Race/Ethnicity and Arts Participation: Findings from the Survey of Public Participation in the Arts

This report analyzes data from the 1982, 1985, 1992, 2002, and 2008 Surveys of Public Participation in the Arts (SPPA). Analyses focus on differential arts participation by race/ethnicity and the effect of race/ethnicity on arts participation. Descriptive and inferential analyses explore trends in arts participation by race/ethnicity across the five rounds of SPPA data. The authors find that, generally, the numbers and proportions of all race/ethnic groups that participate in the arts through attendance at arts events and arts creation are declining over time. The proportion of arts audiences that is white is not declining, despite the fact that the proportion of the national population that is white is declining. Race/ethnic group, per se, is not a strong predictor of attendance at arts events, but it is a good predictor of arts creation activities. Whites and Asians have had arts learning experiences at a greater rate than have blacks and Hispanics. Appendices include: (1) Descriptive statistics, 1982-2008; (2) Participation rate in core arts domains, by race/ethnicity, 1992-2008; (3) Participation rate in core arts creation domain, by race/ethnicity, 1992-2008; (4) Race/ethnic composition of arts creators, by arts creation domain, 1992-2008; (5) Effects of race/ethnicity, educational attainment, and their interactions on specific arts participation (full results); (6) Effects of race/ethnicity, household income, and their interactions on specific arts participation (full results); (7) Effects of race/ethnicity on specific arts creation (full results); and (8) Analysis of logistic regression assumptions. (Contains 36 figures, 40 tables and 7 footnotes.

Multi-level Distance Regularization for Deep Metric Learning

We propose a novel distance-based regularization method for deep metric learning called Multi-level Distance Regularization (MDR). MDR explicitly disturbs a learning procedure by regularizing pairwise distances between embedding vectors into multiple levels that represents a degree of similarity between a pair. In the training stage, the model is trained with both MDR and an existing loss function of deep metric learning, simultaneously; the two losses interfere with the objective of each other, and it makes the learning process difficult. Moreover, MDR prevents some examples from being ignored or overly influenced in the learning process. These allow the parameters of the embedding network to be settle on a local optima with better generalization. Without bells and whistles, MDR with simple Triplet loss achieves the-state-of-the-art performance in various benchmark datasets: CUB-200-2011, Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval. We extensively perform ablation studies on its behaviors to show the effectiveness of MDR. By easily adopting our MDR, the previous approaches can be improved in performance and generalization ability.Comment: Accepted to AAAI 202

A STUDY ON PROCESS PARAMETERS AND MANUFACTURING SUSTAINABILITY OF ADDITIVELY MANUFACTURED LATTICE STRUCTURE UNDER THE UNIAXIAL COMPRESSION

Department of Mechanical EngineeringAdditive manufacturing (AM), also called 3D printing, is a new process that can improve the inefficiency of existing manufacturing processes and has been applied to various industries, such as vehicles and space. In order to replace the existing process, the newly proposed method must be verified in terms of sustainability as well as quality and efficiency of production. This research presents verification of the newly introduced AM process through one case study about cushion manufacturing. In the conventional cushion manufacturing process, there are inefficient processes, such as a requirement of too many types of foam and a dependency on a manual assembly process that relies on the skill of the individual worker. In this study, a selective laser sintering (SLS) process for improving the cushioning process is suggested. Through orthogonal and regression analysis, the relationship between the three SLS process parameters (laser power, scan speed, and hatching distance) and cushion comfort factors (compression load, sag factor, and hysteresis loss rate) was derived by polynomial fitting to replace the conventional cushion foam with the additively manufactured lattice structure. In addition, the results of the comparison between AM process and the conventional manufacturing (CM) process in two aspects of energy use and environmental impact were derived through the life cycle assessment (LCA) of the cushion manufacturing process. The results show that AM has clear advantages in both energy use and environmental impact in less than 75 production volumes, and the derived regression equation shows that desirable cushion properties can be implemented through only one process and one material. The presented parametric design and sustainable assessment framework will contribute as a decision-making tool for researchers and market stakeholders who want to improve the inefficiency of existing processes in all industries.ope