Interactive Web-based Visualization of Atomic Position-time Series Data

Extracting and interpreting the information contained in large sets of time-varying three dimensional positional data for the constituent atoms of simulated material system is a challenging task. This thesis work reports our initial implementation of a web-based visualization system and its use-case study. The system allows the users to perform the desired visualization task on a web browser for the position-time series data extracted from the local or remote hosts. It involves a pre-processing step for data reduction, which involves skipping uninteresting parts of the data uniformly (at full atomic configuration level) or non-uniformly (at atomic species level or individual atom level). Atomic configuration at a given time step (snapshot) is rendered using the ball-stick representation and can be animated by rendering successive configurations. The entire atomic dynamics can be captured as the trajectories by rendering the atomic positions at all time steps together as points. The trajectories can be manipulated at both species and atomic levels so that we can focus on one or more trajectories of interest. They can be color-coded according to the additional information including the time elapsed and the distance traveled. The instantaneous atomic structure and the complete trajectories can be superimposed to help assess the 3D geometries and extents of the selected trajectories. The implementation was done using WebGL and Three.js for graphical rendering, HTML5 and Javascript for GUI, and Elasticsearch and JSON for data storage and retrieval within the Grails Framework. We have demonstrated the usefulness of our visualization system by analyzing the simulated position-time series for proton-bearing forsterite (Mg2SiO4) system – an abundant mineral of Earths upper mantle. Visualization reveals that protons (hydrogen ions) incorporated as interstitials are much more mobile than protons substituting the host Mg and Si cation sites. The proton diffusion appears to be anisotropic with high mobility along the x-direction, showing limited discrete jumps in other two directions. Our work at the present represents a simplistic (direct) web-based rendering of large atomic data sets. While the atomic structure can be animated at an interactive rate, the trajectory processing is slow, taking several minutes. We anticipate to further improve the system and use it in gaining useful structural and dynamical information from more materials simulation data

Learning to Interpret Fluid Type Phenomena via Images

Learning to interpret fluid-type phenomena via images is a long-standing challenging problem in computer vision. The problem becomes even more challenging when the fluid medium is highly dynamic and refractive due to its transparent nature. Here, we consider imaging through such refractive fluid media like water and air. For water, we design novel supervised learning-based algorithms to recover its 3D surface as well as the highly distorted underground patterns. For air, we design a state-of-the-art unsupervised learning algorithm to predict the distortion-free image given a short sequence of turbulent images. Specifically, we design a deep neural network that estimates the depth and normal maps of a fluid surface by analyzing the refractive distortion of a reference background pattern. Regarding the recovery of severely downgraded underwater images due to the refractive distortions caused by water surface fluctuations, we present the distortion-guided network (DG-Net) for restoring distortion-free underwater images. The key idea is to use a distortion map to guide network training. The distortion map models the pixel displacement caused by water refraction. Furthermore, we present a novel unsupervised network to recover the latent distortion-free image. The key idea is to model non-rigid distortions as deformable grids. Our network consists of a grid deformer that estimates the distortion field and an image generator that outputs the distortion-free image. By leveraging the positional encoding operator, we can simplify the network structure while maintaining fine spatial details in the recovered images. We also develop a combinational deep neural network that can simultaneously perform recovery of the latent distortion-free image as well as 3D reconstruction of the transparent and dynamic fluid surface. Through extensive experiments on simulated and real captured fluid images, we demonstrate that our proposed deep neural networks outperform the current state-of-the-art on solving specific tasks

S17RS SGR No. 18 (International Student Policy)

To urge and request the University to adjust their policies regarding international students to allow additional time for compliance with University policy change

S17RS SGR No. 17 (Healthcare International Students)

To urge and request the University to roll back the minimum healthcare standards imposed on international students to previous levels consistent with the minimum healthcare standards required by the US Federal Government (in accordance with international student visa requirements