Scale space consistency of piecewise constant least squares estimators -- another look at the regressogram

We study the asymptotic behavior of piecewise constant least squares regression estimates, when the number of partitions of the estimate is penalized. We show that the estimator is consistent in the relevant metric if the signal is in $L^2([0,1])$, the space of c\`{a}dl\`{a}g functions equipped with the Skorokhod metric or $C([0,1])$ equipped with the supremum metric. Moreover, we consider the family of estimates under a varying smoothing parameter, also called scale space. We prove convergence of the empirical scale space towards its deterministic target.Comment: Published at http://dx.doi.org/10.1214/074921707000000274 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Kondo temperature of magnetic impurities at surfaces

Based on the experimental observation, that only the close vicinity of a magnetic impurity at metal surfaces determines its Kondo behaviour, we introduce a simple model which explains the Kondo temperatures observed for cobalt adatoms at the (111) and (100) surfaces of Cu, Ag, and Au. Excellent agreement between the model and scanning tunneling spectroscopy (STS) experiments is demonstrated. The Kondo temperature is shown to depend on the occupation of the d-level determined by the hybridization between adatom and substrate with a minimum around single occupancy.Comment: 4 pages, 2 figure

3-D Reconstructions and Numerical Simulations of Precarious Rocks in Southern California

Reliable estimates of seismic hazard are essential for the development of resilient communities; however, estimates of rare, yet high intensity earthquakes are highly uncertain due to a lack of observations and recordings. Lacking this data, seismic hazard analyses may be based on extrapolations from earthquakes with more moderate return periods, which can lead to physically unrealistic earthquake scenarios. However, the existence of certain precariously balanced rocks (PBRs) has been identified as an indicator of an upper bound ground motion, which precludes toppling of the balanced rock, over its lifetime. To this end, a survey of PBRs was conducted in proximity to the Elsinore fault east of San Diego, CA. Each identified PBR is modeled using point clouds derived from ground-based laser scanning and images from an unmanned aerial vehicle. The resultant geometric reconstructions are then used in a probabilistic overturning analysis and compared to the anticipated seismic hazard at the site. Accounting for an estimated age range and 50% probability of overturning for the PBRs, approximately half of the surveyed PBRs indicate a potential overestimation of seismic hazard at the site

Delayed presentation of traumatic aortocaval fistula: A report of two cases and a review of the associated compensatory hemodynamic and structural changes

Chronic aortocaval fistula (ACF) is a rare complication of gunshot wounds to the abdomen. Herein we report two cases of traumatic ACF: one asymptomatic and the other presenting with congestive heart failure (CHF) 20 and 30 years, respectively, after their initial injury. The recent onset of CHF, the presence of a continuous abdominal bruit, and, in the second patient, a history of penetrating trauma suggested the diagnosis of ACF. The diagnosis was confirmed by computed tomography scanning in both patients. Surgical repair of the ACF in the symptomatic patient resulted in resolution of the CHF and reversed the dilatation of the aorta and inferior vena cava. The asymptomatic patient was lost to follow-up. CHF in a young male patient with a history of penetrating abdominal trauma should alert the surgeon to this rare complication

Deep domain adaptation by weighted entropy minimization for the classification of aerial images

Fully convolutional neural networks (FCN) are successfully used for the automated pixel-wise classification of aerial images and possibly additional data. However, they require many labelled training samples to perform well. One approach addressing this issue is semi-supervised domain adaptation (SSDA). Here, labelled training samples from a source domain and unlabelled samples from a target domain are used jointly to obtain a target domain classifier, without requiring any labelled samples from the target domain. In this paper, a two-step approach for SSDA is proposed. The first step corresponds to a supervised training on the source domain, making use of strong data augmentation to increase the initial performance on the target domain. Secondly, the model is adapted by entropy minimization using a novel weighting strategy. The approach is evaluated on the basis of five domains, corresponding to five cities. Several training variants and adaptation scenarios are tested, indicating that proper data augmentation can already improve the initial target domain performance significantly resulting in an average overall accuracy of 77.5%. The weighted entropy minimization improves the overall accuracy on the target domains in 19 out of 20 scenarios on average by 1.8%. In all experiments a novel FCN architecture is used that yields results comparable to those of the best-performing models on the ISPRS labelling challenge while having an order of magnitude fewer parameters than commonly used FCNs. © 2020 Copernicus GmbH. All rights reserved

Similarity theory and calculation of turbulent fluxes at the surface for the stably stratified atmospheric boundary layers

In this paper we revise the similarity theory for the stably stratified atmospheric boundary layer (ABL), formulate analytical approximations for the wind velocity and potential temperature profiles over the entire ABL, validate them against large-eddy simulation and observational data, and develop an improved surface flux calculation technique for use in operational models.Comment: The submission to a special issue of the Boundary-Layer Meteorology devoted to the NATO advanced research workshop Atmospheric Boundary Layers: Modelling and Applications for Environmental Securit

Assessing the semantic similarity of images of silk fabrics using convolutional neural networks

This paper proposes several methods for training a Convolutional Neural Network (CNN) for learning the similarity between images of silk fabrics based on multiple semantic properties of the fabrics. In the context of the EU H2020 project SILKNOW (http://silknow.eu/), two variants of training were developed, one based on a Siamese CNN and one based on a triplet architecture. We propose different definitions of similarity and different loss functions for both training strategies, some of them also allowing the use of incomplete information about the training data. We assess the quality of the trained model by using the learned image features in a k-NN classification. We achieve overall accuracies of 93-95% and average F1-scores of 87-92%. © 2020 Copernicus GmbH. All rights reserved

Semantic segmentation of Brazilian Savanna vegetation using high spatial resolution satellite data and U-net

Large-scale mapping of the Brazilian Savanna (Cerrado) vegetation using remote sensing images is still a challenge due to the high spatial variability and spectral similarity of the different characteristic vegetation types (physiognomies). In this paper, we report on semantic segmentation of the three major groups of physiognomies in the Cerrado biome (Grasslands, Savannas and Forests) using a fully convolutional neural network approach. The study area, which covers a Brazilian conservation unit, was divided into three regions to enable testing the approach in regions that were not used in the training phase. A WorldView-2 image was used in cross validation experiments, in which the average overall accuracy achieved with the pixel-wise classifications was 87.0%. The F-1 score values obtained with the approach for the classes Grassland, Savanna and Forest were of 0.81, 0.90 and 0.88, respectively. Visual assessment of the semantic segmentation outcomes was also performed and confirmed the quality of the results. It was observed that the confusion among classes occurs mainly in transition areas, where there are adjacent physiognomies if a scale of increasing density is considered, which agrees with previous studies on natural vegetation mapping for the Cerrado biome. © Authors 2020. All rights reserved