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The purpose of this paper is to detail the initial validation of a scale to assess engineering students’ attitudes toward the value of diversity in engineering and their intentions to enact inclusive behaviors. In study 1, we administered the scale four times. We subjected the first administration to exploratory factor analysis (EFA), and the remaining three administrations to both confirmatory factor analysis (CFA) and tests of longitudinal measurement invariance (LMI). All tests indicated strong evidence for the internal structure of the factor structure of the survey. The four factors were: engineers should value diversity to (a) fulfill a greater purpose and (b) serve customers better; and engineers should (c) challenge discriminatory behavior and (d) promote a healthy work environment. In study 2, we again assessed the structure of the data as described in study 1 and then used the scale to assess potential differences between undergraduate students who participated in activities designed to promote diversity, equity, and inclusion (DEI) (n=116) and those who did not (n=137). Students in the intervention classes demonstrated a small statistically significant increase in their intention to promote a healthy team environment in reference to the comparison classes. No differences were observed between the classes on the other factors. Future directions and implications are discussed. 

Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior. However, by ignoring signal priors, the sensitivity of these approaches is significantly reduced. We present a new strategy dubbed Quasi Anomalous Knowledge (QUAK), whereby we introduce alternative signal priors that capture some of the salient features of new physics signatures, allowing for the recovery of sensitivity even when the alternative signal is incorrect. This approach can be applied to a broad range of physics models and neural network architectures. In this paper, we apply QUAK to anomaly detection of new physics events at the CERN Large Hadron Collider utilizing variational autoencoders with normalizing flow.