A Model for Equity in Arts Education for Youth in Greater Portland, Maine

Time and time again, by providing an outlet that allows innate human creativity to roam free, participation in arts activities such as dance, music, theatre, and visual arts is proven to have significant positive impacts on participants, regardless of age, experience, or background. Even though the arts have been acknowledged as critical for mental, physical, and emotional health, especially for children and adolescents, research has found that providing arts education in an equitable and culturally appropriate way is fraught with barriers. In addition, certain areas of the arts have seen a downward trend in educational priority and availability. My project supports the research of a growing number of scholars concerned with equity in arts education and participation. For my study, I explore how two Maine-based non-profits, located in the greater Portland metropolitan area, are working to increase access to arts education opportunities for students of all ages from preschool to secondary education. To this end, interviews were conducted with gatekeepers from the Boys and Girls Club of Southern Maine and the Children’s Museum and Theatre of Maine, focusing on the specific actions these organizations are currently taking to provide equitable access to arts education as well as identifying what needs to be done in the future to further their initiatives. The results of my study reveal a working model composed of five critical factors needed to create effective and equitable arts education: inclusiveness, relevancy, funding, student autonomy, and community engagement. Gatekeepers of both organizations emphasize there is much more work ahead in order to reach true equity, but agree that these five factors, when combined, provide an effective framework for organizations concerned with equity in arts education for our youth. To further develop this basic model, additional research is needed on alternative organizational approaches to arts education and equity

SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization

Computer vision is experiencing an AI renaissance, in which machine learning models are expediting important breakthroughs in academic research and commercial applications. Effectively training these models, however, is not trivial due in part to hyperparameters: user-configured values that control a model's ability to learn from data. Existing hyperparameter optimization methods are highly parallel but make no effort to balance the search across heterogeneous hardware or to prioritize searching high-impact spaces. In this paper, we introduce a framework for massively Scalable Hardware-Aware Distributed Hyperparameter Optimization (SHADHO). Our framework calculates the relative complexity of each search space and monitors performance on the learning task over all trials. These metrics are then used as heuristics to assign hyperparameters to distributed workers based on their hardware. We first demonstrate that our framework achieves double the throughput of a standard distributed hyperparameter optimization framework by optimizing SVM for MNIST using 150 distributed workers. We then conduct model search with SHADHO over the course of one week using 74 GPUs across two compute clusters to optimize U-Net for a cell segmentation task, discovering 515 models that achieve a lower validation loss than standard U-Net.Comment: 10 pages, 6 figure

Extracting expressive performance information from recorded music

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1995.Includes bibliographical references (leaves 55-56).by Eric David Scheirer.M.S

The Synthesis and Reactions of 8-Carboxy Isatoic Anhydride

This 34 page thesis examines the preparation and characterization of 8-carboxy isatoic anhydride and the study of its reactions, leading to the synthesis of compounds of new composition