMS

Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems

Path prediction is an essential task for many real-world Cyber-Physical Systems (CPS) applications, from autonomous driving and traffic monitoring/management to pedestrian/worker safety. These real-world CPS applications need a robust, lightweight path prediction that can provide a universal network architecture for multiple subjects (e.g., pedestrians and vehicles) from different perspectives. However, most existing algorithms are tailor-made for a unique subject with a specific camera perspective and scenario. This article presents Pishgu, a universal lightweight network architecture, as a robust and holistic solution for path prediction. Pishgu's architecture can adapt to multiple path prediction domains with different subjects (vehicles, pedestrians), perspectives (bird's-eye, high-angle), and scenes (sidewalk, highway). Our proposed architecture captures the inter-dependencies within the subjects in each frame by taking advantage of Graph Isomorphism Networks and the attention module. We separately train and evaluate the efficacy of our architecture on three different CPS domains across multiple perspectives (vehicle bird's-eye view, pedestrian bird's-eye view, and human high-angle view). Pishgu outperforms state-of-the-art solutions in the vehicle bird's-eye view domain by 42% and 61% and pedestrian high-angle view domain by 23% and 22% in terms of ADE and FDE, respectively. Additionally, we analyze the domain-specific details for various datasets to understand their effect on path prediction and model interpretation. Finally, we report the latency and throughput for all three domains on multiple embedded platforms showcasing the robustness and adaptability of Pishgu for real-world integration into CPS applications

CHAD: Charlotte Anomaly Dataset

In recent years, we have seen a significant interest in data-driven deep learning approaches for video anomaly detection, where an algorithm must determine if specific frames of a video contain abnormal behaviors. However, video anomaly detection is particularly context-specific, and the availability of representative datasets heavily limits real-world accuracy. Additionally, the metrics currently reported by most state-of-the-art methods often do not reflect how well the model will perform in real-world scenarios. In this article, we present the Charlotte Anomaly Dataset (CHAD). CHAD is a high-resolution, multi-camera anomaly dataset in a commercial parking lot setting. In addition to frame-level anomaly labels, CHAD is the first anomaly dataset to include bounding box, identity, and pose annotations for each actor. This is especially beneficial for skeleton-based anomaly detection, which is useful for its lower computational demand in real-world settings. CHAD is also the first anomaly dataset to contain multiple views of the same scene. With four camera views and over 1.15 million frames, CHAD is the largest fully annotated anomaly detection dataset including person annotations, collected from continuous video streams from stationary cameras for smart video surveillance applications. To demonstrate the efficacy of CHAD for training and evaluation, we benchmark two state-of-the-art skeleton-based anomaly detection algorithms on CHAD and provide comprehensive analysis, including both quantitative results and qualitative examination. The dataset is available at https://github.com/TeCSAR-UNCC/CHAD

MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos

Convolutional neural network inference on video input is computationally expensive and requires high memory bandwidth. Recently, DeltaCNN managed to reduce the cost by only processing pixels with significant updates over the previous frame. However, DeltaCNN relies on static camera input. Moving cameras add new challenges in how to fuse newly unveiled image regions with already processed regions efficiently to minimize the update rate - without increasing memory overhead and without knowing the camera extrinsics of future frames. In this work, we propose MotionDeltaCNN, a sparse CNN inference framework that supports moving cameras. We introduce spherical buffers and padded convolutions to enable seamless fusion of newly unveiled regions and previously processed regions -- without increasing memory footprint. Our evaluation shows that we outperform DeltaCNN by up to 90% for moving camera videos

Star Formation History of a Young Super-Star Cluster in NGC 4038/39: Direct Detection of Low Mass Pre-Main Sequence Stars

We present an analysis of the near-infrared spectrum of a young massive star cluster in the overlap region of the interacting galaxies NGC 4038/39 using population synthesis models. Our goal is to model the cluster population as well as provide rough constraints on its initial mass function (IMF). The cluster shows signs of youth such as thermal radio emission and strong hydrogen emission lines in the near-infrared. Late-type absorption lines are also present which are indicative of late-type stars in the cluster. The strength and ratio of these absorption lines cannot be reproduced through either late-type pre-main sequence (PMS) stars or red supergiants alone. Thus we interpret the spectrum as a superposition of two star clusters of different ages, which is feasible since the 1" spectrum encompasses a physical region of ~90 pc and radii of super-star clusters are generally measured to be a few parsecs. One cluster is young (<= 3 Myr) and is responsible for part of the late-type absorption features, which are due to PMS stars in the cluster, and the hydrogen emission lines. The second cluster is older (6 Myr - 18 Myr) and is needed to reproduce the overall depth of the late-type absorption features in the spectrum. Both are required to accurately reproduce the near-infrared spectrum of the object. Thus we have directly detected PMS objects in an unresolved super-star cluster for the first time using a combination of population synthesis models and pre-main sequence tracks. This analysis serves as a testbed of our technique to constrain the low-mass IMF in young super-star clusters as well as an exploration of the star formation history of young UC HII regions.Comment: 26 pages, 5 figures, accepted for publication in the Astrophysical Journa

Photonic Band Tuning in 2D Photonic Crystals by Atomic Layer Deposition

Atomic layer deposition (ALD) has become a powerful tool for the fabrication of high quality 3-dimentional photonic crystals (PCs) from both inorganic (opal) and organic (holographically patterned polymer) templates [1,2]. With ALD, highly conformal films can be grown with a precision of 0.05 nm, which, when combined with the availability of a wide range of low temperature film growth protocols, enables a high degree of control over material and structural properties to precisely tune optical properties [3]. Two-dimensional photonic crystals have been developed extensively for applications in optical interconnects, beam steering, and sensor devices; and are predominantly fabricated by electron-beam lithography. The optical properties of 2D photonic crystal slab waveguides are determined by the precision of the lithography process, with limited post fabrication tunability

Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety

Recently, Smart Video Surveillance (SVS) systems have been receiving more attention among scholars and developers as a substitute for the current passive surveillance systems. These systems are used to make the policing and monitoring systems more efficient and improve public safety. However, the nature of these systems in monitoring the public's daily activities brings different ethical challenges. There are different approaches for addressing privacy issues in implementing the SVS. In this paper, we are focusing on the role of design considering ethical and privacy challenges in SVS. Reviewing four policy protection regulations that generate an overview of best practices for privacy protection, we argue that ethical and privacy concerns could be addressed through four lenses: algorithm, system, model, and data. As an case study, we describe our proposed system and illustrate how our system can create a baseline for designing a privacy perseverance system to deliver safety to society. We used several Artificial Intelligence algorithms, such as object detection, single and multi camera re-identification, action recognition, and anomaly detection, to provide a basic functional system. We also use cloud-native services to implement a smartphone application in order to deliver the outputs to the end users