Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
We put forward a novel paradigm for preserving privacy in data outsourcing which departs from encryption. The basic idea behind our proposal is to involve the owner in storing a li...
Valentina Ciriani, Sabrina De Capitani di Vimercat...
In this paper, we propose a hierarchical timing-driven Steiner tree algorithm for global routing which considers the minimization of timing delay during the tree construction as t...
We address the problem of performance and power-efficient thread allocation in a CMP. To that end, based on analytical model, we introduce a parameterized performance/power metric ...
Identifying a minimal unsatisfiable core in an Alloy model proved to be a very useful feature in many scenarios. We extend this concept to hot core, an approximation to unsat core...