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  1. Social distancing can reduce the infection rates in respiratory pandemics such as COVID-19. Traffic intersections are particularly suitable for monitoring and evaluation of social distancing behavior in metropolises. Hence, in this paper, we propose and evaluate a real-time privacy-preserving social distancing analysis system (B-SDA), which uses bird’s-eye view video recordings of pedestrians who cross traffic intersections. We devise algorithms for video pre-processing, object detection, and tracking which are rooted in the known computer-vision and deep learning techniques, but modified to address the problem of detecting very small objects/pedestrians captured by a highly elevated camera. We propose a method for incorporatingmore »pedestrian grouping for detection of social distancing violations, which achieves 0.92 F1 score. B-SDA is used to compare pedestrian behavior in pre-pandemic and during-pandemic videos in uptown Manhattan, showing that the social distancing violation rate of 15.6% during the pandemic is notably lower than 31.4% prepandemic baseline.« less
    Free, publicly-accessible full text available May 1, 2023
  2. Social media platforms are playing increasingly critical roles in disaster response and rescue operations. During emergencies, users can post rescue requests along with their addresses on social media, while volunteers can search for those messages and send help. However, efficiently leveraging social media in rescue operations remains challenging because of the lack of tools to identify rescue request messages on social media automatically and rapidly. Analyzing social media data, such as Twitter data, relies heavily on Natural Language Processing (NLP) algorithms to extract information from texts. The introduction of bidirectional transformers models, such as the Bidirectional Encoder Representations from Transformersmore »(BERT) model, has significantly outperformed previous NLP models in numerous text analysis tasks, providing new opportunities to precisely understand and classify social media data for diverse applications. This study developed and compared ten VictimFinder models for identifying rescue request tweets, three based on milestone NLP algorithms and seven BERT-based. A total of 3191 manually labeled disaster-related tweets posted during 2017 Hurricane Harvey were used as the training and testing datasets. We evaluated the performance of each model by classification accuracy, computation cost, and model stability. Experiment results show that all BERT-based models have significantly increased the accuracy of categorizing rescue-related tweets. The best model for identifying rescue request tweets is a customized BERT-based model with a Convolutional Neural Network (CNN) classifier. Its F1-score is 0.919, which outperforms the baseline model by 10.6%. The developed models can promote social media use for rescue operations in future disaster events.« less
    Free, publicly-accessible full text available July 1, 2023
  3. Researchers have typically identified and characterized teachers’ knowledge bases ( e.g. , pedagogical content knowledge and subject matter knowledge) in an effort to improve enacted instructional strategies. As shown by the Refined Consensus Model (RCM), understanding teacher learning, beliefs, and practices is predicated on the interconnections of such knowledge bases. However, lesson planning (defined as the transformation of subject matter knowledge to enacted pedagogical content knowledge) remains underexplored despite its central position in the RCM. We aim to address this gap by developing a conceptual framework known as Pedagogical Chemistry Sensemaking (PedChemSense). PedChemSense theoretically expands upon the RCM that generatesmore »actionable guidelines to support chemsistry teachers’ lesson planning. We incorporate the constructs of sensemaking, Johnstone's triangle, and the models for perspective to provide a lesson-planning mechanism that is specific, accessible, and practical, respectively. Lesson examples from our own professional development contexts, the VisChem Institute, demonstrate the efficacy of PedChemSense. By leveraging teachers’ sensemaking of the limitations and utility of models, PedChemSense facilitates teachers’ designing for opportunities to advance their students’ chemistry conceptual understanding. Implications and recommendations for chemistry instruction and research at secondary and undergraduate levels are discussed.« less
    Free, publicly-accessible full text available April 1, 2023
  4. Free, publicly-accessible full text available March 6, 2023
