Age Determination Method of Pre-Main Sequence Stars with High-Resolution I-Band Spectroscopy

We present a new method for determining the age of late-K type pre-main sequence (PMS) stars by deriving their surface gravity from high-resolution I-band spectroscopy. Since PMS stars contract as they evolve, age can be estimated from surface gravity. We used the equivalent width ratio (EWR) of nearby absorption lines to create a surface gravity diagnostic of PMS stars that is free of uncertainties due to veiling. The ratios of Fe (818.67nm and 820.49nm) and Na (818.33nm and 819.48nm) absorption lines were calculated for giants, main-sequence stars, and weak-line T Tauri stars. Effective temperatures were nearly equal across the sample. The Fe to Na EWR (Fe/Na) decreases significantly with increasing surface gravity, denoting that Fe/Na is a desirable diagnostic for deriving the surface gravity of pre-main sequence stars. The surface gravity of PMS stars with 0.8 solar mass is able to be determined with an accuracy of 0.1-0.2, which conducts the age of PMS stars within a factor of 1.5, in average.Comment: 16 pages, 5 figures, 3 tables, accepted for publication in PAS

Carrier concentrations in Bi_{2}Sr_{2-z}La_{z}CuO_{6+\delta} single crystals and their relation to Hall coefficient and thermopower

We measured the thermopower S and the Hall coefficients R_H of Bi_{2}Sr_{2-z}La_{z}CuO_{6+\delta} (BSLCO) single crystals in a wide doping range, in an effort to identify the actual hole concentrations per Cu, p, in this system. It is found that the "universal" relation between the room-temperature thermopower and T_c does not hold in the BSLCO system. Instead, comparison of the temperature-dependent R_H data with other cuprate systems is used as a tool to identify the actual p value. To justify this approach, we compare normalized R_H(T) data of BSLCO, La_{2-x}Sr_{x}CuO_{4} (LSCO), YBa_{2}Cu_{3}O_{y}, and Tl_{2}Ba_{2}CuO_{6+\delta}, and demonstrate that the R_H(T) data of the LSCO system can be used as a template for the estimation of p. The resulting phase diagram of p vs T_c for BSLCO suggests that T_c is anomalously suppressed in the underdoped samples, becoming zero at around p ~ 0.10, while the optimum T_c is achieved at p ~ 0.16 as expected.Comment: 4 pages including 5 figures, accepted for publication in Phys. Rev. B, Rapid Communication

Thermodynamic and transport properties of underdoped cuprates from ARPES data

he relationship between photoemission spectra of high-$T_{\textrm{c}}$ cuprates and their thermodynamic and transport properties are discussed. The doping dependence of the expected quasi-particle density at the Fermi level ($E_\mathrm{F}$) are compared with the electronic specific heat coefficient $\gamma$ and that of the spectral weight at $E_\mathrm{F}$ with the in-plane and out-of-plane superfluid density. We have estimated the electrical resistivity of underdoped cuprates from the momentum distribution curve (MDC) at $E_\mathrm{F}$ in the nodal direction. The temperature dependence of the MDC width is also consistent with that of the electrical resistivity.Comment: 14 pages, 4 figures, proceeding of International Symposium on Synchrotron Radiatin Research for Spin and Electronic States in d and f Electron Systems(SRSES2003

Survey on the Family of the Recursive-Rule Extraction Algorithm

In this paper, we first review the theoretical and historical backgrounds on rule extraction from neural network ensembles. Because the structures of previous neural network ensembles were quite complicated, research on an efficient rule extraction algorithm from neural network ensembles has been sparse, even though a practical need exists for rule extraction in Big Data datasets. We describe the Recursive-Rule extraction (Re-RX) algorithm, which is an important step toward handling large datasets. Then we survey the family of the Recursive-Rule extraction algorithm, i.e. the Multiple-MLP Ensemble Re-RX algorithm, and present concrete applications in financial and medical domains that require extremely high accuracy for classification rules. Finally, we mention two promising ideas to considerably enhance the accuracy of the Multiple-MLP Ensemble Re-RX algorithm. We also discuss developments in the near future that will make the Multiple-MLP Ensemble Re-RX algorithm much more accurate, concise, and comprehensible rule extraction from mixed datasets