An adaptation of the experiences in close relationships scale-revised for use with children and adolescents

The investigation of attachment processes during middle childhood and early adolescence has been hampered by a relative lack of measures for this age group differentiating between two fundamental attachment dimensions, that is, anxiety and avoidance. The aim of this study is to develop and validate a child version of the Experiences in Close Relationships Scale-Revised (referred to as the ECR-RC), a self-report questionnaire measuring attachment anxiety and avoidance. Two studies were conducted to examine the internal structure (Study 1, N = 514 and Study 2, N = 296) and construct and predictive validity (Study 2) of the ECR-RC. The ECR-RC appears to be a promising instrument to measure the two attachment dimensions in middle childhood and early adolescence

Perceived maternal autonomy-support and early adolescent emotion regulation: a longitudinal study

This study investigated longitudinal associations between perceived maternal autonomy-supportive parenting and early adolescents' use of three emotion regulation (ER) styles: emotional integration, suppressive regulation, and dysregulation. We tested whether perceived maternal autonomy support predicted changes in ER and whether these ER styles, in turn, related to changes in adjustment (i.e., depressive symptoms, self-esteem). Participants (N= 311, mean age at Time 1 = 12.04) reported on perceived maternal autonomy support, their ER styles, and adjustment at two moments in time, spanning a one-year interval. Cross-lagged analyses showed that perceived maternal autonomy support predicted increases in emotional integration and decreases in suppressive regulation. By contrast, emotional dysregulation predicted decreases in perceived autonomy-supportive parenting. Further, increases in emotional integration were predictive of increases in self-esteem, and decreases in suppressive regulation were predictive of decreases in depressive symptoms. Together, the results show that early adolescents' perception of their mothers as autonomy-supportive is associated with increases in adaptive ER strategies and subsequent adjustment

Transforming Feature Space to Interpret Machine Learning Models

Model-agnostic tools for interpreting machine-learning models struggle to summarize the joint effects of strongly dependent features in high-dimensional feature spaces, which play an important role in pattern recognition, for example in remote sensing of landcover. This contribution proposes a novel approach that interprets machine-learning models through the lens of feature space transformations. It can be used to enhance unconditional as well as conditional post-hoc diagnostic tools including partial dependence plots, accumulated local effects plots, or permutation feature importance assessments. While the approach can also be applied to nonlinear transformations, we focus on linear ones, including principal component analysis (PCA) and a partial orthogonalization technique. Structured PCA and diagnostics along paths offer opportunities for representing domain knowledge. The new approach is implemented in the R package `wiml`, which can be combined with existing explainable machine-learning packages. A case study on remote-sensing landcover classification with 46 features is used to demonstrate the potential of the proposed approach for model interpretation by domain experts.Comment: 13 pages, 7 figures, 1 tabl

Spatial prediction models for landslide hazards: review, comparison and evaluation

The predictive power of logistic regression, support vector machines and bootstrap-aggregated classification trees (bagging, double-bagging) is compared using misclassification error rates on independent test data sets. Based on a resampling approach that takes into account spatial autocorrelation, error rates for predicting 'present' and 'future' landslides are estimated within and outside the training area. In a case study from the Ecuadorian Andes, logistic regression with stepwise backward variable selection yields lowest error rates and demonstrates the best generalization capabilities. The evaluation outside the training area reveals that tree-based methods tend to overfit the data

Geostatistics without Stationarity Assumptions within Geographical Information Systems

