Quantum computing has generated a lot of interested in the past two decades. Research into powerful models of quantum computation has yielded important and elegant results like an efficient algorithm for factoring and a ...

The boxicity of a graph G, denoted as box(G), is the minimum dimension d for which each vertex of G can be mapped to a d-dimensional axis-parallel box in Rd such that two boxes intersect if and only if the corresponding ...

Modern architectures support multiple size pages to facilitate applications that use large chunks of contiguous memory either for buffer allocation, application specific memory management, in-memory caching or garbage ...

Graph Neural Networks (GNNs) have demonstrated exceptional performance across a wide range of applications, driving their widespread adoption. Current frameworks employ CPU and GPU resources—either in isolation or ...

We provide two static analysis approaches(using theorem proving) that check if a given (recursive) procedure behaves as if it were stateless, even when it maintains state in global variables. In other words, we check if ...

The problem of identifying clusters arising in the context of topic models and related approaches is important in the area of machine learning. The problem concerning traversals on Concept Association Networks is of great ...

The thesis started with the early history and development of the theory of determinants. All the important results up to the 1880s were presented through a computing perspective. Pfaffians are closely related to determinants; ...

Measuring scientific processes result in a set of scalar functions (scalar fields) which may be related temporally, be part of an ensemble, or unrelated. Overall understanding and visualization of scientific processes ...

A recent focus in cognitive and brain sciences is to construct models for memory starting with assumptions that are neurophysiologically plausible. The behavior of the model should fit the empirical data. In this thesis, ...