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  1. A growing number of college applications has presented an annual challenge for college admissions in the United States. Admission offices have historically relied on standardized test scores to organize large applicant pools into viable subsets for review. However, this approach may be subject to bias in test scores and selection bias in test-taking with recent trends toward test-optional admission. We explore a machine learning-based approach to replace the role of standardized tests in subset generation while taking into account a wide range of factors extracted from student applications to support a more holistic review. We evaluate the approach on data from an undergraduate admission office at a selective US institution (13,248 applications). We find that a prediction model trained on past admission data outperforms an SAT-based heuristic and matches the demographic composition of the last admitted class. We discuss the risks and opportunities for how such a learned model could be leveraged to support human decision-making in college admissions. 
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    Atmospheric aerosols are suspended particulate matter of varying composition, size, and mixing state. Challenges remain in understanding the impact of aerosols on the climate, atmosphere, and human health. The effect of aerosols depends on their physicochemical properties, such as their hygroscopicity, phase state, and surface tension. These properties are dynamic with respect to the highly variable relative humidity and temperature of the atmosphere. Thus, experimental approaches that permit the measurement of these dynamic properties are required. Such measurements also need to be performed on individual, submicrometer-, and supermicrometer-sized aerosol particles, as individual atmospheric particles from the same source can exhibit great variability in their form and function. In this context, this review focuses on the recent emergence of atomic force microscopy as an experimental tool in physical, analytical, and atmospheric chemistry that enables such measurements. Remaining challenges are noted and suggestions for future studies are offered. 
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    People interacting with voice assistants are often frustrated by voice assistants' frequent errors and inability to respond to backchannel cues. We introduce an open-source video dataset of 21 participants' interactions with a voice assistant, and explore the possibility of using this dataset to enable automatic error recognition to inform self-repair. The dataset includes clipped and labeled videos of participants' faces during free-form interactions with the voice assistant from the smart speaker's perspective. To validate our dataset, we emulated a machine learning classifier by asking crowdsourced workers to recognize voice assistant errors from watching soundless video clips of participants' reactions. We found trends suggesting it is possible to determine the voice assistant's performance from a participant's facial reaction alone. This work posits elicited datasets of interactive responses as a key step towards improving error recognition for repair for voice assistants in a wide variety of applications. 
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