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Migrating VM Workloads to Containers: Issues and Challenges
Modern day enterprises are adopting virtualization to leverage the benefits of improved server utilization through workload consolidation. Server consolidation provides this benefit to enterprise applications as many of ...
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, ...
Algorithms for Estimating Integrals in High Dimensional Spaces
Sampling, estimation and integration in high dimensional continuous spaces is required in diverse areas ranging from modeling multi-particle physical systems and optimization to inference from data. When the number of ...
A study on Deep Learning Approaches, Architectures and Training Methods for Crowd Analysis
Analyzing large crowds quickly is one of the highly sought-after capabilities nowadays. Especially in terms of public security and planning, this assumes prime importance. But automated reasoning of crowd images or videos ...
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 ...