On Objective Measures of Rule Surprisingness

Most of the literature argues that surprisingness is an inherently subjective aspect of the discovered knowledge, which cannot be measured in objective terms. This paper departs from this view, and it has a twofold goal: (1) showing that it is indeed possible to define objective (rather than subjective) measures of discovered rule surprisingness; (2) proposing new ideas and methods for defining objective rule surprisingness measures

Measuring Dark Matter Profiles Non-Parametrically In Dwarf Spheroidals: An Application To Draco

We introduce a novel implementation of orbit-based (or Schwarzschild) modeling that allows dark matter density profiles to be calculated non-parametrically in nearby galaxies. Our models require no assumptions to be made about velocity anisotropy or the dark matter profile. The technique can be applied to any dispersion-supported stellar system, and we demonstrate its use by studying the Local Group dwarf spheroidal galaxy (dSph) Draco. We use existing kinematic data at larger radii and also present 12 new radial velocities within the central 13 pc obtained with the VIRUS-W integral field spectrograph on the 2.7 m telescope at McDonald Observatory. Our non-parametric Schwarzschild models find strong evidence that the dark matter profile in Draco is cuspy for 20 = 20 pc is well fit by a power law with slope alpha = -1.0 +/- 0.2, consistent with predictions from cold dark matter simulations. Our models confirm that, despite its low baryon content relative to other dSphs, Draco lives in a massive halo.NSF-0908639Astronom

Dynamic critical phenomena in the AdS/CFT duality

In critical phenomena, singular behaviors arise not only for thermodynamic quantities but also for transport coefficients. We study this dynamic critical phenomenon in the AdS/CFT duality. We consider black holes with a single R-charge in various dimensions and compute the R-charge diffusion in the linear perturbations. In this case, the black holes belong to model B according to the classification of Hohenberg and Halperin.Comment: 17 pages, ReVTeX4; v2: added references and discussio

The relative tax burden of medium-sized corporations in Germany

Statistical offices do not provide sufficiently disaggregated tax statistics for calculating the relative tax burden of SMEs. We estimate the respective average and median tax burden of small, medium-sized and big corporations in Germany for the period 1998 to 2007 using enterprises micro panel data by applying OLS and quantile regression techniques. We find that the average tax burden levied on profit over the ten years was about 24%, and thus lower than forward-looking techniques suggest. The majority of small corporations did bear a significantly lower burden than the residual bigger corporations. We also provide evidence that medium-sized corporations faced a significantly higher median tax burden than big corporation. This implies an inverse U-shaped trajectory of median tax burden with respect to size of enterprise. Presumably big corporations are internationally operating and hence have more opportunities to manipulate the tax base. Hence, medium-sized corporations seem to have been disadvantaged to big corporations within the German corporation tax. Finally, the size of tax relief provided by the “Tax Reform 2000” was correlated positively with size of enterprise. This size-dependent tax burden identifies a so far neglected type of tax distortion. Future tax reforms hence also have to address size neutrality.Steuerlastmessung; KMU; Steuerreform; Umsatzneutralität

The kernel Kalman rule: efficient nonparametric inference with recursive least squares

Nonparametric inference techniques provide promising tools for probabilistic reasoning in high-dimensional nonlinear systems. Most of these techniques embed distributions into reproducing kernel Hilbert spaces (RKHS) and rely on the kernel Bayes’ rule (KBR) to manipulate the embeddings. However, the computational demands of the KBR scale poorly with the number of samples and the KBR often suffers from numerical instabilities. In this paper, we present the kernel Kalman rule (KKR) as an alternative to the KBR. The derivation of the KKR is based on recursive least squares, inspired by the derivation of the Kalman innovation update. We apply the KKR to filtering tasks where we use RKHS embeddings to represent the belief state, resulting in the kernel Kalman filter (KKF). We show on a nonlinear state estimation task with high dimensional observations that our approach provides a significantly improved estimation accuracy while the computational demands are significantly decreased

31P-NMR spectroscopy of phosphate compartmentation during ischaemia in hearts protected by cardioplegic treatment.

Four tissue compartments, differing in proton and inorganic phosphate concentration, were resolved by 31P-NMR spectroscopy in samples from dog hearts after cardioplegic treatment with HTK solution. Inversion of the physiological cytoplasmic-mitochondrial pH gradient was observed. The considerable ensuing acidosis of the matrix is discussed with regard to a possible delocalisation of ferrous ions