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  1. Spatial ability is a well-known predictor of success in science, technology, engineering, and mathematics (STEM) fields. The purpose of this study was to investigate and understand the spatial strategies that were used by blind and low-vision (BLV) individuals as they solved problems on the tactile mental cutting test (TMCT), an instrument that was designed to measure the spatial ability of BLV audiences. The TMCT is an accessible adaptation of the older, 1938 version of the mental cutting test (MCT) that has been used extensively in spatial ability research. Additionally, this paper seeks to compare these strategies with existing strategies that have been investigated with sighted populations. The BLV community is underrepresented in engineering and in spatial ability research. By understanding how BLV students understand and solve spatial problems and concepts, educators can develop and enhance educational content that is relevant to this population. By incorporating perspectives from the BLV community and making STEM curricula accessible to this population, more BLV individuals may be encouraged to pursue STEM or engineering career pathways. 
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    Free, publicly-accessible full text available July 6, 2024
  2. Free, publicly-accessible full text available June 2, 2024
  3. We explore the connection between outlier-robust high-dimensional statistics and non-convex optimization in the presence of sparsity constraints, with a focus on the fundamental tasks of robust sparse mean estimation and robust sparse PCA. We develop novel and simple optimization formulations for these problems such that any approximate stationary point of the associated optimization problem yields a near-optimal solution for the underlying robust estimation task. As a corollary, we obtain that any first-order method that efficiently converges to stationarity yields an efficient algorithm for these tasks. The obtained algorithms are simple, practical, and succeed under broader distributional assumptions compared to prior work. 
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  4. We explore the connection between outlier-robust high-dimensional statistics and non-convex optimization in the presence of sparsity constraints, with a focus on the fundamental tasks of robust sparse mean estimation and robust sparse PCA. We develop novel and simple optimization formulations for these problems such that any approximate stationary point of the associated optimization problem yields a near-optimal solution for the underlying robust estimation task. As a corollary, we obtain that any first-order method that efficiently converges to stationarity yields an efficient algorithm for these tasks.1 The obtained algorithms are simple, practical, and succeed under broader distributional assumptions compared to prior work. 
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