Using semantically paired images to improve domain adaptation for the semantic segmentation of aerial images

Modern machine learning, especially deep learning, which is used in a variety of applications, requires a lot of labelled data for model training. Having an insufficient amount of training examples leads to models which do not generalize well to new input instances. This is a particular significant problem for tasks involving aerial images: Often training data is only available for a limited geographical area and a narrow time window, thus leading to models which perform poorly in different regions, at different times of day, or during different seasons. Domain adaptation can mitigate this issue by using labelled source domain training examples and unlabeled target domain images to train a model which performs well on both domains. Modern adversarial domain adaptation approaches use unpaired data. We propose using pairs of semantically similar images, i.e., whose segmentations are accurate predictions of each other, for improved model performance. In this paper we show that, as an upper limit based on ground truth, using semantically paired aerial images during training almost always increases model performance with an average improvement of 4.2% accuracy and .036 mean intersection-over-union (mIoU). Using a practical estimate of semantic similarity, we still achieve improvements in more than half of all cases, with average improvements of 2.5% accuracy and .017 mIoU in those cases. © 2020 Copernicus GmbH. All rights reserved

Automatically generated training data for land cover classification with cnns using sentinel-2 images

Pixel-wise classification of remote sensing imagery is highly interesting for tasks like land cover classification or change detection. The acquisition of large training data sets for these tasks is challenging, but necessary to obtain good results with deep learning algorithms such as convolutional neural networks (CNN). In this paper we present a method for the automatic generation of a large amount of training data by combining satellite imagery with reference data from an available geospatial database. Due to this combination of different data sources the resulting training data contain a certain amount of incorrect labels. We evaluate the influence of this so called label noise regarding the time difference between acquisition of the two data sources, the amount of training data and the class structure. We combine Sentinel-2 images with reference data from a geospatial database provided by the German Land Survey Office of Lower Saxony (LGLN). With different training sets we train a fully convolutional neural network (FCN) and classify four land cover classes (code Building, Agriculture, Forest, Water/code). Our results show that the errors in the training samples do not have a large influence on the resulting classifiers. This is probably due to the fact that the noise is randomly distributed and thus, neighbours of incorrect samples are predominantly correct. As expected, a larger amount of training data improves the results, especially for the less well represented classes. Other influences are different illuminations conditions and seasonal effects during data acquisition. To better adapt the classifier to these different conditions they should also be included in the training data. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Effect of Level Statistics on Superconductivity in Ultrasmall Metallic Grains

We examine the destruction of superconducting pairing in metallic grains as their size is decreased for both even and odd numbers of electrons. This occurs when the average level spacing d is of the same order as the BCS order parameter. The energy levels of these grains are randomly distributed according to random matrix theory, and we must work statistically. We find that the average value of the critical level spacing is larger than for the model of equally spaced levels for both parities, and derive numerically the probabilities $P_{o,e}(d)$ that a grain of mean level spacing d shows pairing.Comment: 12 pages, 2 PostScript files, RevTex format, submitted to PR

Parity Effect in Ground State Energies of Ultrasmall Superconducting Grains

We study the superconductivity in small grains in the regime when the quantum level spacing $\delta\varepsilon$ is comparable to the gap $\Delta$. As $\delta\varepsilon$ is increased, the system crosses over from superconducting to normal state. This crossover is studied by calculating the dependence of the ground state energy of a grain on the parity of the number of electrons. The states with odd numbers of particles carry an additional energy $\Delta_P$, which shows non-monotonic dependence on $\delta\varepsilon$. Our predictions can be tested experimentally by studying the parity-induced alternation of Coulomb blockade peak spacings in grains of different sizes.Comment: 4 pages, revtex, multicol.st

Pregnancy termination for fetal abnormality: are health professionals’ perceptions of women’s coping congruent with women’s accounts?

