Tokyo Institute of Technology

About: Tokyo Institute of Technology is a education organization based out in Tokyo, Tôkyô, Japan. It is known for research contribution in the topics: Thin film & Catalysis. The organization has 46775 authors who have published 101656 publications receiving 2357893 citations. The organization is also known as: Tokyo Tech & Tokodai.

Measurement of the τ Lepton Polarization and R (D * ) in the Decay B ̄ →D * τ - ν ̄ τ

Abstract: We report the first measurement of the tau lepton polarization P-tau(D*) in the decay (B) over bar -> D* tau(-) (v) over bar (tau) as well as a newmeasurement of the ratio of the branching fractions R(D*) = B((B) over bar -> D* tau(-) (v) over bar (tau)) / B((B) over bar -> D* l(-) (v) over bar (l)), where l(-) denotes an electron or a muon, and the tau is reconstructed in the modes tau(-) -> pi(-) v(tau) and tau(-) -> rho(-) v(tau). We use the full data sample of 772 x 10(6) B (B) over bar pairs recorded with the Belle detector at the (KEKB) over bar electron-positron collider. Our results, P-tau(D*) = -0.38 +/- 0.51 (stat)(-0.16)(+0.21) (syst) and R(D*) = 0.270 +/- 0.035 (stat)(- 0.025)(+0.028) (syst), are consistent with the theoretical predictions of the standard model.

Formulas for the Analysis of the Surface SFG Spectrum and Transformation Coefficients of Cartesian SFG Tensor Components

Abstract: Comprehensive expressions have been presented to facilitate the analysis of the surface sum-frequency generation (SFG) spectrum. The electric field components of the SFG beam for a given experimental setup have been related via appropriately defined Fresnel coefficients to the non-linear source polarization, which in turn has been related to the electric fields of exciting visible and infrared beams through the macroscopic SFG susceptibility tensor. The coefficients of transformation have been given to relate the laboratory-fixed Cartesian components of the SFG tensor to the components described in a surface-fixed axis system. The tensor components have been further related to the components of the microscopic hyperpolarizability tensor of surface species, and the explicit expressions (in terms of the Euler angles defining molecular orientation) of the transformation coefficients are presented to describe the Cartesian tensor components described in a surface-fixed axis system by the molecule-fixed components.

Analysis of Speaker Adaptation Algorithms for HMM-Based Speech Synthesis and a Constrained SMAPLR Adaptation Algorithm

Abstract: In this paper, we analyze the effects of several factors and configuration choices encountered during training and model construction when we want to obtain better and more stable adaptation in HMM-based speech synthesis. We then propose a new adaptation algorithm called constrained structural maximum a posteriori linear regression (CSMAPLR) whose derivation is based on the knowledge obtained in this analysis and on the results of comparing several conventional adaptation algorithms. Here, we investigate six major aspects of the speaker adaptation: initial models; the amount of the training data for the initial models; the transform functions, estimation criteria, and sensitivity of several linear regression adaptation algorithms; and combination algorithms. Analyzing the effect of the initial model, we compare speaker-dependent models, gender-independent models, and the simultaneous use of the gender-dependent models to single use of the gender-dependent models. Analyzing the effect of the transform functions, we compare the transform function for only mean vectors with that for mean vectors and covariance matrices. Analyzing the effect of the estimation criteria, we compare the ML criterion with a robust estimation criterion called structural MAP. We evaluate the sensitivity of several thresholds for the piecewise linear regression algorithms and take up methods combining MAP adaptation with the linear regression algorithms. We incorporate these adaptation algorithms into our speech synthesis system and present several subjective and objective evaluation results showing the utility and effectiveness of these algorithms in speaker adaptation for HMM-based speech synthesis.