Emergent bubbling geometries in the plane wave matrix model

The gravity dual geometry of the plane wave matrix model is given by the bubbling geometry in the type IIA supergravity, which is described by an axially symmetric electrostatic system. We study a quarter BPS sector of the plane wave matrix model in terms of the localization method and show that this sector can be mapped to a one-dimensional interacting Fermi gas system. We find that the mean-field density of the Fermi gas can be identified with the charge density in the electrostatic system in the gravity side. We also find that the scaling limits in which the dual geometry reduces to the D2-brane or NS5-brane geometry are given as the free limit or the strongly coupled limit of the Fermi gas system, respectively. We reproduce the radii of $S^5$'s in these geometries by solving the Fermi gas model in the corresponding limits.Comment: 34 pages, 3 figures; typos correcte

Unusual Carbonaceous Dust Distribution in PN G095.2+00.7

We investigate the polycyclic aromatic hydrocarbon features in the young Galactic planetary nebula PN G095.2+00.7 based on mid-infrared observations. The near- to mid-infrared spectra obtained with the AKARI/IRC and the Spitzer/IRS show the PAH features as well as the broad emission feature at 12 {\mu}m usually seen in proto-planetary nebulae (pPNe). The spatially resolved spectra obtained with Subaru/COMICS suggest that the broad emission around 12 {\mu}m is distributed in a shell-like structure, but the unidentified infrared band at 11.3 {\mu}m is selectively enhanced at the southern part of the nebula. The variation can be explained by a difference in the amount of the UV radiation to excite PAHs, and does not necessarily require the chemical processing of dust grains and PAHs. It suggests that the UV self-extinction is important to understand the mid-infrared spectral features. We propose a mechanism which accounts for the evolutionary sequence of the mid-infrared dust features seen in a transition from pPNe to PNe.Comment: 6 pages, 4 figure

Mental deterioration in childhood epilepsy

Mental retardation is detected in 20-30% of children with epilepsy at hospitals specializing in treatment of childhood epilepsy. However, the incidence of mental deterioration in childhood epilepsy is not high. In this study, mental deterioration was found in 52 (1.8%) of the 2,880 children with epilepsy at Okayama University Hospital. The patients showing mental deterioration mostly suffered from specific epileptic syndromes, such as West syndrome, Lennox-Gastaut syndrome, severe myoclonic epilepsy in infancy and epilepsy with continuous spike-waves during slow wave sleep. These types of epilepsy show generalized electroencephalographic (EEG) abnormalities. It is presumed that mental deterioration is caused by the total effects of prolonged diffuse EEG abnormalities and the age of the patients. Antiepileptic drugs exert a relatively minor effect on mental deterioration.</p

Fabrication and characterization of an L3 nanocavity designed by an iterative machine-learning method

Optical nanocavities formed by defects in a two-dimensional photonic crystal (PC) slab can simultaneously realize a very small modal volume and an ultrahigh quality factor (Q). Therefore, such nanocavities are expected to be useful for the enhancement of light-matter interaction and slowdown of light in devices. In the past, it was difficult to design a PC hole pattern that makes sufficient use of the high degree of structural freedom of this type of optical nanocavity, but very recently, an iterative optimization method based on machine learning was proposed that efficiently explores a wide parameter space. Here, we fabricate and characterize an L3 nanocavity that was designed by using this method and has a theoretical Q value of 29 x 10(6) and a modal volume of 0.7 cubic wavelength in the material. The highest unloaded Q value of the fabricated cavities is 4.3 x 10(6); this value significantly exceeds those reported previously for an L3 cavity, i.e., approximate to 2.1 x 10(6). The experimental result shows that the iterative optimization method based on machine learning is effective in improving cavity Q values