Background: Pregnancy termination for fetal abnormality (TFA) may have profound psychological consequences for those involved. Evidence suggests that women’s experience of care influences their psychological adjustment to TFA and that they greatly value compassionate healthcare. Caring for women in these circumstances presents challenges for health professionals, which may relate to their understanding of women’s experience. This qualitative study examined health professionals’ perceptions of women’s coping with TFA and assessed to what extent these perceptions are congruent with women’s accounts. Methods: Fifteen semi-structured interviews were carried out with health professionals in three hospitals in England. Data were analysed using thematic analysis and compared with women’s accounts of their own coping processes to identify similarities and differences. Results: Health professionals’ perceptions of women’s coping processes were congruent with women’s accounts in identifying the roles of support, acceptance, problem-solving, avoidance, another pregnancy and meaning attribution as key coping strategies. Health professionals regarded coping with TFA as a unique grieving process and were cognisant of women’s idiosyncrasies in coping. They also considered their role as information providers as essential in helping women cope with TFA. The findings also indicate that health professionals lacked insight into women’s long-term coping processes and the potential for positive growth following TFA, which is consistent with a lack of aftercare following TFA reported by women. Conclusions: Health professionals’ perceptions of women’s coping with TFA closely matched women’s accounts, suggesting a high level of understanding. However, the lack of insight into women’s long-term coping processes has important clinical implications, as research suggests that coping with TFA is a long-term process and that the provision of aftercare is beneficial to women. Together, these findings call for further research into the most appropriate ways to support women post-TFA, with a view to developing a psychological intervention to better support women in the future

Mesoscopic quantum transport: Resonant tunneling in the presence of strong Coulomb interaction

Coulomb blockade phenomena and quantum fluctuations are studied in mesoscopic metallic tunnel junctions with high charging energies. If the resistance of the barriers is large compared to the quantum resistance, transport can be described by sequential tunneling. Here we study the influence of quantum fluctuations. They are important when the resistance is small or the temperature very low. A real-time approach is developed which allows the diagrammatic classification of ``inelastic resonant tunneling'' processes where different electrons tunnel coherently back and forth between the leads and the metallic island. With the help of a nonperturbative resummation technique we evaluate the spectral density which describes the charge excitations of the system. From it physical quantities of interest like current and average charge can be deduced. Our main conclusions are: An energy renormalization leads to a logarithmic temperature dependence of the renormalized system parameters. A finite lifetime broadening can change the classical picture drastically. It gives rise to a strong flattening of the Coulomb oscillations for low resistances, but in the Coulomb blockade regime inelastic electron cotunneling persists. The temperature where these effects are important are accessible in experiments.Comment: 24 pages + 23 figures (available by fax or conventional mail, upon request) tfp-1994-1

Resonant tunneling through a macroscopic charge state in a superconducting SET transistor

We predict theoretically and observe in experiment that the differential conductance of a superconducting SET transistor exhibits a peak which is a complete analogue in a macroscopic system of a standard resonant tunneling peak associated with tunneling through a single quantum state. In particular, in a symmetric transistor, the peak height is universal and equal to $e^2/2\pi \hbar$. Away from the resonance we clearly observe the co-tunneling current which in contrast to the normal-metal transistor varies linearly with the bias voltage.Comment: 11 pages, 3 figures, Fig. 1 available upon request from the first autho

Coulomb blockade in superconducting quantum point contacts

Amplitude of the Coulomb blockade oscillations is calculated for a single-mode Josephson junction with arbitrary electron transparency $D$. It is shown that the Coulomb blockade is suppressed in ballistic junctions with $D\to 1$. The suppression is described quantitatively as the Landau-Zener transition in imaginary time.Comment: 5 pages, 3 figures include

Conductance of the single-electron transistor: A comparison of experimental data with Monte Carlo calculations

We report on experimental results for the conductance of metallic single-electron transistors as a function of temperature, gate voltage and dimensionless conductance. In contrast to previous experiments our transistor layout allows for a direct measurement of the parallel conductance and no ad hoc assumptions on the symmetry of the transistors are necessary. Thus we can make a comparison between our data and theoretical predictions without any adjustable parameter. Even for rather weakly conducting transistors significant deviations from the perturbative results are noted. On the other hand, path integral Monte Carlo calculations show remarkable agreement with experiments for the whole range of temperatures and conductances.Comment: 8 pages, 7 figures, revtex4, corrected typos, submitted to PR

Detection of curved lines with B-COSFIRE filters: A case study on crack delineation

The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. %The visual system of the brain has remarkable abilities to detect curvilinear structures in noisy images. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure=0.865). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.Comment: Accepted at Computer Analysis of Images and Patterns (CAIP) 201