Vipin Kumar

TL;DR: This work shows through simulation that new policies based on resource balancing perform consistently better than the classical load distribution strategies.

TL;DR: It is shown that a PGRNN can improve prediction accuracy over that of physics-based models, while generating outputs consistent with physical laws, and is applicable more widely to a range of scientific and engineering disciplines where physics- based models are used.

TL;DR: This work presents a new formulation of the multi-objective graph partitioning problem and describes an algorithm that computes partitionings with respect to this formulation and explains how this algorithm provides the user with a fine-tuned control of the tradeoffs among the objectives, results in predictable partitionings, and is able to handle both similar and dissimilar objectives.

TL;DR: A data mining approach was developed to exhaustively discover all block structures within Saccharomyces cerevisiae's genetic interaction network, which allowed for its complete modular decomposition and revealed the importance of the context of individual genetic interactions in their interpretation and revealed distinct trends among genetic interaction hubs.

TL;DR: Intended for students and researchers actively involved in parallel algorithms design and in machine intelligence and vision, this book will serve as a valuable reference work as well as an introduction to several research directions in these areas.