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Full-Text Articles in Computer Sciences
Extracting Social Network Model Parameters From Social Science Literature, Isaac Batts
Extracting Social Network Model Parameters From Social Science Literature, Isaac Batts
Theses and Dissertations--Computer Science
When looking at computer modeling of social situations, much of the social science literature does not include ready-to-use statistics or parameters to be included in a social model. I explore studies related to speaking about racism (and other forms of bias), and interventions designed to diminish the occurrence of biased behavior, and use those readings to synthesize plausible parameters for a social computer model.
Flexible Attenuation Fields: Tomographic Reconstruction From Heterogeneous Datasets, Clifford S. Parker
Flexible Attenuation Fields: Tomographic Reconstruction From Heterogeneous Datasets, Clifford S. Parker
Theses and Dissertations--Computer Science
Traditional reconstruction methods for X-ray computed tomography (CT) are highly constrained in the variety of input datasets they admit. Many of the imaging settings -- the incident energy, field-of-view, effective resolution -- remain fixed across projection images, and the only real variance is in the detector's position and orientation with respect to the scene. In contrast, methods for 3D reconstruction of natural scenes are extremely flexible to the geometric and photometric properties of the input datasets, readily accepting and benefiting from images captured under varying lighting conditions, with different cameras, and at disparate points in time and space. Extending CT …
Language Models For Rare Disease Information Extraction: Empirical Insights And Model Comparisons, Shashank Gupta
Language Models For Rare Disease Information Extraction: Empirical Insights And Model Comparisons, Shashank Gupta
Theses and Dissertations--Computer Science
End-to-end relation extraction (E2ERE) is a crucial task in natural language processing (NLP) that involves identifying and classifying semantic relationships between entities in text. This thesis compares three paradigms for end-to-end relation extraction (E2ERE) in biomedicine, focusing on rare diseases with discontinuous and nested entities. We evaluate Named Entity Recognition (NER) to Relation Extraction (RE) pipelines, sequence-to-sequence models, and generative pre-trained transformer (GPT) models using the RareDis information extraction dataset. Our findings indicate that pipeline models are the most effective, followed closely by sequence-to-sequence models. GPT models, despite having eight times as many parameters, perform worse than sequence-to-sequence models and …