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Latinx undergraduate student enrollment has dramatically increased over the past 20 years. However, this growing representation of Latinx students has not come with a parallel increase in the number of Latinx higher education administrators. This dearth in Latinx administrators is especially alarming because students who are able to build mentoring relationships with faculty and staff of similar backgrounds are more likely to persist and complete their college degrees. In particular, considering that Latinas are one of the least educated groups in the nation despite their growing college-going patterns, the paucity of Latina higher education administrators, who can potentially serve as their role models and femmentors, is problematic. Using a Chicana feminist epistemology and thematic analysis, this study examines how multiple social identities, such as race and gender, influence the experiences of eight early career Latina administrators, and how these early career experiences consequently impact their professional trajectories in higher education. Our findings indicate that early career Latina professionals feel pressured to tone down their race/ethnicity; are impacted by their intersecting identities; and navigate the field of higher education. Implications for research and practice are discussed.

This paper describes the system used by the LIPN team in the task 10, Multilingual Semantic Textual Similarity, at SemEval 2014, in both the English and Spanish sub-tasks. The system uses a support vector regression model, combining different text similarity measures as features. With respect to our 2013 participation, we included a new feature to take into account the geographical context and a new semantic distance based on the Bhattacharyya distance calculated on cooccurrence distributions derived from the Spanish Google Books n-grams dataset.

Traditionally, a few activation functions have been considered in neural networks, including bounded functions such as threshold, sigmoidal and hyperbolic-tangent, as well as unbounded ReLU, GELU, and Soft-plus, among other functions for deep learning, but the search for new activation functions still being an open research area. In this paper, wavelets are reconsidered as activation functions in neural networks and the performance of Gaussian family wavelets (first, second and third derivatives) are studied together with other functions available in Keras-Tensorflow. Experimental results show how the combination of these activation functions can improve the performance and supports the idea of extending the list of activation functions to wavelets which can be available in high performance platforms.