The present work deals with two challenging problems of applied geostatistics: (i) Stationarity assumptions often do not hold under real-world conditions. (ii) Geostatistical methods have to be linked with spatial databases in order to be applicable in non-stationary situations. Solutions for both problems are proposed and implemented. (i) A central assumption in geostatistics is the stationarity of the process. However the spatial variability of many natural phenomena heavily depends on the local geology, which is nonstationary in most cases. To deal with this, the concept of process stationarity is replaced by a stationarity of the governing influence relating the local semivariogram and the local geology as stored in a Geographical Information System (GIS). A construction method is used, which can meaningfully incorporate additional spatial information from GIS, e.g. smoothly varying geology in the investigated area, spatially varying anisotropy induced by mountainous morphology, or geological faults interrupting continuity. Least-squares parameter estimation is used for fitting instationary semivariogram models in typical example situations, leading to non-linear optimization problems. Furthermore, a method for semivariogram parameter estimation in the present of linear trend is proposed. (ii) Geostatistical tools that make use of the local geology need direct access to the data stored in the GIS. A link between the presented geostatistical tools and the GIS software ArcView was established. Thus, spatial data such as measured contaminant concentrations, soil properties and morphology can be incorporated in geostatistical analyses. R code that fits instationary semivariogram models and performs kriging was implemented and can be obtained from the author. It is applied to simulated datasets.Die vorliegende Diplomarbeit befasst sich mit zwei wichtigen Problemen der angewandten Geostatistik: (i) Stationaritätsannahmen werden unter realweltlichen Bedingungen oft nicht erfüllt. (ii) Geostatistische Methoden müssen mit räumlichen Datenbanken verbunden werden, um unter nichtstationären Bedingungen anwendbar zu sein. Lösungen für beide Probleme werden vorgeschlagen und implementiert. (i) In der Geostatistik ist die Stationarität des Prozesses eine zentrale Annahme. Die räumliche Variabilität vieler Phänomene in unserer Umwelt hängt jedoch stark von lokalen geologischen Verhältnissen ab, die meist aber instationär sind. Um damit umgehen zu können, wird das Konzept der Stationarität des Prozesses ersetzt durch eine Stationarität des Einflusses der lokalen Geologie, wie sie in einem GIS gespeichert ist, auf das lokale Semivariogramm. Es wird eine Konstruktionsmethode benutzt, die auf sinnvolle Art räumliche Informationen aus dem GIS in Semivariogrammmodelle einbinden kann, etwa sich über das Untersuchungsgebiet gleichmäßig verändernde geologische Verhältnisse, sich räumlich verändernde Anisotropie im Gebirgsrelief oder geologische Störungen, die die Kontinuität unterbrechen. Kleinste-Quadrate Schätzung wird für die Anpassung instationärer Semivariogrammmodelle in typischen Beispielsituationen verwendet. Dies führt zu nichtlinearen Optimierungsproblemen. Des weiteren wird eine Methode der Schätzung von Semivariogrammparametern in Modellen mit linearem Trend vorgestellt. (ii) Geostatistische Werkzeuge, die lokalen geologischen Verh¨ältnisse berücksichtigen, benötigen einen direkten Zugang zu Daten, die in einem GIS gespeichert sind. Im Rahmen dieser Arbeit wurde eine Verbindung zwischen den vorgestellten geostatistischen Werkzeugen und dem GIS Programm ArcView erstellt. Auf diese Weise können räumliche Daten wie etwa Schadstoffkonzentrationen, Bodeneigenschaften oder die Morphologie in geostatistische Analysen einbezogen werden. R-Code, der instationäre Semivariogrammmodelle anpasst und Kriging durchführt, wurde erstellt und auf simulierte Datensätze angewandt. Der Code kann über den Author bezogen werden.researc

On Trusting Third-party Satellite Data

Increased access to space has opened the door to many satellite vendors. These vendors are collecting data using a variety of sensors, including electro-optical, radio frequency, and synthetic aperture radar. Customers want true-sourced, authentic data. However, as with any lower barrier to entry, the risk of counterfeit, tampered, or low-quality products increases. In this work, we describe the key requirements for trusting imagery and present a hardware design and a system of controls that meet those requirements of trust for Earth imaging satellites. Our trusted hardware provides assurance of capture time, location, and preserves the content and origin by capturing and digitally signing the information end users need to make trust decisions about the data. Our hardware functions as an independent witness that oversees and signs off on satellite collection activities. Anti-tamper, inspection, and verification measures protect and verify the secure operation of our hardware. Satellite operators that use this approach in their satellites and operations will offer their end users greater assurance in the authenticity of the produced satellite imagery products