Dark information of black hole radiation raised by dark energy

The "lost" information of black hole through the Hawking radiation was discovered being stored in the correlation among the non-thermally radiated particles [Phys. Rev. Lett 85, 5042 (2000), Phys. Lett. B 675, 1 (2009)]. This correlation information, which has not yet been proved locally observable in principle, is named by dark information. In this paper, we systematically study the influences of dark energy on black hole radiation, especially on the dark information. Calculating the radiation spectrum in the existence of dark energy by the approach of canonical typicality, which is reconfirmed by the quantum tunneling method, we find that the dark energy will effectively lower the Hawking temperature, and thus makes the black hole has longer life time. It is also discovered that the non-thermal effect of the black hole radiation is enhanced by dark energy so that the dark information of the radiation is increased. Our observation shows that, besides the mechanical effect (e.g., gravitational lensing effect), the dark energy rises the the stored dark information, which could be probed by a non-local coincidence measurement similar to the coincidence counting of the Hanbury-Brown -Twiss experiment in quantum optics.Comment: 21 pages, 3 figures, complete journal-info of Ref.[4] is added, comments are welcome ([email protected]

Neutrino Masses via the Zee Mechanism in 5D split fermions model

We study the Zee model in the framework of the split fermion model in $M_4\times S_1/Z_2$ spacetime. Neutrino masses are generated through 1-loop diagrams without the right-handed neutrinos introduced. By assuming an order one anarchical complex 5D Yukawa couplings, all the effective 4D Yukawa couplings are determined by the wave function overlap between the split fermions and the bulk scalars in the fifth dimension. The predictability of the Yukawa couplings is in sharp contrast to the original Zee model in 4D where the Yukawa couplings are unknown free parameters. This setup exhibits a geometrical alternative to the lepton flavor symmetry. By giving four explicit sets of the split fermion locations, we demonstrate that it is possible to simultaneously fit the lepton masses and neutrino oscillation data by just a handful free parameters without much fine tuning. Moreover, we are able to make definite predictions for the mixing angle $\theta_{13}$, the absolute neutrino masses, and the lepton flavor violation processes for each configuration.Comment: 16 pages, 2 figure

Rapid (<5 min) identification of pathogen in human blood by electrokinetic concentration and surface-enhanced Raman spectroscopy.

This study reports a novel microfluidic platform for rapid and long-ranged concentration of rare-pathogen from human blood for subsequent on-chip surface-enhanced Raman spectroscopy (SERS) identification/discrimination of bacteria based on their detected fingerprints. Using a hybrid electrokinetic mechanism, bacteria can be concentrated at the stagnation area on the SERS-active roughened electrode, while blood cells were excluded away from this region at the center of concentric circular electrodes. This electrokinetic approach performs isolation and concentration of bacteria in about three minutes; the density factor is increased approximately a thousand fold in a local area of ~5000 μm(2) from a low bacteria concentration of 5 × 10(3) CFU/ml. Besides, three genera of bacteria, S. aureus, E. coli, and P. aeruginosa that are found in most of the isolated infections in bacteremia were successfully identified in less than one minute on-chip without the use of any antibody/chemical immobilization and reaction processes

Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networks

We propose a novel framework called Semantics-Preserving Adversarial Embedding Network (SP-AEN) for zero-shot visual recognition (ZSL), where test images and their classes are both unseen during training. SP-AEN aims to tackle the inherent problem --- semantic loss --- in the prevailing family of embedding-based ZSL, where some semantics would be discarded during training if they are non-discriminative for training classes, but could become critical for recognizing test classes. Specifically, SP-AEN prevents the semantic loss by introducing an independent visual-to-semantic space embedder which disentangles the semantic space into two subspaces for the two arguably conflicting objectives: classification and reconstruction. Through adversarial learning of the two subspaces, SP-AEN can transfer the semantics from the reconstructive subspace to the discriminative one, accomplishing the improved zero-shot recognition of unseen classes. Comparing with prior works, SP-AEN can not only improve classification but also generate photo-realistic images, demonstrating the effectiveness of semantic preservation. On four popular benchmarks: CUB, AWA, SUN and aPY, SP-AEN considerably outperforms other state-of-the-art methods by an absolute performance difference of 12.2\%, 9.3\%, 4.0\%, and 3.6\% in terms of harmonic mean value