Hypergraph partitioning lies at the heart of a number of problems in machine learning as well as other engineering disciplines. While partitioning uniform hypergraphs is often required in computer vision problems that ...

Test power, data volume, and test time have been long-standing problems for sequential scan based testing of system-on-chip (SoC) design. The modern SoCs fabricated at lower technology nodes are complex in nature, the ...

In recent decades, scientific data has become available in increasing sizes and precision. Therefore techniques to analyze and summarize the ever increasing datasets are of vital importance. A common form of scientific ...

In this work, we study two problems: first is one of the central problem in learning theory of learning sparse parities and the other k-Vector-SUM is an extension of the not oriousk-SUM problem. We first consider the problem ...

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 ...

Supervised learning of piecewise linear models is a well studied problem in machine learning community. The key idea in piecewise linear modeling is to properly partition the input space and learn a linear model for every ...

Large flexible software systems tend to incur “bloat”, here defined as the runtime overhead induced by the accumulation of excess functionality and objects. Removing bloat is hard as these overheads are a side-effect of ...

Estimating program worst case execution time (WCET) is an important problem in the domain of real-time systems and embedded systems that are deadline-centric. If WCET of a program is found to exceed the deadline, it is ...

In many optimization problems, the relationship between the objective and parameters is not known. The objective function itself may be stochastic such as a long-run average over some random cost samples. In such cases ...

Microscopic analysis of biological structures can be significantly enhanced by representing the object of study as a three-dimensional entity. To assist neurobiologists investigate the molecular mechanisms involved in ...