We present the results of applying new object classification techniques to the supernova search of the Nearby Supernova Factory. In comparison to simple threshold cuts, more sophisticated methods such as boosted decision trees, random forests, and support vector machines provide dramatically better object discrimination: we reduced the number of nonsupernova candidates by a factor of 10 while increasing our supernova identification efficiency. Methods such as these will be crucial for maintaining a reasonable false positive rate in the automated transient alert pipelines of upcoming large optical surveys.

An important goal of character animation is to create believable, life-like movements and expressions. For film, artists spend significant amounts of effort to add sufficient complexity to a character rig to enable believable and emotionally evocative performances. However, once a complex character rig has been authored, an artist then needs to spend a significant amount of effort to animate a character and bring it to life. For video games, mesh deformations and geometry processing must be real-time, which affects the types of deformations included in a character rig for an interactive application. As a result, video game characters tend to lack some of the sophisticated deformations and motions seen in film-quality characters.

This dissertation explores applications of machine learning for improving the quality of deformations in real-time character rigs as well as applications to assist artists in producing high-quality animations. We detail a deep learning-based approach to enable complex film-quality mesh deformations to run in real-time for both a character's body and face. Our method learns mesh deformations from an existing character rig and produces an accurate approximation using significantly less computational time. In addition to mesh deformations, we present a statistical approach to synthesize novel animations from a collection of artist-created animations. Thus, single-use animations for film can be leveraged for additional applications. We also present a method for generating facial animation from a recorded performance, which provides artists with an initial animation that can be fine-tuned to meet stylistic and expressive needs.

Bacterial response to nitric oxide (NO) is of major importance since NO is an obligatory intermediate of the nitrogen cycle. Transcriptional regulation of the dissimilatory nitric oxides metabolism in bacteria is Large-scale workflows are becoming increasingly important in both the scientific research and business domains. Science and commerce have both experienced an explosion in the sheer amount of data that must be analyzed. An important tool for analyzing these huge data sets is a compute cluster of hundreds or thousands of machines. However, debugging and tuning clusters requires specialized tools. Current cluster performance tools are more oriented towards tightly coupled parallel applications. We describe how the NetLogger Toolkit methodology is more appropriate for this class of cluster computing, and describe our new automatic workflow anomaly detection component. We also describe how this methodology is being used in the Nearby Supernova Factory (SNfactory) project at Lawrence Berkeley National Laboratory.

We present the results of applying new object classification techniques to difference images in the context of the Nearby Supernova Factory supernova search. Most current supernova searches subtract reference images from new images, identify objects in these difference images, and apply simple threshold cuts on parameters such as statistical significance, shape, and motionto reject objects such as cosmic rays, asteroids, and subtraction artifacts. Although most static objects subtract cleanly, even a very low false positive detection rate can lead to hundreds of non-supernova candidates which must be vetted by human inspection before triggering additional followup. In comparison to simple threshold cuts, more sophisticated methods such as Boosted Decision Trees, Random Forests, and Support Vector Machines provide dramatically better object discrimination. At the Nearby Supernova Factory, we reduced the number of non-supernova candidates by a factor of 10 while increasing our supernova identification efficiency. Methods such as these will be crucial for maintaining a reasonable false positive rate in the automated transient alert pipelines of upcoming projects such as PanSTARRS and LSST.