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Free, publicly-accessible full text available February 1, 2023
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Free, publicly-accessible full text available December 1, 2022
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Over the past two decades, educators have used computer-supported collaborative learning (CSCL) to integrate technology with pedagogy to improve student engagement and learning outcomes. Researchers have also explored the diverse affordances of CSCL, its contributions to engineering instruction, and its effectiveness in K-12 STEM education. However, the question of how students use CSCL resources in undergraduate engineering classrooms remains largely unexplored. This study examines the affordances of a CSCL environment utilized in a sophomore dynamics course with particular attention given to the undergraduate engineering students’ use of various CSCL resources. The resources include a course lecturebook, instructor office hours, amore »Free, publicly-accessible full text available July 26, 2022
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Free, publicly-accessible full text available December 16, 2022
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Abstract Superconductivity is among the most fascinating and well-studied quantum states of matter. Despite over 100 years of research, a detailed understanding of how features of the normal-state electronic structure determine superconducting properties has remained elusive. For instance, the ability to deterministically enhance the superconducting transition temperature by design, rather than by serendipity, has been a long sought-after goal in condensed matter physics and materials science, but achieving this objective may require new tools, techniques and approaches. Here, we report the transmutation of a normal metal into a superconductor through the application of epitaxial strain. We demonstrate that synthesizing RuOmore »Free, publicly-accessible full text available December 1, 2022
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Bartoli, A ; Fusiello, A (Ed.)We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker. The baseline pre-trained short-term tracker is SuperDiMP which combines the bounding-box regressor of PrDiMP with the standard DiMP classifier. Our tracker RLT-DiMP improves SuperDiMP in the follow- ing three aspects: (1) Uncertainty reduction using random erasing: To make our model robust, we exploit an agreement from multiple im- ages after erasing random small rectangular areas as a certainty. And then, we correct the tracking state of our model accordingly. (2) Ran- dom search with spatio-temporal constraints: we propose a robust ran- dommore »
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Aliasing refers to the phenomenon that high frequency signals degenerate into com- pletely different ones after sampling. It arises as a problem in the context of deep learning as downsampling layers are widely adopted in deep architectures to reduce parameters and computation. The standard solution is to apply a low-pass filter (e.g., Gaussian blur) before downsampling [37]. However, it can be suboptimal to apply the same filter across the entire content, as the frequency of feature maps can vary across both spatial locations and feature channels. To tackle this, we propose an adaptive content-aware low-pass filtering layer, which predicts separatemore »