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Studies have shown that the graduation rate for underrepresented minorities (URM) students enrolled in engineering doctorates is significantly lower than their peers. In response, we created the “Rising Doctoral Institute (RDI)”. This project aims to address issues that URM students encounter when transitioning into a Ph.D. in engineering and their decision to persist in the program. To suggest institutional policies that increase the likelihood of URM students to persist in their doctorate, we identify and analyze some factors in the academic system that reinforce or hinder the retention of URM students in doctoral education. Although the factors that influence persistence in URM students have been largely studied as direct causes of attrition or retention, there is a need for a system perspective that takes into account the complexity and dynamic interaction that exists between those factors. The academic system is a complex system that, by nature, is policy resistant. This means that a positive variation of a factor can incur unintended consequences that could lead to a negative variation in other factors and ultimately hinder the positive outcomes of that policy. In this work-in-progress article, we analyze the dynamics of the factors in the academic system that reinforce or hinder the retention of URM graduate students in engineering. The purpose is to build some of the causal loops that involve those factors, to improve the understanding of how the complex system works, and prevent unintended consequences of institutional policies. We used Causal Loop Diagrams (CLD) to model the feedback loops of the system based on initial hypotheses of causal relationships between the factors. We followed a process that started with establishing hypotheses from a previous literature review, then using a different set of articles we identified the factors related to the hypotheses and the causal links between them. Next, we did axial coding to group the concepts into smaller categories and established the causal relations between categories. With these categories and relations, we created the CLDs for each hypothesis. For the CLDs that have connections missing to close the loop, we went to find additional literature to close them. Finally, we analyzed the implications of each CLD. In this article, we analyze and describe three major CLDs found in literature. The first one was built around the factor of having a positive relationship with the supervisor. The second centered on the student’s experience. The third focused on factors that relate to university initiatives 

Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. Theoretical approaches to the problem have hit some limits in the past decades and analytical solutions are known for only a few simple settings. Computational approaches to the problem through the use of LPs have their own set of limitations. Building on the success of deep learning, a new approach was recently proposed by Duetting et al. (2019) in which the auction is modeled by a feed-forward neural network and the design problem is framed as a learning problem. The neural architectures used in that work are general purpose and do not take advantage of any of the symmetries the problem could present, such as permutation equivariance. In this work, we consider auction design problems that have permutation-equivariant symmetry and construct a neural architecture that is capable of perfectly recovering the permutation- equivariant optimal mechanism, which we show is not possible with the previous architecture. We demonstrate that permutation-equivariant architectures are not only capable of recovering previous results, they also have better generalization properties. 

A bstract Jet production in lead-lead (PbPb) and proton-proton (pp) collisions at a nucleon-nucleon center-of-mass energy of 5.02 TeV is studied with the CMS detector at the LHC, using PbPb and pp data samples corresponding to integrated luminosities of 404 μ b − 1 and 27.4 pb − 1 , respectively. Jets with different areas are reconstructed using the anti- k T algorithm by varying the distance parameter R . The measurements are performed using jets with transverse momenta ( p T ) greater than 200 GeV and in a pseudorapidity range of |η| < 2. To reveal the medium modification of the jet spectra in PbPb collisions, the properly normalized ratio of spectra from PbPb and pp data is used to extract jet nuclear modification factors as functions of the PbPb collision centrality, p T and, for the first time, as a function of R up to 1.0. For the most central collisions, a strong suppression is observed for high- p T jets reconstructed with all distance parameters, implying that a significant amount of jet energy is scattered to large angles. The dependence of jet suppression on R is expected to be sensitive to both the jet energy loss mechanism and the medium response, and so the data are compared to several modern event generators and analytic calculations. The models considered do not fully reproduce the data. 

A bstract We present the first study of charged-hadron production associated with jets originating from b quarks in proton-proton collisions at a center-of-mass energy of 5.02 TeV. The data sample used in this study was collected with the CMS detector at the CERN LHC and corresponds to an integrated luminosity of 27.4 pb − 1 . To characterize the jet substructure, the differential jet shapes, defined as the normalized transverse momentum distribution of charged hadrons as a function of angular distance from the jet axis, are measured for b jets. In addition to the jet shapes, the per-jet yields of charged particles associated with b jets are also quantified, again as a function of the angular distance with respect to the jet axis. Extracted jet shape and particle yield distributions for b jets are compared with results for inclusive jets, as well as with the predictions from the pythia and herwig++ event generators.