Automatic derivation of land-use from topographic data

The paper presents an approach for the reclassification and generalization of land-use information from topographic information. Based on a given transformation matrix describing the transition from topographic data to land-use data, a semantic and geometry based generalization of too small features for the target scale is performed. The challenges of the problem are as follows: (1) identification and reclassification of heterogeneous feature classes by local interpretation, (2) presence of concave, narrow or very elongated features, (3) processing of very large data sets. The approach is composed of several steps consisting of aggregation, feature partitioning, identification of mixed feature classes and simplification of feature outlines. The workflow will be presented with examples for generating CORINE Land Cover (CLC) features from German Authoritative Topographic Cartographic Information System (ATKIS) data for the whole are of Germany. The results will be discussed in detail, including runtimes as well as dependency of the result on the parameter setting

Building generalization using deep learning

Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g. simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the benchmark is the human operator, who is able to design an aesthetic and correct representation of the physical reality. Deep Learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform the traditional computer vision methods. In both domains – computer vision and cartography – humans are able to produce a solution; a prerequisite for this is, that there is the possibility to generate many training examples for the different cases. Thus, the idea in this paper is to employ Deep Learning for cartographic generalizations tasks, especially for the task of building generalization. An advantage of this task is the fact that many training data sets are available from given map series. The approach is a first attempt using an existing network. In the paper, the details of the implementation will be reported, together with an in depth analysis of the results. An outlook on future work will be given

Learning cartographic building generalization with deep convolutional neural networks

Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g., simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the human operator is the benchmark, who is able to design an aesthetic and correct representation of the physical reality. Deep learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform traditional computer vision methods. In both domains-computer vision and cartography-humans are able to produce good solutions. A prerequisite for the application of deep learning is the availability of many representative training examples for the situation to be learned. As this is given in cartography (there are many existing map series), the idea in this paper is to employ deep convolutional neural networks (DCNNs) for cartographic generalizations tasks, especially for the task of building generalization. Three network architectures, namely U-net, residual U-net and generative adversarial network (GAN), are evaluated both quantitatively and qualitatively in this paper. They are compared based on their performance on this task at target map scales 1:10,000, 1:15,000 and 1:25,000, respectively. The results indicate that deep learning models can successfully learn cartographic generalization operations in one single model in an implicit way. The residual U-net outperforms the others and achieved the best generalization performance

A novel method for transient detection in high-cadence optical surveys: Its application for a systematic search for novae in M31

[abridged] In large-scale time-domain surveys, the processing of data, from procurement up to the detection of sources, is generally automated. One of the main challenges is contamination by artifacts, especially in regions of strong unresolved emission. We present a novel method for identifying candidates for variables and transients from the outputs of such surveys' data pipelines. We use the method to systematically search for novae in iPTF observations of the bulge of M31. We demonstrate that most artifacts produced by the iPTF pipeline form a locally uniform background of false detections approximately obeying Poissonian statistics, whereas genuine variables and transients as well as artifacts associated with bright stars result in clusters of detections, whose spread is determined by the source localization accuracy. This makes the problem analogous to source detection on images produced by X-ray telescopes, enabling one to utilize tools developed in X-ray astronomy. In particular, we use a wavelet-based source detection algorithm from the Chandra data analysis package CIAO. Starting from ~2.5x10^5 raw detections made by the iPTF data pipeline, we obtain ~4000 unique source candidates. Cross-matching these candidates with the source-catalog of a deep reference image, we find counterparts for ~90% of them. These are either artifacts due to imperfect PSF matching or genuine variable sources. The remaining ~400 detections are transient sources. We identify novae among these candidates by applying selection cuts based on the expected properties of nova lightcurves. Thus, we recovered all 12 known novae registered during the time span of the survey and discovered three nova candidates. Our method is generic and can be applied for mining any target out of the artifacts in optical time-domain data. As it is fully automated, its incompleteness can be accurately computed and corrected for.Comment: 16 pages, 8 figures, accepted to A&

Sleep disturbances and circadian CLOCK genes in borderline personality disorder

Borderline personality disorder (BPD) is characterised by a deep-reaching pattern of affective instability, incoherent identity, self-injury, suicide attempts, and disturbed interpersonal relations and lifestyle. The daily activities of BPD patients are often chaotic and disorganized, with patients often staying up late while sleeping during the day. These behavioural patterns suggest that altered circadian rhythms may be associated with BPD. Furthermore, BPD patients frequently report suffering from sleep disturbances. In this review, we overview the evidence that circadian rhythms and sleep are disturbed in BPD, and we explore the possibility that personality traits that are pertinent for BPD may be associated with circadian typology, and perhaps to circadian genotypes. With regards to sleep architecture, we review the evidence that BPD patients display altered non-REM and REM sleep. A possible cue to a deeper understanding of this temporal dysregulation might be an analysis of the circadian clock at the molecular and cellular level, as well as behavioural studies using actigraphy and we suggest avenues for further exploration of these factors

