Locally Accessible Information from Multipartite Ensembles

Locally Accessible Information and Distillation of Entanglement

The problem of local distinguishability of orthogonal quantum states has raised much interest in the arena of quantum information. Interestingly where any two pure orthogonal states can be distinguished locally [1], there exists more than two orthogonal states which can not be distinguished by local operations and classical communication (LOCC) [2, 3]. All these results lead to investigate the ...

Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes – Supporting Information

High-speed Internet growth and the demand for locally accessible information content

Proximity to information resources has repeatedly been shown to affect urban development. However, individuals’ increased abilities to access information content electronically may have dampened urban areas’ comparative advantage of proximity-driven knowledge flows. We investigate the effects of increased high-speed Internet access on the role of information proximity by modeling changes in the...

Accenting Accessible Information

In a perception experiment in German, subjects judged the appropriateness of three types of nuclear pitch accent (including deaccentuation) on non-pronominal anaphoric referring expressions, which were either textually or inferentially accessible from the preceding context. Results confirm that accessible information can indeed be accented – and in some cases must be. However, not all accents a...

Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes.

In artificial neural networks, learning from data is a computationally demanding task in which a large number of connection weights are iteratively tuned through stochastic-gradient-based heuristic processes over a cost function. It is not well understood how learning occurs in these systems, in particular how they avoid getting trapped in configurations with poor computational performance. Her...