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1. Pathways from Engineering Programs to Labor Unions

   https://doi.org/10.18260/1-2--47831  

   Valle, Joey; Stepback, Lazlo; Parkinson, Polly; Groves, Fawn; Minichiello, Angela; Ohland, Matthew (June 2024, ASEE Conferences)

   According to the United States Bureau of Labor Statistics, union density amongst engineering workers within the US hovers around 7%. Despite hundreds of thousands of US engineers participating in the labor movement, engineering education on labor unions has been virtually non-existent within US higher education engineering programs. US higher education engineering programs are critical junctures in the making of engineers that have long histories of ensnarement by corporate industries with vested interests in undermining organized labor. This stark and significant absence of labor education coupled with decades-long denunciations that many engineering professional societies have made to discourage participation of engineers in building labor unions and the labor movement interrupt engineers’ capacity to collectively leverage our power for safer, healthier, and more just workplaces and worlds. An imperative task in the (re)development of the US engineering workforce is to build and strengthen union density amongst engineers by expanding unionization pathways. This paper offers a preliminary report back on a broader engineering workforce development project to nurture relationships between an unorganized (i.e. non-union) engineering research center and organized labor. Herein, we uplift stories from union members describing their pathways from higher education engineering programs to labor unions. Group interview conversations illuminating these stories offer broader contextualization for the sparseness and rarity of the paths from engineering programs to labor unions. Dialogue from group interviews further pointed toward opportunities to expand unionization pathways for engineering workers. 

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2. On the Packing Dimension of Unions and Extensions of k-planes

   https://doi.org/10.1007/s12220-026-02441-w  

   Fiedler, Jacob B (May 2026, The Journal of Geometric Analysis)

   We study the packing dimension of unions of subsets of k-planes in ℝn using tools from algorithmic information theory, obtaining an analog of a result of Héra and a mild generalization of a recent result of Fraser. Along the way, we introduce a notion of effective dimension on the Grassmannian and affine Grassmannian, and we establish several useful algorithmic and geometric tools in this setting. Additionally, we consider how the packing dimension of the union of certain subsets of k-planes changes when the subsets are extended to the entire k-plane. Finally, we improve the above bounds for unions and extensions in the special case that k=n−1. 

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3. Unsupervised Manifold Linearizing and Clustering

   Ding, Tianjiao; Tong, Shengbang; Chan, Ryan; Dai, Xili; Ma, Yi; Haeffele, Benjamin (October 2023, International Conference on Computer Vision)

   We consider the problem of simultaneously clustering and learning a linear representation of data lying close to a union of low-dimensional manifolds, a fundamental task in machine learning and computer vision. When the manifolds are assumed to be linear subspaces, this reduces to the classical problem of subspace clustering, which has been studied extensively over the past two decades. Unfortunately, many real-world datasets such as natural images can not be well approximated by linear subspaces. On the other hand, numerous works have attempted to learn an appropriate transformation of the data, such that data is mapped from a union of general non-linear manifolds to a union of linear subspaces (with points from the same manifold being mapped to the same subspace). However, many existing works have limitations such as assuming knowledge of the membership of samples to clusters, requiring high sampling density, or being shown theoretically to learn trivial representations. In this paper, we propose to optimize the Maximal Coding Rate Reduction metric with respect to both the data representation and a novel doubly stochastic cluster membership, inspired by state-of-the-art subspace clustering results. We give a parameterization of such a representation and membership, allowing efficient mini-batching and one-shot initialization. Experiments on CIFAR-10, -20, -100, and TinyImageNet-200 datasets show that the proposed method is much more accurate and scalable than state-of-the-art deep clustering methods, and further learns a latent linear representation of the data. 

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4. Smart Moves: Making Sense of the Math in Environmental Data

   Merson, Martha; Gonzalez-Carrillo, Selene; Contini-Field, Ethan; Small, Meredith (July 2016, Science education & civic engagement)

   Environmental organizers and their constituents, local community group members concerned about environmental health, operate in a context with rich and varied opportunities for learning about and applying mathematics to communicating environmental data. Prior to Statistics for Action, project partners—organizers at environmental non-profits—spent little time with group members analyzing data. Organizations did not have a method or protocol for considering the most effective way to frame findings for neighbors and decision makers. During the Statistics for Action Project, STEM educators and environmental organizers collaborated to use the context of environmental organizing as a platform for science and math learning. This article describes Smart Moves and Memorable Messages, two approaches that advanced goals for both math learning and organizing 

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5. Observational approaches to chimpanzee behavior in an African sanctuary: Implications for research, welfare, and capacity‐building

   https://doi.org/10.1002/ajp.23534  

   Rosati, Alexandra G.; Sabbi, Kris H.; Bryer, Margaret A.; Barnes, Paige; Rukundo, Joshua; Mukungu, Titus; Sekulya, Phillip; Ampeire, Innocent; Aligumisiriza, Hillary; Kyama, Stanley; et al (September 2023, American Journal of Primatology)

   Abstract Research in African ape sanctuaries has emerged as an important context for our understanding of comparative cognition and behavior. While much of this work has focused on experimental studies of cognition, these animals semi‐free‐range in forest habitats and therefore can also provide important information about the behavior of primates in socioecologically‐relevant naturalistic contexts. In this “New Approaches” article, we describe a project where we implemented a synthetic program of observational data collection at Ngamba Island Chimpanzee Sanctuary in Uganda, directly modeled after long‐term data collection protocols at the Kibale Chimpanzee Project in Uganda, a wild chimpanzee field site. The foundation for this project was a strong partnership between sanctuary staff, field site staff, and external researchers. We describe how we developed a data‐collection protocol through discussion and collaboration among these groups, and trained sanctuary caregivers to collect novel observational data using these protocols. We use these data as a case study to examine: (1) how behavioral observations in sanctuaries can inform primate welfare and care practices, such as by understanding aggression within the group; (2) how matched observational protocols across sites can inform our understanding of primate behavior across different contexts, including sex differences in social relationships; and (3) how more robust collaborations between foreign researchers and local partners can support capacity‐building in primate range countries, along with mentoring and training students more broadly. 

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