Real estate syndications since the Tax Reform Act of 1986

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1987.Bibliography: leaves 76-77.by Megan Mary Dobroth.M.S

The Unspoken

Studio boards consist of two single-page pdfs

Machine learning approaches to multi-animal behavioral analysis

One of the most important tests of selective social relationships in laboratory animals is the partner preference test (PPT). This test quantifies the preference of a certain animal for a familiar individual by comparing the time it spends with that animal to the time it spends with a stranger over a three-hour period. Though effective and useful, the PPT is time consuming and labor intensive to score by hand. In this project, we attempted to use free deep learning programs to create a process to automatically score partner preference tests. We used DeepLabCut (pose analysis software) and SimBA (behavioral analysis software) to track the movements of voles (Microtus sp.) as they moved around a preference test arena. We compared four different test models with various specifications to determine which was the most accurate in identifying individual animals and in correctly identifying body parts. We found that the programs were uniformly mediocre at identifying body parts, and the models made in multi-animal DeepLabCut were significantly worse than the single-animal DeepLabCut model at identifying individual animals. As the identification of individual animals is a crucial step in creating a program that can score a PPT, we were unfortunately unable to create a working version. However, when new releases of DeepLabCut and SimBA improve individual identification, the foundational work of this project can be used to create a process to make it easier and more efficient to analyze partner preference tests and so contribute to the future work of the lab