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Constraint-Driven Open-World Scene Generation
Get PDFWe introduce an alternative method for open-world scene generation. In this thesis, Graph-based Wave Function Collapse (GWFC) is integrated with Space Colonization Algorithm (SCA) and used to place objects in an unstructured 3D environment. This combined algorithm, Space Colonization Graph-based Wave Function Collapse (SC-GWFC), leverages the constraint-based capabilities of GWFC and the ability of SCA to populate arbitrary 3D volumes. We demonstrate that objects of variable scale can be successfully used with SC-GWFC. Since this algorithm is run in an interactive environment, we demonstrate iterative modifications to a partially complete scene and incorporate PCG into a scene editing process. As part of the implementation, we also introduce our Scene Modeling Application for rendering and editing 3D scenes. This modeling application allows for editing and viewing constraints for our SC-GWFC scene generator. We evaluate the performance characteristics of SC-GWFC in the Scene Modeling Application to demonstrate that SC-GWFC can be used interactively. Through the application, users can specify adjacency requirements for objects, and SC-GWFC will attempt to place objects in patterns that respect these rules. We demonstrate the ability to place up to 5000 items on a terrain using our proposed SC-GWFC technique
Neural Tabula Rasa: Foundations for Realistic Memories and Learning
Get PDFUnderstanding how neural systems perform memorization and inductive learning tasks are of key interest in the field of computational neuroscience. Similarly, inductive learning tasks are the focus within the field of machine learning, which has seen rapid growth and innovation utilizing feedforward neural networks. However, there have also been concerns regarding the precipitous nature of such efforts, specifically in the area of deep learning. As a result, we revisit the foundation of the artificial neural network to better incorporate current knowledge of the brain from computational neuroscience. More specifically, a random graph was chosen to model a neural system. This random graph structure was implemented along with an algorithm for storing information, allowing the network to create memories by creating subgraphs of the network. This implementation was derived from a proposed neural computation system, the Neural Tabula Rasa, by Leslie Valiant. Contributions of this work include a new approximation of memory size, several algorithms for implementing aspects of the Neural Tabula Rasa, and empirical evidence of the functional form for memory capacity of the system. This thesis intends to benefit the foundations of learning systems, as the ability to form memories is required for a system to inductively learn
Environmental Controls on Soil Organic Matter Fractions in Post-fire Landscapes
Get PDFIn the past few decades, the world’s climate is being altered by anthropogenic forces such as the increase in carbon dioxide (CO2) emissions due to fossil fuel combustion (Paraschiv and Paraschiv, 2020). In California, the impacts of increased CO2 concentrations have created longer fire seasons, warmer temperatures, and increased lightning strikes which allow for wildfires to become more frequent, severe, and larger in scale (Westerling et al., 2006). Wildfires of this magnitude result in acceleration of landscape scale processes such as erosion and aggregation which in turn affects soil organic matter (SOM), the basis of soil health (Gonz´alez-P´erez et al., 2004). This project is intended to close the knowledge gap and broaden our understanding of the impacts of burn severity on SOM partitioning at the watershed scale in California. To accomplish this, soil samples were first collected one year following the CZU Lightning Complex Fire at Swanton Pacific Ranch. The samples were separated into particulate organic matter (POM) and mineral-associated organic matter (MAOM), analyzed for carbon (C) and nitrogen (N) content, and then their stock was compared to burn severity and other environmental covariates. The environmental covariates were derived from geospatial data corresponding to the soil forming factors and burn severity to understand the watershed scale controls on SOM fractions. Spatial data included remote sensing data corresponding to terrain, climate, vegetation, lithology, and fire variables. Through analysis, Difference Normalized Burn Ratio (dNBR), climate data, and terrain data were the most dominant predictors of MAOM and POM C and N. Ultimately, this research will allow for greater understanding of landscape-scale SOM partitioning and soil quality post-fire
