Sudden Death of Entanglement: Classical Noise Effects

When a composite quantum state interacts with its surroundings, both quantum coherence of individual particles and quantum entanglement will decay. We have shown that under vacuum noise, i.e., during spontaneous emission, two-qubit entanglement may terminate abruptly in a finite time [T. Yu and J. H. Eberly, \prl {93}, 140404 (2004)], a phenomenon termed entanglement sudden death (ESD). An open issue is the behavior of mixed-state entanglement under the influence of classical noise. In this paper we investigate entanglement sudden death as it arises from the influence of classical phase noise on two qubits that are initially entangled but have no further mutual interaction.Comment: 5 pages, 1 figur

Erratum: First-principles study on the intrinsic stability of the magic Fe13O8 Cluster [Phys. Rev. B 61, 5781 (2000)]

See Also: Original Article: Q. Sun, Q. Wang, K. Parlinski, J. Z. Yu, Y. Hashi, X. G. Gong, and Y. Kawazoe, First-principles studies on the intrinsic stability of the magic Fe13O8 cluster, Phys. Rev. B 61, 5781 (2000)

Bilateral Hipoglossal Nerve Palsy In Necrotizing Otitis Externa

[No abstract available]734576Benecke Jr., J.A., Management of osteomyelitis of the skull base (1989) Laryngoscope, 99 (12), pp. 1220-1223Boringa, J.B., Hoekstra, O.S., Roos, J.W., Bertelsmann, F.W., Multiple cranial nerve palsy after otitis externa: A case report (1995) Clin Neurol Neurosurg, 97, pp. 332-335Rubin, J., Yu, V.L., Malignant external otitis: Insights into pathogenesis, clinical manifestations and therapy (1998) Am J Med, 85, pp. 391-39

A Double Joint Bayesian Approach for J-Vector Based Text-dependent Speaker Verification

J-vector has been proved to be very effective in text-dependent speaker verification with short-duration speech. However, the current state-of-the-art back-end classifiers, e.g. joint Bayesian model, cannot make full use of such deep features. In this paper, we generalize the standard joint Bayesian approach to model the multi-faceted information in the j-vector explicitly and jointly. In our generalization, the j-vector was modeled as a result derived by a generative Double Joint Bayesian (DoJoBa) model, which contains several kinds of latent variables. With DoJoBa, we are able to explicitly build a model that can combine multiple heterogeneous information from the j-vectors. In verification step, we calculated the likelihood to describe whether the two j-vectors having consistent labels or not. On the public RSR2015 data corpus, the experimental results showed that our approach can achieve 0.02\% EER and 0.02\% EER for impostor wrong and impostor correct cases respectively