Predicting Citi Bike Demand Evolution Using Dynamic Graphs

Bike sharing systems often suffer from poor capacity management as a result of variable demand. These bike sharing systems would benefit from models to predict demand in order to moderate the number of bikes stored at each station. In this paper, we attempt to apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.

Double U-Net for Super-Resolution and Segmentation of Live Cell Images

Accurate segmentation of live cell images has broad applications in clinical and research contexts. Deep learning methods have been able to perform cell segmentations with high accuracy; however developing machine learning models to do this requires access to high fidelity images of live cells. This is often not available due to resource constraints like limited accessibility to high performance microscopes or due to the nature of the studied organisms. Segmentation on low resolution images of live cells is a difficult task. This paper proposes a method to perform live cell segmentation with low resolution images by performing super-resolution as a pre-processing step in the segmentation pipeline.