Dynamic Software Product Lines (DSPLs) is still an incipient paradigm to support runtime reconfiguration of systems that need to change their behavior under varying circumstances. As many modern systems demand reconfiguration after post-deployment time, this 10th edition of the DSPL workshops attempts to bring new insight and approaches for such needs.

Zum bundesweiten Digitaltag am 24. Juni 2022 richtete das Zentrum für Digitalen Wandel in Kooperation mit dem Hi-X-DigiHub und der COMPRA GmbH zum zweiten Mal ein digitales Symposium zum Thema der Low-Code-Softwareentwicklung aus. Mit der Low-Code-Technologie ist die Hoffnung verbunden, mehr Nicht-Informatiker_innen in die Software-Entwicklung einzubinden. Die Technologie ist für verschiedene Problemfelder von besonderer Bedeutung. Es besteht die Möglichkeit, Expert_innen aus Politik, BWL, Recht und anderen Bereichen direkt in die Software-Entwicklung einzubinden, so dass es zu weniger Reibungsverlusten bei der entwickelten Software kommt, da die Fachexpertise direkt in die Entwicklung eingebracht werden kann. Dem bestehenden Fachkräftemangel in der Softwareentwicklung kann insoweit begegnet werden, als dass die Mitwirkung von Expert_innen in der Entwick-lung dazu beitragen kann, dass Systeme entstehen können, bei denen weniger Code und damit weniger Programmier-Expertise erforderlich ist. Insgesamt verspricht dieses Verfahren Effizienz und Innovation in der Softwareent-wicklung.

Early life stress disrupts growth and creates horizontal grooves on the tooth surface in humans and other mammals, yet there is no consensus for their quantitative analysis. Linear defects are considered to be nonspecific stress indicators, but evidence suggests that intermittent, severe stressors create deeper defects than chronic, low-level stressors. However, species-specific growth patterns also influence defect morphology, with faster-growing teeth having shallower defects at the population level. Here we describe a method to measure the depth of linear enamel defects and normal growth increments (i.e., perikymata) from high-resolution 3D topographies using confocal profilometry and apply it to a diverse sample of Homo neanderthalensis and H. sapiens anterior teeth. Debate surrounds whether Neanderthals exhibited modern human-like growth patterns in their teeth and other systems, with some researchers suggesting that they experienced more severe childhood stress. Our results suggest that Neanderthals have shallower features than H. sapiens from the Upper Paleolithic, Neolithic, and medieval eras, mirroring the faster growth rates in Neanderthal anterior teeth. However, when defect depth is scaled by perikymata depth to assess their severity, Neolithic humans have less severe defects, while Neanderthals and the other H. sapiens groups show evidence of more severe early life growth disruptions.

In computer vision, object reconstruction is the task of inferring the 3D shape of an object based on a single or multiple 2D images. For such purpose, most common frameworks use voxel grids and point clouds. However, both of these approaches have strong limitations. On one hand, the computational cost of using voxels grows cubically as the resolution of the voxels increases. Therefore, 3D object reconstructions are usually set to low resolution. On the other hand, point clouds are unstructured in nature and the proper definition of surfaces and contours is complex. In this study, 3D object reconstruction is carried out applying free-form deformations on pre-existent 3D meshes, through two basic learning processes: template selection and template deformation. From this approach, it is possible to generate high-quality 3D object reconstructions with a lower computational cost. Concretely, two novel lightweight CNNs models are developed and tested: a multi-target learner (Model A) and depth information learner (Model B). According to the results, the performance of the multi-target learner regarding the template selection was around three times better (lower error) than in the baseline architecture, which improved the quality of the 3D reconstructions, whereas the depth-information learner showed promising results in the reconstruction of objects with complex geometry. The inherent issue of using chamfer distance as a loss measure is also examined.

Traditionally cultivated vineyards in the Mediterranean terraced landscape of the Cinque Terre (Liguria, Italy) have been affected by land use abandonment for decades. This trend has profound consequences for the cultural landscape that today is protected as a national park and UNESCO World Heritage Site with great importance for tourism. The change of vegetation in the course of secondary succession is of crucial importance for the development of slope stability, biodiversity and the scenic value of the landscape. On many slopes of the Cinque Terre, a mosaic of different stages of fallow vegetation in direct neighborhood of remaining cultivated areas has formed. Near to the Village of Corniglia vegetation studies on fallow and cultivated areas were carried out in the 1980s and 2000s by Richter (1989), Carl & Richter (1989), Richter & Block (2001). In this study we present a continuation of a mapping time series of plant formations that now reaches back to round about 40 years and characterizes it based on collected field data. On the one hand our results show the expected continuation of abandonment and the progression of secondary succession and a related change in life-form spectra. Furthermore, disturbances such as insect calamities have led to a local disappearance of pine stands on all study sites since the beginning of 2000s, thus advancing the maquis shrubs and holm oak stands as temporary final communities. Future research should integrate knowledge about spatial and temporal development of follow vegetation on agricultural terraces into approaches for ecosystem services assessment, thus supporting the decision making processes of landscape management.

