Department of Computational and Data Sciences (CDS): Recent submissions
Now showing items 61-80 of 95
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Enhancing Blockchain Implementations
Blockchain technology elegantly provides a spectrum of desirable semantic properties, including immutability, authenticity, verifiability, and data integrity. In recent years, implementing blockchains on relational database ... -
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 ... -
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 ... -
Adaptive charging techniques for Li-ion battery using Reinforcement Learning
Li-ion batteries have become a promising technology in recent years and are used everywhere from low-end devices like mobile phones to high-end ones like electric vehicles. In most applications, the discharge of a battery ... -
Towards Designing PCM-Conscious Database Systems
Phase Change Memory (PCM) is a recently developed non-volatile memory technology that is expected to provide an attractive combination of the best features of conventional disks (persistence, capacity) and of DRAM (access ... -
Improving Data Center Utilisation by Reducing Fragmentation
Virtualization enables better server consolidation and utilisation compared to stand-alone servers running a single workload. This enabled wide-spread cloud adoption among many organizations. Data center utilisation is ... -
A Divide and Conquer Framework For Graph Processing in Distributed Heterogeneous Systems
In many fields of science and engineering graph data structures are used to represent real-world information. As these graphs scale in size, it becomes very inefficient to process these graphs on a single core CPU using ... -
EMF: System Design and Challenges for Disaggregated GPUs in datacenters for Efficiency, Modularity and Flexibility
With Dennard Scaling phasing out in the mid-2000s, architectural scaling and hardware specialization take centre stage to provide performance bene fits with already stalling Moore's law. An outcome from this hardware ... -
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. ... -
A Numerical Method for Modeling Light Scattering from Spherical Particles with a Probability Density in the Parametric Space
With modern computational tools, modeling light scattering from a particle with known physical parameters has become relatively trivial. Evaluation using analytical solutions is highly eficient compared to a full ... -
Vector Extrapolation and Guided Filtering Methods for Improving Photoacoustic and Microscopic Images
Photoacoustic imaging is a noninvasive imaging modality which combines the bene ts of optical contrast and ultrasonic resolution. It is applied widely for monitoring tissue health conditions in the elds of cardiology, ... -
Exploring the Inherent Saliency in Visual Data through Convolutional Neural Networks
Saliency plays a key role in various computer vision tasks. Extracting salient regions from images and videos has been a well-established problem in computer vision. Determining salient regions in an image or video has ... -
Numerical Analysis of Some Preconditioners and Associated Error Estimators for Solving Linear Systems
Convergence of iterative algorithms in solving large linear systems is largely affected by the condition number of the matrix. Preconditioners reduce the condition number of the system matrix, thereby letting the linear ... -
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 ... -
Scalability Bottleneck Analysis of High Performance Applications
Obtaining high performance and scalability for high performance applications are challenging. There are various bottlenecks including, higher rate of memory access, complex algorithm, high rate of communication, big ... -
Towards Learning Adversarially Robust Deep Learning Models
Deep learning models have shown impressive performance across a wide spectrum of computer vision applications, including medical diagnosis and autonomous driving. One of the major concerns that these models face is their ... -
Stability Preserving Bisection Algorithms in Reaction-Diffusion Complex Networks
Reaction-Diffusion complex networks are ubiquitous in many pragmatic models of network of interacting nodes with individual dynamics, such as social interactions, neuronal functions, transportation models, ecological ... -
Parallel Smoothers for Multigrid Method in Heterogeneous CPU-GPU Environment
Real-world applications require the solution of large a sparse system of algebraic equations that arise from the discretization of partial di erential equations with the help of supercomputers. Modern supercomputers are ... -
A Hierarchical Control Plane Framework for Integrated SDN-SFC Management in Multi-tenant Cloud Datacenters
Cloud data centers represent one of the most complex and dynamic environments in terms of network management. The multitude of hosted applications in such centers share the same fabric and yet demand easy and fast service ... -
Improving photoacoustic imaging with model compensating and deep learning methods
Photoacoustic imaging is a hybrid biomedical imaging technique combining optical ab- sorption contrast with ultrasonic resolution. It is a non-invasive technique that is scalable to reveal structural, functional, and ...