Parity-time electromagnetic diodes in a two-dimensional nonreciprocal photonic crystal

We propose a kind of electromagnetic (EM) diode based on a two-dimensional nonreciprocal gyrotropic photonic crystal. This periodic microstructure has separately broken symmetries in both parity (P) and time-reversal (T) but obeys parity-time (PT) symmetry. This kind of diode could support bulk one-way propagating modes either for group velocity or phase velocity with various types of negative and positive refraction. This symmetry-broken system could be a platform to realize abnormal photoelectronic devices, and it may be analogous to an electron counterpart with one-way features

Full counting statistics of renormalized dynamics in open quantum transport system

The internal dynamics of a double quantum dot system is renormalized due to coupling respectively with transport electrodes and a dissipative heat bath. Their essential differences are identified unambiguously in the context of full counting statistics. The electrode coupling caused level detuning renormalization gives rise to a fast-to-slow transport mechanism, which is not resolved at all in the average current, but revealed uniquely by pronounced super-Poissonian shot noise and skewness. The heat bath coupling introduces an interdot coupling renormalization, which results in asymmetric Fano factor and an intriguing change of line shape in the skewness.Comment: 9 pages, 5 figure

Context Modeling for Ranking and Tagging Bursty Features in Text Streams

Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without taking into account the semantic contexts of terms, and as a result the detected bursty features may not always be interesting or easy to interpret. In this paper we propose to model the contexts of bursty features using a language modeling approach. We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty features. Using a large corpus of a stream of news articles, we quantitatively show that the proposed context language models for bursty features can effectively help rank bursty features based on their newsworthiness and to assign meaningful tags to annotate bursty features. ? 2010 ACM.EI