The problem of feature selection is critical in several areas of machine learning and data analysis such as, for example, cancer classification using gene expression data, text categorization, etc. In this work, we consider ...

Software components expose Application Programming Interfaces (APIs) as a means to access their functionality, and facilitate reuse. Developers use APIs supplied by programming languages to access the core data structures ...

The emergence of the exascale era necessitates development of new techniques to efficiently perform high-performance scientific simulations, online data analysis and on-the-fly visualization. Critical applications like ...

Missense mutations account for more than 50% of the mutations known to be involved in human inherited diseases. Missense classification is a challenging task that involves sequencing of the genome, identifying the variations, ...

Ranking a set of candidates or items from pair-wise comparisons is a fundamental problem that arises in many settings such as elections, recommendation systems, sports team rankings, document rankings and so on. Indeed it ...

This thesis introduces a new mathematical object: collection of arithmetic progressions with elements satisfying the inverse property, \j-th terms of i-th and (i+1)-th progressions are multiplicative inverses of each other ...

Stochastic approximation algorithms encompass a class of iterative schemes that converge to a sought value through a series of successive approximations. Such algorithms converge even when the observations are erroneous. ...

Concurrent programs are pervasive owing to the increasing adoption of multi-core systems across the entire computing spectrum. However, the large set of possible program behaviors make it difficult to write correct and ...

A program slice consists of a subset of the statements of a program that can potentially affect values computed at some point of interest. Such a point of interest along with a set of variables is called a slicing criterion. ...

Scalar fields are used to represent physical quantities measured over a domain of interest. Study of symmetric or repeating patterns in scalar fields is important in scientific data analysis because it gives deep insights ...