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Representation And Reconstruction Of Linear, Time-Invariant Networks, Nathan Scott Woodbury
Representation And Reconstruction Of Linear, Time-Invariant Networks, Nathan Scott Woodbury
Theses and Dissertations
Network reconstruction is the process of recovering a unique structured representation of some dynamic system using input-output data and some additional knowledge about the structure of the system. Many network reconstruction algorithms have been proposed in recent years, most dealing with the reconstruction of strictly proper networks (i.e., networks that require delays in all dynamics between measured variables). However, no reconstruction technique presently exists capable of recovering both the structure and dynamics of networks where links are proper (delays in dynamics are not required) and not necessarily strictly proper.The ultimate objective of this dissertation is to develop algorithms capable of …
Fully Convolutional Neural Networks For Pixel Classification In Historical Document Images, Seth Andrew Stewart
Fully Convolutional Neural Networks For Pixel Classification In Historical Document Images, Seth Andrew Stewart
Theses and Dissertations
We use a Fully Convolutional Neural Network (FCNN) to classify pixels in historical document images, enabling the extraction of high-quality, pixel-precise and semantically consistent layers of masked content. We also analyze a dataset of hand-labeled historical form images of unprecedented detail and complexity. The semantic categories we consider in this new dataset include handwriting, machine-printed text, dotted and solid lines, and stamps. Segmentation of document images into distinct layers allows handwriting, machine print, and other content to be processed and recognized discriminatively, and therefore more intelligently than might be possible with content-unaware methods. We show that an efficient FCNN with …
Scalable Detection And Extraction Of Data In Lists In Ocred Text For Ontology Population Using Semi-Supervised And Unsupervised Active Wrapper Induction, Thomas L. Packer
Scalable Detection And Extraction Of Data In Lists In Ocred Text For Ontology Population Using Semi-Supervised And Unsupervised Active Wrapper Induction, Thomas L. Packer
Theses and Dissertations
Lists of records in machine-printed documents contain much useful information. As one example, the thousands of family history books scanned, OCRed, and placed on-line by FamilySearch.org probably contain hundreds of millions of fact assertions about people, places, family relationships, and life events. Data like this cannot be fully utilized until a person or process locates the data in the document text, extracts it, and structures it with respect to an ontology or database schema. Yet, in the family history industry and other industries, data in lists goes largely unused because no known approach adequately addresses all of the costs, challenges, …
A Confidence-Prioritization Approach To Data Processing In Noisy Data Sets And Resulting Estimation Models For Predicting Streamflow Diel Signals In The Pacific Northwest, Nathaniel Lee Gustafson
A Confidence-Prioritization Approach To Data Processing In Noisy Data Sets And Resulting Estimation Models For Predicting Streamflow Diel Signals In The Pacific Northwest, Nathaniel Lee Gustafson
Theses and Dissertations
Streams in small watersheds are often known to exhibit diel fluctuations, in which streamflow oscillates on a 24-hour cycle. Streamflow diel fluctuations, which we investigate in this study, are an informative indicator of environmental processes. However, in Environmental Data sets, as well as many others, there is a range of noise associated with individual data points. Some points are extracted under relatively clear and defined conditions, while others may include a range of known or unknown confounding factors, which may decrease those points' validity. These points may or may not remain useful for training, depending on how much uncertainty they …