Critical adsorption at chemically structured substrates

We consider binary liquid mixtures near their critical consolute points and exposed to geometrically flat but chemically structured substrates. The chemical contrast between the various substrate structures amounts to opposite local preferences for the two species of the binary liquid mixtures. Order parameters profiles are calculated for a chemical step, for a single chemical stripe, and for a periodic stripe pattern. The order parameter distributions exhibit frustration across the chemical steps which heals upon approaching the bulk. The corresponding spatial variation of the order parameter and its dependence on temperature are governed by universal scaling functions which we calculate within mean field theory. These scaling functions also determine the universal behavior of the excess adsorption relative to suitably chosen reference systems

"Die trägt ja als Deutsche ein Kopftuch" - Der Einfluss autoritärer Einstellungen auf die Wahrnehmung der Verletzung kultureller Normen

Muslimische Symbole werden häufig als eine von außen herangetragene Bedrohung kultureller Werte in Deutschland wahrgenommen. Was geschieht aber, wenn Mitglieder der eigenen Gruppe diese Symbole verwenden? In der vorliegenden Studie sind wir der Frage nachgegangen, wie deutsche Frauen wahrgenommen werden, die das Kopftuch Al-Amira, tragen. Nach dem Black Sheep Effekt werden Normverstöße durch Mitglieder der Eigengruppe stärker geahndet als durch Mitglieder einer Fremdgruppe. Wir nehmen an, dass dies besonders dann gelten sollte, wenn Personen hohe Ausprägungen in autoritären Einstellungen zeigen und daher Normabweichungen als Bedrohung für die Gruppenkohäsion wahrnehmen. In einer online durchgeführten experimentellen Studie präsentierten wir deutschen Teilnehmenden (N = 193, 139 weiblich, 51 männlich, 3 divers; M Alter = 26, SD = 7,64) anhand eines zweifaktoriellen between-participants Designs Fotos von deutschen oder türkischen Frauen mit oder ohne Kopftuch (Al-Amira) und erfassten Autoritarismus als Moderatorvariable. Entsprechend unserer Hypothesen konnten wir zeigen, dass deutsche Proband*innen mit steigenden Ausprägungen in Autoritarismus weniger Vertrauen und weniger Kommunikationsbereitschaft gegenüber deutschen Frauen mit Kopftuch als gegenüber türkischen Frauen mit Kopftuch zeigten. Die Ergebnisse stützen das Verständnis von Normorientierung als zentrales Merkmal von Autoritarismus.Muslim symbols are often perceived as an external threat to cultural values in Germany. What happens if in-group members apply these symbols? In the present study, we investigated the perception of German women wearing the headscarf Al-Amira. According to the black sheep effect, norm violations by in-group members are punished more severely than norm violations by out-group members. We assume that this should apply in particular when people show high levels of authoritarian attitudes and therefore perceive deviations from the norm as a threat to group cohesion. In an experimental study conducted online, we presented German participants (N = 193, 139 female, 51 male, 3 diverse; M age = 26, SD = 7.64), in a two-factor between-participant design, photos of German or Turkish women with or without a headscarf (Al-Amira) and recorded authoritarianism as a moderator variable. Based on our hypotheses, we were able to show that German participants showed with increasing levels of authoritarianism less trust and less willingness to communicate with German women wearing headscarves than with Turkish women wearing headscarves. The results support the understanding of norm orientation as a central characteristic of authoritarianism

Variability of Red Supergiants in M31 from the Palomar Transient Factory

Most massive stars end their lives as Red Supergiants (RSGs), a short-lived evolution phase when they are known to pulsate with varying amplitudes. The RSG period-luminosity (PL) relation has been measured in the Milky Way, the Magellanic Clouds and M33 for about 120 stars in total. Using over 1500 epochs of R-band monitoring from the Palomar Transient Factory (PTF) survey over a five-year period, we study the variability of 255 spectroscopically cataloged RSGs in M31. We find that all RGSs brighter than M_K~ -10 mag (log(L/L_sun)>4.8) are variable at dm_R>0.05 mag. Our period analysis finds 63 with significant pulsation periods. Using the periods found and the known values of M_K for these stars, we derive the RSG PL relation in M31 and show that it is consistent with those derived earlier in other galaxies of different metallicities. We also detect, for the first time, a sequence of likely first-overtone pulsations. Comparison to stellar evolution models from MESA confirms the first overtone hypothesis and indicates that the variable stars in this sample have 12 M_sun<M<24 M_sun. As these RSGs are the immediate progenitors to Type II-P core-collapse supernovae (SNe), we also explore the implication of their variability in the initial-mass estimates for SN progenitors based on archival images of the progenitors. We find that this effect is small compared to the present measurement errors.Comment: 17 pages, 10 figure