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Teaching professional development (TPD) in collegiate mathematics has expanded over the last few decades. Providers of TPD, people who organize and facilitate professional learning about teaching, are at the center of this growth. Yet, little is known about who Providers are and what they do. To better understand the national landscape of Providers of TPD within university mathematics departments, this report shares data from a national survey where respondents were Providers. The focus here is on findings from survey questions asking about characteristics of Providers and the “providees” with whom they work, along with formats, topics, and activities used in TPD. Results suggest that Providers value active, learner-centered instructional methods promoted by research and policy. However, in the TPD itself, formats, topics, and activities commonly used by Providers may preach but not regularly practice activity-based methods. 

Trajectory prediction forecasts nearby agents’ moves based on their historical trajectories. Accurate trajectory prediction (or prediction in short) is crucial for autonomous vehicles (AVs). Existing attacks compromise the prediction model of a victim AV by directly manipulating the historical trajectory of an attacker AV, which has limited real-world applicability. This paper, for the first time, explores an indirect attack approach that induces prediction errors via attacks against the perception module of a victim AV. Although it has been shown that physically realizable attacks against LiDAR-based perception are possible by placing a few objects at strategic locations, it is still an open challenge to find an object location from the vast search space in order to launch effective attacks against prediction under varying victim AV velocities. Through analysis, we observe that a prediction model is prone to an attack focusing on a single point in the scene. Consequently, we propose a novel two-stage attack framework to realize the single-point attack. The first stage of predictionside attack efficiently identifies, guided by the distribution of detection results under object-based attacks against perception, the state perturbations for the prediction model that are effective and velocity-insensitive. In the second stage of location matching, we match the feasible object locations with the found state perturbations. Our evaluation using a public autonomous driving dataset shows that our attack causes a collision rate of up to 63% and various hazardous responses of the victim AV. The effectiveness of our attack is also demonstrated on a real testbed car 1. To the best of our knowledge, this study is the first security analysis spanning from LiDARbased perception to prediction in autonomous driving, leading to a realistic attack on prediction. To counteract the proposed attack, potential defenses are discussed. 

This paper presents a non-resonant vibration energy harvester (VEH) optimized for 0.5-1.0 Hz at 0.2g acceleration, typically associated with human motion in daily activities. Different amounts of water-based and oil-based ferrofluids as liquid bearings have been studied in an experimental setup with a precisely controllable spacing between top and bottom coil plates where the magnet array and ferrofluid bearings reside. The sub-miniature VEH (1.4cc and 3.3gram) steadily generates voltages between 0.5-1.0 Hz and is measured to produce an open-circuit voltage of Vrms = 19.5 - 31.9 mV (or 0.33-0.89 μW into a match load) from 0.2g sub-Hz applied acceleration. The highest figure of merit (FOM) of the VEH at 0.2g at 1.0 Hz is 15.5 μW/cc/g2. 

Communities and cities are becoming vulnerable due to climate change-induced disasters such as heat waves, flooding, landslides, and droughts. The severity and increased frequency of these extreme events have demanded a resilience plan in the major cities to combat extreme weather events, which requires a systematic community vulnerability assessment. However, the specific impact of extreme events such as high heat waves on community vulnerability has been difficult to measure due to the unpredictability of weather patterns and events. Further, compounding the effects of building and built environment characteristics, the social and behavioral characteristics of households can result in differing levels of vulnerability to extreme temperature events. Even though many studies have discussed social vulnerability based on community demographics, the compounding effect has not been fully explored. When it comes to thermal resilience against extreme weather events, socially vulnerable communities are more likely to be affected by extreme heat due to a lack of thermal-resilient houses. In this research, Kolmogorov-Smirnov (KS-2 test) test was used to extend the relationship between building features data and the social vulnerability index of the city of Philadelphia. The outcome of this research strengthens our understanding of how social vulnerability and building resilience are correlated, also in the future to build community prototypes that integrate building features and social vulnerability to simulate community response against extreme weather events.