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Full-Text Articles in Physical Sciences and Mathematics
Contrastive Learning For Unsupervised Auditory Texture Models, Christina Trexler
Contrastive Learning For Unsupervised Auditory Texture Models, Christina Trexler
Computer Science and Computer Engineering Undergraduate Honors Theses
Sounds with a high level of stationarity, also known as sound textures, have perceptually relevant features which can be captured by stimulus-computable models. This makes texture-like sounds, such as those made by rain, wind, and fire, an appealing test case for understanding the underlying mechanisms of auditory recognition. Previous auditory texture models typically measured statistics from auditory filter bank representations, and the statistics they used were somewhat ad-hoc, hand-engineered through a process of trial and error. Here, we investigate whether a better auditory texture representation can be obtained via contrastive learning, taking advantage of the stationarity of auditory textures to …
Using Deep Learning For Children Brain Image Analysis, Rafael Toche Pizano
Using Deep Learning For Children Brain Image Analysis, Rafael Toche Pizano
Computer Science and Computer Engineering Undergraduate Honors Theses
Analyzing the correlation between brain volumetric/morphometry features and cognition/behavior in children is important in the field of pediatrics as identifying such relationships can help identify children who may be at risk for illnesses. Understanding these relationships can not only help identify children who may be at risk of illnesses, but it can also help evaluate strategies that promote brain development in children. Currently, one way to do this is to use traditional statistical methods such as a correlation analysis, but such an approach does not make it easy to generalize and predict how brain volumetric/morphometry will impact cognition/behavior. One of …
Trunctrimmer: A First Step Towards Automating Standard Bioinformatic Analysis, Z. Gunner Lawless, Dana Dittoe, Dale R. Thompson, Steven C. Ricke
Trunctrimmer: A First Step Towards Automating Standard Bioinformatic Analysis, Z. Gunner Lawless, Dana Dittoe, Dale R. Thompson, Steven C. Ricke
Computer Science and Computer Engineering Undergraduate Honors Theses
Bioinformatic analysis is a time-consuming process for labs performing research on various microbiomes. Researchers use tools like Qiime2 to help standardize the bioinformatic analysis methods, but even large, extensible platforms like Qiime2 have drawbacks due to the attention required by researchers. In this project, we propose to automate additional standard lab bioinformatic procedures by eliminating the existing manual process of determining the trim and truncate locations for paired end 2 sequences. We introduce a new Qiime2 plugin called TruncTrimmer to automate the process that usually requires the researcher to make a decision on where to trim and truncate manually after …
Simulating Foodborne Pathogens In Poultry Production And Processing To Defend Against Intentional Contamination, Silas B. Lankford
Simulating Foodborne Pathogens In Poultry Production And Processing To Defend Against Intentional Contamination, Silas B. Lankford
Computer Science and Computer Engineering Undergraduate Honors Theses
There is a lack of data in recent history of food terrorism attacks, and as such, it is difficult to predict its impact. The food supply industry is one of the most vulnerable industries for terrorist threats while the poultry industry is one of the largest food industries in the United States. A small food terrorism attack against just a single poultry processing center has the potential to affect a much larger population than its immediate consumers. In this work, the spread of foodborne pathogens is simulated in a poultry production and processing system to defend against intentional contamination. An …
A Support Vector Machine Base Model For Predicting Heparin-Binding Proteins Using Biological Metrics And Xb Patterns As Features, Joseph W. Sirrianni
A Support Vector Machine Base Model For Predicting Heparin-Binding Proteins Using Biological Metrics And Xb Patterns As Features, Joseph W. Sirrianni
Computer Science and Computer Engineering Undergraduate Honors Theses
Heparin is a highly sulphated and negatively charged polysaccharides belonging to the glycosamino- glycans(GAGs) family. It is widely used in medical treatments as an injectable anticoagulant. Although many heparin-binding proteins have been identified through experimental studies, there are still many proteins needing to be classified as heparin-binding or not. Many studies have been aimed at prediction of heparin binding patterns or motifs in the primary structure of proteins. For example XBBXBX and XBBBXXBX are two well-known patterns or motifs. In spite of intensive studies, still no good model has emerged which reasonably predicts proteins in the protein database as heparin-binding …