Model-based learning of local image features for unsupervised texture segmentation

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images

Soiling and other optical losses in solar-tracking PV plants in Navarra

Field data of soiling energy losses on PV plants are scarce. Furthermore, since dirt type and accumulation vary with the location characteristics (climate, surroundings, etc.), the available data on optical losses are, necessarily, site dependent. This paper presents field measurements of dirt energy losses (dust) and irradiance incidence angle losses along 2005 on a solar-tracking PV plant located south of Navarre (Spain). The paper proposes a method to calculate these losses based on the difference between irradiance measured by calibrated cells on several trackers of the PV plant and irradiance calculated from measurements by two pyranometers (one of them incorporating a shadow ring) regularly cleaned. The equivalent optical energy losses of an installation incorporating fixed horizontal modules at the same location have been calculated as well. The effect of dirt on both types of installations will accordingly be compared

The heat of atomization of sulfur trioxide, SO3_3 - a benchmark for computational thermochemistry

Calibration ab initio (direct coupled cluster) calculations including basis set extrapolation, relativistic effects, inner-shell correlation, and an anharmonic zero-point energy, predict the total atomization energy at 0 K of SO$_3$ to be 335.96 (observed 335.92$\pm$0.19) kcal/mol. Inner polarization functions make very large (40 kcal/mol with $spd$, 10 kcal/mol with $spdfg$ basis sets) contributions to the SCF part of the binding energy. The molecule presents an unusual hurdle for less computationally intensive theoretical thermochemistry methods and is proposed as a benchmark for them. A slight modification of Weizmann-1 (W1) theory is proposed that appears to significantly improve performance for second-row compounds.Comment: Chem. Phys. Lett., in pres

A theorem on the real part of the high-energy scattering amplitude near the forward direction

We show that if for fixed negative (physical) square of the momentum transfer t, the differential cross-section ${d\sigma\over dt}$ tends to zero and if the total cross-section tends to infinity, when the energy goes to infinity, the real part of the even signature amplitude cannot have a constant sign near t = 0.Comment: 7 pages, late

A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution

High-resolution depth maps can be inferred from low-resolution depth measurements and an additional high-resolution intensity image of the same scene. To that end, we introduce a bimodal co-sparse analysis model, which is able to capture the interdependency of registered intensity and depth information. This model is based on the assumption that the co-supports of corresponding bimodal image structures are aligned when computed by a suitable pair of analysis operators. No analytic form of such operators exist and we propose a method for learning them from a set of registered training signals. This learning process is done offline and returns a bimodal analysis operator that is universally applicable to natural scenes. We use this to exploit the bimodal co-sparse analysis model as a prior for solving inverse problems, which leads to an efficient algorithm for depth map super-resolution.Comment: 13 pages, 4 figure

Investigation of the Galactic Magnetic Field with Ultra-High Energy Cosmic Rays

We present a method to correct for deflections of ultra-high energy cosmic rays in the galactic magnetic field. We perform these corrections by simulating the expected arrival directions of protons using a parameterization of the field derived from Faraday rotation and synchrotron emission measurements. To evaluate the method we introduce a simulated astrophysical scenario and two observables designed for testing cosmic ray deflections. We show that protons can be identified by taking advantage of the galactic magnetic field pattern. Consequently, cosmic ray deflection in the galactic field can be verified experimentally. The method also enables searches for directional correlations of cosmic rays with source candidates.Comment: 12 pages, 3 figures, presented at the Eur. Phys. Soc. Conf. on High Energy Physics, Jul. 2015, Vienna, Austria, and the 34th Intern. Cosmic Ray Conf., Jul. 2015, The Hague, The Netherland