TLM (Transaction-Level Modeling) was introduced to cope with the increasing complexity of Systems-on-Chip designs by raising the modeling level. Currently, TLM is primarily used fo...
Partially-observable Markov decision processes (POMDPs) provide a powerful model for sequential decision-making problems with partially-observed state and are known to have (appro...
Relational Markov Decision Processes (MDP) are a useraction for stochastic planning problems since one can develop abstract solutions for them that are independent of domain size ...
We present a method for hierarchical data approximation using curved quadratic simplicial elements for domain decomposition. Scientific data defined over two- or three-dimensional ...
We present a “black-box” approach to estimating query cardinality that has no knowledge of query execution plans and data distribution, yet provides accurate estimates. It doe...