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Characterization of Divergence resulting from Workload, Memory and Control-Flow behavior in GPGPU Applications
GPGPUs have emerged as high-performance computing platforms and are used for boosting
the performance of general non-graphics applications from various scientifi c domains. These
applications span varied areas like social ...
Advances in High Dynamic Range Imaging Using Deep Learning
Natural scenes have a wide range of brightness, from dark starry nights to bright sunlit beaches. Our human eyes can perceive such a vast range of illumination through various adaptation techniques, thus allowing us to ...
Optimization of Traversal Queries on Distributed Graph Stores
In this era of Big Data Analytics, much of the semi-structured data has inherent interconnectivity
between representative entities. These are increasingly being modeled as property graphs
because of the semantic advantages ...
Deep Visual Representations: A study on Augmentation, Visualization, and Robustness
Deep neural networks have resulted in unprecedented performances for various learning tasks. Particularly,
Convolutional Neural Networks (CNNs) are shown to learn representations that can efficiently
discriminate hundreds ...
Development of advanced regularization methods to improve photoacoustic tomography
Photoacoustic tomography (PAT) is a scalable imaging modality having huge potential for
imaging biological samples at very high depth to resolution ratio, thereby playing pivotal role
in the areas of neuroscience, ...
Novel Deep Learning Methods for Improving Low-Dose Computed Tomography Perfusion Imaging of Brain
Computed Tomography (CT) Perfusion imaging is a non-invasive medical imaging modality that has also established itself as a fast and economical imaging modality for diagnosing cerebrovascular diseases such as acute ischemia, ...
Efficient and Resilient Stream Processing in Distributed Shared Environment
Internet of Things (IoT) deployments comprising of sensors and actuators collect observational
data and provide continuous streams of data, often called streaming data or fast data. Smart
Cities use such IoT technologies ...
Reliable and Efficient Application Scheduling on Edge, Fog and Cloud
Cloud computing has emerged in the last decade as a popular distributed computing service
offered by commercial providers. Public Clouds offer pay-as-you-go access to elastic resources
that can be acquired and released ...
Epistasis Detection and Phenotype Prediction in GWAS Using Machine Learning Methods
Genome-wide association studies (GWAS) are used to find the association between genetic variants, Single Nucleotide Polymorphisms (SNPs), and phenotypic traits or diseases in a population. The number of GWAS has increased ...
Deep Convolutional and Generative Networks for Ocean Synoptic Feature Extraction and Super Resolution from Remotely Sensed Images
Accurate extraction of Synoptic Ocean Features and Downscaling of Ocean Features is crucial
for climate studies and the operational forecasting of ocean systems. With the advancement of
space and sensor technologies, the ...