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The basic unit of meaning on the Semantic Web is the RDF statement, or triple, which combines a distinct subject, predicate and object to make a definite assertion about the world. A set of triples constitutes a graph, to which they give a collective meaning. It is upon this simple foundation that the rich, complex knowledge structures of the Semantic Web are built. Yet the very expressiveness of RDF, by inviting comparison with real-world knowledge, highlights a fundamental shortcoming, in that RDF is limited to statements of absolute fact, independent of the context in which a statement is asserted. This is in stark contrast with the thoroughly context-sensitive nature of human thought. The model presented here provides a particularly simple means of contextualizing an RDF triple by associating it with related statements in the same graph. This approach, in combination with a notion of graph similarity, is sufficient to select only those statements from an RDF graph which are subjectively most relevant to the context of the requesting process.
The quantum mechanical brachistochrone system with PT-symmetric Hamiltonian is Naimark dilated and reinterpreted as subsystem of a Hermitian system in a higher-dimensional Hilbert space. This opens a way to a direct experimental implementation of the
To effectively realize a $cal PT$-symmetric system, one can dilate a $cal PT$-symmetric Hamiltonian to some global Hermitian one and simulate its evolution in the dilated Hermitian system. However, with only a global Hermitian Hamiltonian, how do we
We propose a system that uses a convolution neural network (CNN) to estimate depth from a stereo pair followed by volumetric fusion of the predicted depth maps to produce a 3D reconstruction of a scene. Our proposed depth refinement architecture, pre
The computation of Kronecker coefficients is a challenging problem with a variety of applications. In this paper we present an approach based on methods from symplectic geometry and residue calculus. We outline a general algorithm for the problem and
Introducing explicit constraints on the structural predictions has been an effective way to improve the performance of semantic segmentation models. Existing methods are mainly based on insufficient hand-crafted rules that only partially capture the