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Causal consistency is one of the most adopted consistency criteria for distributed implementations of data structures. It ensures that operations are executed at all sites according to their causal precedence. We address the issue of verifying automaticall ...
Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the c ...
Holant is a framework of counting characterized by local constraints. It is closely related to other well-studied frameworks such as the counting constraint satisfaction problem (#CSP) and graph homomorphism. An effective dichotomy for such frameworks can ...
Decision making and planning with partial state information is a problem faced by all forms of intelligent entities. The formulation of a problem under partial state information leads to an exorbitant set of choices with associated probabilistic outcomes m ...
Caching at edge servers can smooth the temporal traffic variability and reduce the service load of base stations in wireless streaming. However, the assignment of the cached content for possibly multiple versions of different video sequences is still a cha ...
This thesis focuses on the development of robust control solutions for linear time-invariant interconnected systems affected by polytopic-type uncertainty. The main issues involved in the control of such systems, e.g. sensor and actuator placement, control ...
The atomic distribution in crystal structures becomes very complex if atoms are disordered and randomly distributed over positions not being fully occupied. Interatomic distances between neighboring atoms might be too close for simultaneous occupancies and ...
In multiview applications, camera views can be used as reference views to synthesize additional virtual viewpoints, allowing users to freely navigate within a 3D scene. However, bandwidth constraints may restrict the number of reference views sent to clien ...
Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main promise of these models is the recovery of ``interpretable" signals along with ...
Institute of Electrical and Electronics Engineers2016
The most basic form of the max-sum dispersion problem (MSD) is as follows: given n points in R^q and an integer k, select a set of k points such that the sum of the pairwise distances within the set is maximal. This is a prominent diversity problem, with w ...