Accelerated global change, including land use change, is altering plant community composition and challenging conservation in protected areas (PAs) worldwide. Vegetation resurveys of quasi-permanent plots provide insights into these shifts over decades. However, resurvey studies are scarce in the temperate forests of the Andes in northern Patagonia, leaving questions on temporal vegetation dynamics unanswered. We resurveyed vegetation plots of three different forest types (mixed evergreen, coniferous and subalpine) located in Nahuel Huapi National Park >50 years after the original survey, in an area with current minimal but moderate to intense land use in the past. We applied multivariate analyses to assess changes in plant community composition and fitted generalized linear mixed models to compare temporal changes of different life forms and origin groups. Species gains and losses were analyzed by resurvey location and “winner” and “loser” species were identified. Our results indicate biotic homogenization of montane mixed evergreen and coniferous forests, ongoing understory closure and an increasing dominance of non-native species. Conversely, subalpine forests showed a more stable composition over time. These findings underscore the significance of legacy effects, i.e., land-use practices occurring decades ago having residual influences on the current biological composition. We conclude that without conservation management following intensive land use, such as non-native species removal, neither local nor global biodiversity conservation goals are likely to be met.

More than fourty participants attended the 9th Symposium on Software Performance in Hildesheim. The "Symposium on Software Performance" brings together researchers and practitioners interested in all facets of software performance, ranging from modeling and prediction to monitoring and runtime management.

Background: The Multi Depot Pickup and Delivery Problem with Time Windows and Heterogeneous Vehicle Fleets (MDPDPTWHV) is a strongly practically oriented routing problem with many real-world constraints. Due to its complexity, solution approaches with sufficiently good quality ideally contain several operators with certain probabilities.Thus, automatically selecting the best parameter configurations enhances the overall solution quality. Methods: To solve the MDPDPTWHV, we present a Grouping Genetic Algorithm (GGA) framework with several operators and population management variants. A Bayesian Optimization (BO) approach is introduced to optimize the GGA’s parameter configuration. The parameter tuning is evaluated on five data sets which differ in several structural characteristics and contain 1200 problem instances. The outcomes of the parameter-tuned GGA are compared to both the initial GGA parameter configuration and a state-of-the-art Adaptive Large Neighborhood Search (ALNS). Results: The presented GGA framework achieves a better solution quality than the ALNS, even for the initial parameter configuration used. The mean value of the relative error is less than 0.9% and its standard deviation is less than 1.31% for every problem class. For the ALNS, these values are up to three times higher and the GGA is up to 38% faster than the ALNS. Conclusions: It is shown that the BO, as a parameter tuning approach, is a good choice in improving the performance of the considered meta-heuristic over all instances in each data set. In addition, the best parameter configuration per problem class with the same characteristics is able to improve both the frequency of finding the best solution, as well as the relative error to this solution, significantly.

Residual value forecasting plays an important role in many areas, e.g., for vehicles to price leasing contracts. High forecasting accuracy is crucial as any overestimation will lead to lost sales...

In this note, we introduce a generalized formula for Cohen’s d> under the presence of additional independent variables, providing a measure for the size of a possible effect concerning the size of a difference location effect of a variable in two groups. This is done by employing the so-called Frisch–Waugh–Lovell theorem in a partitioned linear regression model. The generalization is motivated by demonstrating the relationship to appropriate t and F statistics. Our discussion is further illustrated by inference about a publicly available data set.

In this note we reconsider two known algorithms which both usually converge faster than the randomized Kaczmarz method introduced by Strohmer and Vershynin(2009), but require the additional computation of all residuals of an iteration at each step. As already indicated in the literature, e.g. arXiv:2007.02910 and arXiv:2011.14693, it is shown that the non-randomized version of the two algorithms converges at least as fast as the randomized version, while still requiring computation of all residuals. Based on that observation, a new simple random sample selection scheme has been introduced by arXiv:2011.14693 to reduce the required total of residuals. In the same light we propose an alternative random selection scheme which can easily be included as a `partially weighted selection step' into the classical randomized Kaczmarz algorithm without much ado. Numerical examples show that the randomly determined number of required residuals can be quite moderate.