Magnetized stars with differential rotation and a differential toroidal field

We have succeeded in obtaining magnetized equilibrium states with differential rotation and differential toroidal magnetic fields. If an internal toroidal field of a proto-neutron star is wound up from the initial poloidal magnetic field by differential rotation, the distribution of the toroidal magnetic field is determined by the profile of this differential rotation. However, the distributions of the toroidal fields in all previous magnetized equilibrium studies do not represent the magnetic winding by the differential rotation of the star. In this paper, we investigate a formulation of a differential toroidal magnetic field that represents the magnetic field wound up by differential rotation. We have developed two functional forms of differential toroidal fields which correspond to a v-constant and a j-constant field in analogy to differential rotations. As the degree of the differential becomes very high, the toroidal magnetic field becomes highly localized and concentrated near the rotational axis. Such a differential toroidal magnetic field would suppress the low-T/|W| instability more efficiently even if the total magnetic field energy is much smaller than that of a non-differential toroidal magnetic field.Comment: 10 pages, 4 figures; published in MNRA

Polarizations on limiting mixed Hodge structures

We construct a polarization on the relative log de Rham cohomology groups of a projective log deformation. To this end, we study the behavior of weight and Hodge filtrations under the cup product and construct a trace morphism for a log deformation.Comment: 47 pages, corrections and improvement

Inelastic tunneling in a double quantum dot coupled to a bosonic environment

Coupling a quantum system to a bosonic environment always give rise to inelastic processes, which reduce the coherency of the system. We measure energy dependent rates for inelastic tunneling processes in a fully controllable two-level system of a double quantum dot. The emission and absorption rates are well repro-duced by Einstein's coefficients, which relate to the spontaneous emission rate. The inelastic tunneling rate can be comparable to the elastic tunneling rate if the boson occupation number becomes large. In the specific semiconductor double dot, the energy dependence of the inelastic rate suggests that acoustic phonons are coupled to the double dot piezoelectrically.Comment: 6 pages, 4 figure

Robust and Sparse Regression via γ\gamma-divergence

In high-dimensional data, many sparse regression methods have been proposed. However, they may not be robust against outliers. Recently, the use of density power weight has been studied for robust parameter estimation and the corresponding divergences have been discussed. One of such divergences is the $\gamma$-divergence and the robust estimator using the $\gamma$-divergence is known for having a strong robustness. In this paper, we consider the robust and sparse regression based on $\gamma$-divergence. We extend the $\gamma$-divergence to the regression problem and show that it has a strong robustness under heavy contamination even when outliers are heterogeneous. The loss function is constructed by an empirical estimate of the $\gamma$-divergence with sparse regularization and the parameter estimate is defined as the minimizer of the loss function. To obtain the robust and sparse estimate, we propose an efficient update algorithm which has a monotone decreasing property of the loss function. Particularly, we discuss a linear regression problem with $L_1$ regularization in detail. In numerical experiments and real data analyses, we see that the proposed method outperforms past robust and sparse methods.Comment: 25 page

Robust Estimation under Heavy Contamination using Enlarged Models

In data analysis, contamination caused by outliers is inevitable, and robust statistical methods are strongly demanded. In this paper, our concern is to develop a new approach for robust data analysis based on scoring rules. The scoring rule is a discrepancy measure to assess the quality of probabilistic forecasts. We propose a simple way of estimating not only the parameter in the statistical model but also the contamination ratio of outliers. Estimating the contamination ratio is important, since one can detect outliers out of the training samples based on the estimated contamination ratio. For this purpose, we use scoring rules with an extended statistical models, that is called the enlarged models. Also, the regression problems are considered. We study a complex heterogeneous contamination, in which the contamination ratio of outliers in the dependent variable may depend on the independent variable. We propose a simple method to obtain a robust regression estimator under heterogeneous contamination. In addition, we show that our method provides also an estimator of the expected contamination ratio that is available to detect the outliers out of training samples. Numerical experiments demonstrate the effectiveness of our methods compared to the conventional estimators.Comment: 32 pages, 3 figures, 3 table