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Full-Text Articles in Physical Sciences and Mathematics
A Bayesian Spatial Scan Statistic For Normal Data, Laasya Velamakanni
A Bayesian Spatial Scan Statistic For Normal Data, Laasya Velamakanni
Theses and Dissertations
Scan statistics are useful methods for detecting spatial clustering. While they were initially developed to detect regions with an excess of binomial or Poisson events, spatial scan statistics have been extended to detect hotspots in other types of data including continuous data. They have many applications in different fields such as epidemiology (e.g. detecting disease outbreaks), sociology (e.g. detecting crime hotspots), and environmental health (e.g. detecting high-pollution areas). Spatial scan statistics identify a ‘most likely cluster’ and then use a likelihood ratio test to determine if this cluster is statistically significant. Spatial scan statistics have been extended to the Bayesian …
Accurate And Integrative Detection Of Copy Number Variants With High-Throughput Data, Xizhi Luo
Accurate And Integrative Detection Of Copy Number Variants With High-Throughput Data, Xizhi Luo
Theses and Dissertations
Copy number variation, as a major source of genetic variation in the human genome, are gains or losses of the DNA segments. Copy number variation has gained considerable interest as it plays important roles in human complex diseases. Therefore, accurate detection of CNVs with data generated by modern genotyping technologies, such as SNP array and whole-exome sequencing (WES), comprises a critical step toward a better understanding of disease etiology. However, current statistical methodologies for CNV detection still face analytical challenges due to numerous genetic and technological factors that may lead to spurious findings. First, existing methods assume the independent observations …
Time Series Analysis Of Weather Data In South Carolina, Geophrey Odero
Time Series Analysis Of Weather Data In South Carolina, Geophrey Odero
Theses and Dissertations
This thesis discusses time series analysis of weather data in South Carolina for the last fifteen years (January 2003 to December 2017) for Columbia, Greenville and North Myrtle Beach. The first part presents a brief overview of different variables that are used in the analysis. That is, temperature, dew point, humidity and sea level pressure. A short discussion of time series data is also introduced. The second part is about modeling the variables. The models of choice are presented, fitted and model diagnostics is carried out. In the third part, we discuss background on climates of the cities and model …
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 …
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 …