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  1. Does interdisciplinary collaboration make a difference when it comes to communicating engineering concepts to community audiences? This research focuses on the effect of communication strategies on community attitudes toward engineering research. Two cohorts of four academic researchers each, representing eight different disciplinary backgrounds (aviation planning, cancer research, math education, musicology, chemical/biomolecular engineering, material science, soil science, and theater) developed research communication outputs for the public by creating: 1) an individual video presenting their research through the lens of their discipline alone; and 2) a convergent video where they collaboratively discussed their research with others in their cohort around a common theme, integrating all of their disciplinary lenses. Using a panel of respondents (n = 2,938) procured through Qualtrics, and purposefully recruited to create a diverse sample in age and racial/ethnic background, the research team randomly assigned respondents to watch one of three video treatments: one individual video, multiple individual videos, or a convergent video. Then, respondents answered a series of questions about their interest and knowledge of several STEM topics, both before and after watching the video(s). This retrospective pre/post questionnaire technique helps to alleviate response-shift bias present in self-assessed changes in learning attitudes. Our findings show that collaborative presentation videos increased self-reported audience interest in engineering, and perceptions of disciplinary relatedness more than the non-collaborative, individual presentations made by the same researchers. These results suggest a beneficial role for collaborative communication strategies to foster interest in engineering among public audiences, even among people without a background in STEM. Further, collaborative communication led to an increased sense of relatedness among different disciplines, which may be useful for effective public research communication about interdisciplinary engineering projects. 
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  3. Embedding undirected graphs in a Euclidean space has many computational benefits. FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs. However, Euclidean distances are inherently symmetric and, thus, Euclidean embeddings cannot be used for directed graphs. In this paper, we present FastMap-D, an efficient generalization of FastMap to directed graphs. FastMap-D embeds vertices using a potential field to capture the asymmetry between the pairwise distances in directed graphs. FastMap-D learns a potential function to define the potential field using a machine learning module. In experiments on various kinds of directed graphs, we demonstrate the advantage of FastMap-D over other approaches. Errata: This version of the paper corrects a programming mistake, resulting in even better experimental results than those reported in the original paper. 
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