  5. Transformers provide a class of expressive architectures that are extremely effective for sequence modeling. However, the key limitation of transformers is their quadratic memory and time complexity O(L2) with respect to the sequence length in attention layers, which restricts application in extremely long sequences. Most existing approaches leverage sparsity or low-rank assumptions in the attention matrix to reduce cost, but sacrifice expressiveness. Instead, we propose Combiner, which provides full attention capability in each attention head while maintaining low computation and memory complexity. The key idea is to treat the self-attention mechanism as a conditional expectation over embeddings at each location,more »and approximate the conditional distribution with a structured factorization. Each location can attend to all other locations, either via direct attention, or through indirect attention to abstractions, which are again conditional expectations of embeddings from corresponding local regions. We show that most sparse attention patterns used in existing sparse transformers are able to inspire the design of such factorization for full attention, resulting in the same sub-quadratic cost (O(L log(L)) or O(L√L)). Combiner is a drop-in replacement for attention layers in existing transformers and can be easily implemented in common frameworks. An experimental evaluation on both autoregressive and bidirectional sequence tasks demonstrates the effectiveness of this approach, yielding state-of-the-art results on several image and text modeling tasks.« less
    Free, publicly-accessible full text available December 1, 2022
  6. Abstract We present the first theoretical and experimental evidence of time-resolved dynamic x-ray magnetic linear dichroism (XMLD) measurements of GHz magnetic precessions driven by ferromagnetic resonance in both metallic and insulating thin films. Our findings show a dynamic XMLD in both ferromagnetic Ni 80 Fe 20 and ferrimagnetic Ni 0.65 Zn 0.35 Al 0.8 Fe 1.2 O 4 for different measurement geometries and linear polarizations. A detailed analysis of the observed signals reveals the importance of separating different harmonic components in the dynamic signal in order to identify the XMLD response without the influence of competing contributions. In particular, RFmore »magnetic resonance elicits a large dynamic XMLD response at the fundamental frequency under experimental geometries with oblique x-ray polarization. The geometric range and experimental sensitivity can be improved by isolating the 2 ω Fourier component of the dynamic response. These results illustrate the potential of dynamic XMLD and represent a milestone accomplishment toward the study of GHz spin dynamics in systems beyond ferromagnetic order.« less
    Free, publicly-accessible full text available January 1, 2023
  7. This work presents a unique approach to the design, fabrication, and characterization of paper-based origami robotic systems consisting of stackable pneumatic actuators. These paper-based actuators (PBAs) use materials with high elastic modulus-to-mass ratios, accordion-like structures, and direct coupling with pneumatic pressure for extension and bending. The study contributes to the scientific and engineering understanding of foldable components under applied pneumatic pressure by constructing stretchable and flexible structures with intrinsically nonstretchable materials. Experiments showed that a PBA possesses a power-to-mass ratio greater than 80 W/kg, which is more than four times that of human muscle. This work also illustrates the stackabilitymore »and functionality of PBAs by two prototypes: a parallel manipulator and a legged locomotor. The manipulator consisting of an array of PBAs can bend in a specific direction with the corresponding actuator inflated. In addition, the stacked actuators in the manipulator can rotate in opposite directions to compensate for relative rotation at the ends of each actuator to work in parallel and manipulate the platform. The locomotor rotates the PBAs to apply and release contact between the feet and the ground. Furthermore, a numerical model developed in this work predicts the mechanical performance of these inflatable actuators as a function of dimensional specifications and folding patterns. Overall, we use stacked origami actuators to implement functionalities of manipulation, gripping, and locomotion as conventional robotic systems. Future origami robots made of paper-like materials may be suitable for single use in contaminated or unstructured environments or low-cost educational materials.« less
  8. Engineers design for an inherently uncertain world. In the early stages of design processes, they commonly account for such uncertainty either by manually choosing a specific worstcase and multiplying uncertain parameters with safety factors, or by using Monte Carlo simulations to estimate the probabilistic boundaries in which their design is feasible. The safety factors of this first practice are determined by industry and organizational standards, providing an inexpressive account of uncertainty; the second practice is time intensive, requiring the development of separate testing infrastructure. In theory, robust optimization provides an alternative, allowing set based conceptualizations of uncertainty to be representedmore »during model development as optimizable design parameters. How these theoretical benefits translate to design practice has not previously been studied. In this work, we analyzed present use of geometric programs as design models in the aerospace industry to determine the current state-of-the-art, then conducted a human-subjects experiment to investigate how various mathematical representations of uncertainty affect design space exploration. We found that robust optimization led to far more efficient explorations of possible designs with only small differences in experimental participant’s understandings of their model. Specifically, the Pareto frontier of a typical participant using robust optimization left less performance “on the table” across various levels of risk than the very best frontiers of participants using industry-standard practices.« less