Lung Segmentation from Chest X-rays using Variational Data Imputation

Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the high opacity regions as missing data and present a modified CNN-based image segmentation network that utilizes a deep generative model for data imputation. We train this model on normal CXRs with extensive data augmentation and demonstrate the usefulness of this model to extend to cases with extreme abnormalities.Comment: Accepted to be presented at the first Workshop on the Art of Learning with Missing Values (Artemiss) hosted by the 37th International Conference on Machine Learning (ICML). Source code, training data and the trained models are available here: https://github.com/raghavian/lungVAE

Understanding from Machine Learning Models

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding

Smoke and Shadows: Rendering and Light Interaction of Smoke in Real-Time Rendered Virtual Environments

Realism in computer graphics depends upon digitally representing what we see in the world with careful attention to detail, which usually requires a high degree of complexity in modelling the scene. The inevitable trade-off between realism and performance means that new techniques that aim to improve the visual fidelity of a scene must do so without compromising the real-time rendering performance. We describe and discuss a simple method for realistically casting shadows from an opaque solid object through a GPU (graphics processing unit) based particle system representing natural phenomena, such as smoke

Atmospheric Circulation and Composition of GJ1214b

The exoplanet GJ1214b presents an interesting example of compositional degeneracy for low-mass planets. Its atmosphere may be composed of water, super-solar or solar metallicity material. We present atmospheric circulation models of GJ1214b for these three compositions, with explicit grey radiative transfer and an optional treatment of MHD bottom drag. All models develop strong, superrotating zonal winds (~ 1-2 km/s). The degree of eastward heat advection, which can be inferred from secondary eclipse and thermal phase curve measurements, varies greatly between the models. These differences are understood as resulting from variations in the radiative times at the thermal photosphere, caused by separate molecular weight and opacity effects. Our GJ1214b models illustrate how atmospheric circulation can be used as a probe of composition for similar tidally-locked exoplanets in the mini-Neptune/waterworld class.Comment: 14 pages, 4 figures, 1 table, accepted for publication in ApJ