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- All Subjects: Music
- All Subjects: Machine Learning
- Creators: Barrett, The Honors College
- Creators: Holbrook, Amy
- Creators: Spring, Robert
The aim of this project was to create an original sound design and score for the ASU SOMDT production of HEDDATRON, by Elizabeth Meriwether. Composition and sound design was done primarily with a modular synthesizer. All audio editing was done in Reaper, and the cues were programmed in Qlab.
The aim of this creative project was to explore the ideas of impermanence and transience through the lens of different, largely non-western cultural backgrounds, and to incorporate what I learned into my own work as a painter. As part of this, I focused on the materials, techniques, visual strategies, and philosophies that guided the creation of these works. The project consisted of a discrete research phase, during which time I gathered information and materials related to my topic, and a creation phase, when I focused largely on the production of oil paintings and ink paintings whose technique and/or subject matter pertained to impermanence. Research for the most part was conducted by utilizing online and physical collections of work to analyze the formal elements of the work along with the cultural context in which it was created. Ultimately the creative project resulted in a product of three oil paintings and five ink paintings.
Music has consistently been documented as a manner to bring people together across cultures throughout the world. In this research, we propose that people use similar musical taste as a strong sign of potential social connection. To investigate this notion, we draw on literature examining how music merges the public/private self, the link to personality, and group identity, as well as how it is linked to romantic relationships. Thus, music can be a tool when wanting to get to know someone else and/or forge a platonic relationship. To test this hypothesis, we designed an experiment comparing music relative to another commonality (sharing a sports team in common) to see which factor is stronger in triggering an online social connection. We argue that people believe they have more in common with someone who shares similar music taste compared to other commonalities. We discuss implications for marketers on music streaming platforms.
Music has consistently been documented as a manner to bring people together across cultures throughout the world. In this research, we propose that people use similar musical tastes as a strong sign of potential social connection. To investigate this notion, we draw on literature examining how music merges the public/private self, the link to personality, and group identity, as well as how it is linked to romantic relationships. Thus, music can be a tool when wanting to get to know someone else and/or forge a platonic relationship. To test this hypothesis, we designed an experiment comparing music relative to another commonality (sharing a sports team in common) to see which factor is stronger in triggering an online social connection. We argue that people believe they have more in common with someone who shares similar music taste compared to other commonalities. We discuss implications for marketers on music streaming platforms.
The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.
High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.