Graduate Recital:Sara Giovanelli, Horn

Kemp Recital Hall Friday Evening March 21, 2003 9:00p.m

The iWildCam 2018 Challenge Dataset

Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation. With the vast amounts of data now available it is imperative that we develop automatic solutions for annotating camera trap data in order to allow this research to scale. A promising approach is based on deep networks trained on human-annotated images. We provide a challenge dataset to explore whether such solutions generalize to novel locations, since systems that are trained once and may be deployed to operate automatically in new locations would be most useful.Comment: Challenge hosted at the fifth Fine-Grained Visual Categorization Workshop (FGVC5) at CVPR 201

Senior Recital: Brekke Day, Horn; Sara Hoffee, Piano; February 28, 2010

Kemp Recital HallFebruary 28, 2010Sunday Evening8:00 p.m

Understanding Loan Use and Debt Burden Among Low-income and Minority Students at a Large Urban Community College

This study examined a sample of community college students from a diverse, large urban community college system in Texas. To gain a deeper understanding about the effects of background characteristics on student borrowing behaviors and enrollment outcomes, the study employed descriptive statistics and regression techniques to examine two separate samples of students consisting of 1) loan recipients and 2) non-loan recipients. Chen’s heterogeneous research model served as the conceptual framework in the selection of predictors of interest and outcome variables. This study primarily focused on the relationship between borrowing and enrollment outcomes of low-income and racially/ethnically diverse students. Results show that students taking on debt at Metropolitan Community College (a pseudonym) are primarily female, Black, over the age of 20, low-income, and not academically prepared. While race/ethnicity did not significantly influence cumulative debt amount, race/ethnicity did account for significant differences in the likelihood of completion or transfer for both loan recipients and non-loan recipients

Gross Anatomy of Bifid Xiphoid Process

Introduction: Xiphoid process is the ossified extension of the lower sternum in the chest midline of human adults. Natural variance in this process results in the less common bifurcated morphology. Objective: The primary objective of this study was to explore the distribution of the bifid variant. As a secondary objective, we aimed to compare bifurcated Xiphoid processes to other possible variations. Methods: A case study was designed to explore the distribution of bifid variants in a sample of cadavers (n=30) at a large medical education institution. The dependent variable was binarized (bifid or normal) and univariate analyses were performed based on the height, width, and presence of bifurcation measured in each cadaver. Results: Data is reported using textual and diagrammatic visuals. A significant difference was observed between the measurements of the bifid and the normal variants. Additionally, we provide a review of the implications of this bony landmark for mediastinal pressure and surgeries performed on the thoracic and abdominal areas. Conclusion: This case study demonstrates a significant variation of the Xiphoid process, aiding clinicians in performing more accurate imaging and diagnosis. Future research should consider the physiological effects and clinical significance of this process

The iWildCam 2018 Challenge Dataset

Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation. With the vast amounts of data now available it is imperative that we develop automatic solutions for annotating camera trap data in order to allow this research to scale. A promising approach is based on deep networks trained on human-annotated images. We provide a challenge dataset to explore whether such solutions generalize to novel locations, since systems that are trained once and may be deployed to operate automatically in new locations would be most useful

Financial Aid Packaging at Community Colleges: Which types of awards packages increase student persistence?

Increasing college costs, coupled with decreasing financial aid has raised public concerns over the affordability of higher education. For the past four decades, the nation has seen the cost of tuition rise at levels that exceed inflation, and financial assistance rates that have not kept pace with that growth. Studies suggest that these financial resources play a role in influencing college attendance decisions and persistence for low-income students. This study examines the characteristics of zero-EFC students as compared to non-zero EFC students and determines the extent to which a gift-aid only, and gift-aid plus loans awards package affects the likelihood of persistence. Also, it explores the relationship between the ratio of loans-to-gift-aid, and the likelihood of persistence across income levels. By employing logistic regression, this study aims to determine if there are differential effects among financial aid award packages, and if the ratio of a loans-to-gift-aid package affects persistence by income status. Results demonstrated that a gift-aid only package, and a gift-aid plus loans package negatively influenced the enrollment outcomes of zero-EFC students and positively influenced the enrollment outcomes of high-income students. Additionally, when examining the ratio of loans-to-gift-aid for students with a gift-aid and loans package, results showed that the higher the ratio of loans to gift-aid, the higher the likelihood of persistence for all income levels. In an era where the rising costs of a college education are becoming more difficult to cover with present levels of financial aid, earning a higher education credential is possible if students are willing to take on educational debt. A comprehensive higher education plan that acknowledges financial barriers as fundamental obstacles to the college success of its lowest income students is necessary to preserving equal opportunity to upward social mobility

Benchmarking Representation Learning for Natural World Image Collections

Recent progress in self-supervised learning has resulted in models that are capable of extracting rich representations from image collections without requiring any explicit label supervision. However, to date the vast majority of these approaches have restricted themselves to training on standard benchmark datasets such as ImageNet. We argue that fine-grained visual categorization problems, such as plant and animal species classification, provide an informative testbed for self-supervised learning. In order to facilitate progress in this area we present two new natural world visual classification datasets, iNat2021 and NeWT. The former consists of 2.7M images from 10k different species uploaded by users of the citizen science application iNaturalist. We designed the latter, NeWT, in collaboration with domain experts with the aim of benchmarking the performance of representation learning algorithms on a suite of challenging natural world binary classification tasks that go beyond standard species classification. These two new datasets allow us to explore questions related to large-scale representation and transfer learning in the context of fine-grained categories. We provide a comprehensive analysis of feature extractors trained with and without supervision on ImageNet and iNat2021, shedding light on the strengths and weaknesses of different learned features across a diverse set of tasks. We find that features produced by standard supervised methods still outperform those produced by self-supervised approaches such as SimCLR. However, improved self-supervised learning methods are constantly being released and the iNat2021 and NeWT datasets are a valuable resource for tracking their progress.Comment: CVPR 202