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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 ...
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 ...
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 ...
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 ...
Coarse-grained dynamics derived structural ensemble for prediction of metal binding sites of protein and phenotypic effects of variants
Structures of proteins play a key role in determining their functions. Knowledge of structure,
especially the details of specific sites of a protein can help us understand their contribution to
the overall activity. ...
Learning Across Domains: Applications to Text-based Person Search and Multi-Source Domain Adaptation
With rapid development in technology and ubiquitous presence of diverse types of sensors, a large
amount of data from different modalities (e.g., text, audio, images etc.) describing the same person/
object/event has ...