Chemical Abundances of the Outer Halo Stars in the Milky Way

We present chemical abundances of 57 metal-poor stars that are likely constituents of the outer stellar halo in the Milky Way. Almost all of the sample stars have an orbit reaching a maximum vertical distance (Z_max) of >5 kpc above and below the Galactic plane. High-resolution, high signal-to-noise spectra for the sample stars obtained with Subaru/HDS are used to derive chemical abundances of Na, Mg, Ca, Ti, Cr, Mn, Fe, Ni, Zn, Y and Ba with an LTE abundance analysis code. The resulting abundance data are combined with those presented in literature that mostly targeted at smaller Z_max stars, and both data are used to investigate any systematic trends in detailed abundance patterns depending on their kinematics. It is shown that, in the metallicity range of -25 kpc are systematically lower (~0.1 dex) than those with smaller Z_max. This result of the lower [alpha/Fe] for the assumed outer halo stars is consistent with previous studies that found a signature of lower [alpha/Fe] ratios for stars with extreme kinematics. A distribution of the [Mg/Fe] ratios for the outer halo stars partly overlaps with that for stars belonging to the Milky Way dwarf satellites in the metallicity interval of -2<[Fe/H]<-1 and spans a range intermediate between the distributions for the inner halo stars and the stars belonging to the satellites. Our results confirm inhomogeneous nature of chemical abundances within the Milky Way stellar halo depending on kinematic properties of constituent stars as suggested by earlier studies. Possible implications for the formation of the Milky Way halo and its relevance to the suggested dual nature of the halo are discussed.Comment: 68 pages with 23 figures, Accepted for publication in PAS

Effects of environment on microhardness of magnesium oxide

Micro-Vickers hardness measurements of magnesium oxide single crystals were conducted in various environments. These environments included air, nitrogen gas, water, mineral oil with or without various additives, and aqueous solutions with various pH values. Indentations were made on the (100) plane with the diagonals of the indentation in the (100) direction. The results indicate that a sulfur containing additve in mineral oil increased hardness, a chlorine containing additive in mineral oil decreased hardness, and aqueous solutions of hydrogen chloride decreased hardness. Other environments were found to have little effect on hardness. Mechanically polished surfaces showed larger indentation creep than did as-cleaved surfaces

Chemical Abundances of the Milky Way Thick Disk and Stellar Halo I.: Implications of [alpha/Fe] for Star Formation Histories in Their Progenitors

We present the abundance analysis of 97 nearby metal-poor (-3.3<[Fe/H]<-0.5) stars having kinematics characteristics of the Milky Way (MW) thick disk, inner, and outer stellar halos. The high-resolution, high-signal-to-noise optical spectra for the sample stars have been obtained with the High Dispersion Spectrograph mounted on the Subaru Telescope. Abundances of Fe, Mg, Si, Ca and Ti have been derived using a one-dimensional LTE abundance analysis code with Kurucz NEWODF model atmospheres. By assigning membership of the sample stars to the thick disk, inner or outer halo components based on their orbital parameters, we examine abundance ratios as a function of [Fe/H] and kinematics for the three subsamples in wide metallicity and orbital parameter ranges. We show that, in the metallicity range of -1.5<[Fe/H]<= -0.5, the thick disk stars show constantly high mean [Mg/Fe] and [Si/Fe] ratios with small scatter. In contrast, the inner, and the outer halo stars show lower mean values of these abundance ratios with larger scatter. The [Mg/Fe], [Si/Fe] and [Ca/Fe] for the inner and the outer halo stars also show weak decreasing trends with [Fe/H] in the range [Fe/H]$>-2$. These results favor the scenarios that the MW thick disk formed through rapid chemical enrichment primarily through Type II supernovae of massive stars, while the stellar halo has formed at least in part via accretion of progenitor stellar systems having been chemically enriched with different timescales.Comment: Accepted for publication in Ap

A Numerical Scheme Based on Semi-Static Hedging Strategy

In the present paper, we introduce a numerical scheme for the price of a barrier option when the price of the underlying follows a diffusion process. The numerical scheme is based on an extension of a static hedging formula of barrier options. For getting the static hedging formula, the underlying process needs to have a symmetry. We introduce a way to "symmetrize" a given diffusion process. Then the pricing of a barrier option is reduced to that of plain options under the symmetrized process. To show how our symmetrization scheme works, we will present some numerical results applying (path-independent) Euler-Maruyama approximation to our scheme, comparing them with the path-dependent Euler-Maruyama scheme when the model is of the Black-Scholes, CEV, Heston, and $(\lambda)$-SABR, respectively. The results show the effectiveness of our scheme

DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning

The increasing reliance upon cloud services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted in a progressive way due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems, since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to dependency among network nodes and layers. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some dependent network layers exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on Deep Reinforcement Learning (Deep RL). Our simulation results indicate that DeepPR can achieve the near-optimal solutions in certain networks and is more robust to adversarial failures, compared to a baseline heuristic algorithm.Comment: Technical Report, 12 page