813 research outputs found
Phase diagram of microcavity exciton-polariton condensates
In this work, we study the exciton-polariton condensate phase transition in a
microcavity matter-light system in which electron-hole Coulomb interaction and
matter-light coupling effects are treated on an equal footing. In the framework
of the unrestricted Hartree-Fock approximation applying the two-dimensional
exciton-polariton model, we derive the self-consistent equations determining
simultaneously the excitonic and the photonic condenstate order parameters. In
the thermal equilibrium limit, we find a condensed state of the
exciton-polariton systems and phase diagrams are then constructed. At a given
low temperature, the condensate by its nature shows a crossover from an
excitonic to a polaritonic and finally photonic condensed state as the
excitation density increases at large detuning. Without the detuning, the
excitonic condensed state disappears whereas the polaritonic or photonic phases
dominate. The crossover is also found by lowering the Coulomb interaction at a
finite matter-light coupling. Lowering the Coulomb interaction or increasing
the temperature, the excitonic Mott transition occurs, at which the
exciton-polariton condensates dissociate to free electron-hole/photon.
Depending on temperature and excitation density, the phase transition of the
exciton-polariton condensates is also addressed in signatures of
photoluminescence mapping to the photonic momentum distribution.Comment: 7 pages, 6 figure
Quantification of Data Needs, Data Collection and
Further to the development of a model analysis fr
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
We present a Bayesian nonparametric framework for multilevel clustering which
utilizes group-level context information to simultaneously discover
low-dimensional structures of the group contents and partitions groups into
clusters. Using the Dirichlet process as the building block, our model
constructs a product base-measure with a nested structure to accommodate
content and context observations at multiple levels. The proposed model
possesses properties that link the nested Dirichlet processes (nDP) and the
Dirichlet process mixture models (DPM) in an interesting way: integrating out
all contents results in the DPM over contexts, whereas integrating out
group-specific contexts results in the nDP mixture over content variables. We
provide a Polya-urn view of the model and an efficient collapsed Gibbs
inference procedure. Extensive experiments on real-world datasets demonstrate
the advantage of utilizing context information via our model in both text and
image domains.Comment: Full version of ICML 201
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