Redshift-Distance Survey of Early-Type Galaxies: Spectroscopic Data

We present central velocity dispersions and Mg2 line indices for an all-sky sample of ~1178 elliptical and S0 galaxies, of which 984 had no previous measures. This sample contains the largest set of homogeneous spectroscopic data for a uniform sample of elliptical galaxies in the nearby universe. These galaxies were observed as part of the ENEAR project, designed to study the peculiar motions and internal properties of the local early-type galaxies. Using 523 repeated observations of 317 galaxies obtained during different runs, the data are brought to a common zero point. These multiple observations, taken during the many runs and different instrumental setups employed for this project, are used to derive statistical corrections to the data and are found to be relatively small, typically 5% of the velocity dispersion and 0.01 mag in the Mg2 line strength. Typical errors are about 8% in velocity dispersion and 0.01 mag in Mg2, in good agreement with values published elsewhere

Redshift-Distance Survey of Early-Type Galaxies. IV. Dipoles of the Velocity Field

We use the recently completed redshift-distance survey of nearby early-type galaxies (ENEAR) to measure the dipole component of the peculiar velocity field to a depth of cz ~ 6000 km/s. The sample consists of 1145 galaxies brighter than m_B=14.5 and cz < 7000 km/s, uniformly distributed over the whole sky, and 129 fainter cluster galaxies within the same volume. Most of the Dn-sigma distances were obtained from new spectroscopic and photometric observations conducted by this project, ensuring the homogeneity of the data over the whole sky. These 1274 galaxies are objectively assigned to 696 objects -- 282 groups/clusters and 414 isolated galaxies. We find that within a volume of radius ~ 6000 km/s, the best-fitting bulk flow has an amplitude of |vbulk| =220 +/- 42 km/s in the CMB restframe, pointing towards l=304 +/- 16 degrees, b=25 +/- 11 degrees. The error in the amplitude includes statistical, sampling and possible systematic errors. This solution is in excellent agreement with that obtained by the SFI Tully-Fisher survey. Our results suggest that most of the motion of the Local Group is due to fluctuations within 6000 km/s, in contrast to recent claims of large amplitude bulk motions on larger scales.Comment: 11 pages, 2 figures, ApJL, accepted (updated results; matches accepted version

Two loop results from one loop computations and non perturbative solutions of exact evolution equations

A nonperturbative method is proposed for the approximative solution of the exact evolution equation which describes the scale dependence of the effective average action. It consists of a combination of exact evolution equations for independent couplings with renormalization group improved one loop expressions of secondary couplings. Our method is illustrated by an example: We compute the beta-function of the quartic coupling lambda of an O(N) symmetric scalar field theory to order lambda^3 as well as the anomalous dimension to order lambda^2 using only one loop expressions and find agreement with the two loop perturbation theory. We also treat the case of very strong coupling and confirm the existence of a "triviality bound".Comment: 32 pages, HD-THEP-94-3, replaced because: lines too long, blank line

CNN-based multi-scale hierarchical land use classification for the verification of geospatial databases

Land use is an important piece of information with many applications. Commonly, land use is stored in geospatial databases in the form of polygons with corresponding land use labels and attributes according to an object catalogue. The object catalogues often have a hierarchical structure, with the level of detail of the semantic information depending on the hierarchy level. In this paper, we extend our prior work for the CNN (Convolutional Neural Network)-based prediction of land use for database objects at multiple semantic levels corresponding to different levels of a hierarchical class catalogue. The main goal is the improvement of the classification accuracy for small database objects, which we observed to be one of the largest problems of the existing method. In order to classify large objects using a CNN of a fixed input size, they are split into tiles that are classified independently before fusing the results to a joint prediction for the object. In this procedure, small objects will only be represented by a single patch, which might even be dominated by the background. To overcome this problem, a multi-scale approach for the classification of small objects is proposed in this paper. Using this approach, such objects are represented by multiple patches at different scales that are presented to the CNN for classification, and the classification results are combined. The new strategy is applied in combination with the earlier tiling-based approach. This method based on an ensemble of the two approaches is tested in two sites located in Germany and improves the classification performance up to +1.8% in overall accuracy and +3.2% in terms of mean F1 score

Critical equation of state from the average action

The scaling form of the critical equation of state is computed for $O(N)$-symmetric models. We employ a method based on an exact flow equation for a coarse grained free energy. A suitable truncation is solved numerically.Comment: Latex, 8 pages, 2 uuencoded figure

Is the mean-field approximation so bad? A simple generalization yelding realistic critical indices for 3D Ising-class systems

Modification of the renormalization-group approach, invoking Stratonovich transformation at each step, is proposed to describe phase transitions in 3D Ising-class systems. The proposed method is closely related to the mean-field approximation. The low-order scheme works well for a wide thermal range, is consistent with a scaling hypothesis and predicts very reasonable values of critical indices.Comment: 4 page

Symmetry Dependence of Localization in Quasi- 1- dimensional Disordered Wires

The crossover in energy level statistics of a quasi-1-dimensional disordered wire as a function of its length L is used, in order to derive its averaged localization length, without magnetic field, in a magnetic field and for moderate spin orbit scattering strength. An analytical function of the magnetic field for the local level spacing is obtained, and found to be in excellent agreement with the magnetic field dependent activation energy, recently measured in low-mobility quasi-one-dimensional wires\cite{khavin}. This formula can be used to extract directly and accurately the localization length from magnetoresistance experiments. In general, the local level spacing is shown to be proportional to the excitation gap of a virtual particle, moving on a compact symmetric space.Comment: 4 pages, 2 Eqs. added, Eperimental Data included in Fig.

Learning multi-modal features for dense matching-based confidence estimation

In recent years, the ability to assess the uncertainty of depth estimates in the context of dense stereo matching has received increased attention due to its potential to detect erroneous estimates. Especially, the introduction of deep learning approaches greatly improved general performance, with feature extraction from multiple modalities proving to be highly advantageous due to the unique and different characteristics of each modality. However, most work in the literature focuses on using only mono- or bi- or rarely tri-modal input, not considering the potential effectiveness of modalities, going beyond tri-modality. To further advance the idea of combining different types of features for confidence estimation, in this work, a CNN-based approach is proposed, exploiting uncertainty cues from up to four modalities. For this purpose, a state-of-the-art local-global approach is used as baseline and extended accordingly. Additionally, a novel disparity-based modality named warped difference is presented to support uncertainty estimation at common failure cases of dense stereo matching. The general validity and improved performance of the proposed approach is demonstrated and compared against the bi-modal baseline in an evaluation on three datasets using two common dense stereo matching techniques