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Sparsification of Reaction-Diffusion Dynamical Systems on Complex Networks
Graph sparsification is an area of interest in computer science and applied mathematics. Spar-
sification of a graph, in general, aims to reduce the number of edges in the network while
preserving specific properties of ...
An arbitrary lagrangian eulerian volume of fluid method for floating body dynamics simulation
The floating body dynamics is treated as a Fluid-Structure Interaction (FSI) problem. A FSI problem is where the forces from the fluid move/deform the interacting structure, and the movement of the structure, in turn, ...
Development of Novel Deep Learning Models with Improved Generalizability for Medical Image Analysis
Medical imaging is a process of visualization of disease/tissue in a non-invasive manner. Several imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), optical coherence tomography (OCT), and ...
Integrating Coarse Semantic Information with Deep Image Representations for Object Localization, Model Generalization and Efficient Training
Coarse semantic features are abstract descriptors capturing broad semantic information in an
image, including scene labels, crude contextual relationships between objects in the scene, or
even objects described using ...
Structural and functional studies on the hypothetical protein TTHA1873 from Thermus thermophilus
This thesis reports a detailed study on the structural and functional characterization of the hypothetical protein (TTHA1873) from Thermus thermophilus. In this study, both the structure and function of TTHA1873 were ...
Scalable Video Data Management and Visual Querying System for Autonomous Camera Networks
Video data has been historically known for its unstructured nature, rich semantic content and
scalability issues in terms of storage. With advances in computer vision and Deep Neural Net works (DNNs) it is now possible ...
Novel Neural Architectures based on Recurrent Connections and Symmetric Filters for Visual Processing
Artificial Neural Networks (ANN) have been very successful due to their ability to extract meaningful information without any need for pre-processing raw data. First artificial neural networks were created in essence to ...
Communication Overlapping Krylov Subspace Methods for Distributed Memory Systems
Many high performance computing applications in computational fluid dynamics, electromagnetics etc. need to solve a linear system of equations $Ax=b$. For linear systems where $A$ is generally large and sparse, Krylov ...
Mitigating Domain Shift via Self-training in Single and Multi-target Unsupervised Domain Adaptation
Though deep learning has achieved significant successes in many computer vision tasks, the state-of-the-art approaches rely on the availability of a large amount of labeled data for supervision, collection of which is ...
Data Driven Stabilization Schemes for Singularly Perturbed Differential Equations
This thesis presents a novel way of leveraging Artificial Neural Network (ANN) to aid
conventional numerical techniques for solving Singularly Perturbed Differential Equation (SPDE). SPDEs are challenging to solve with ...