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An intercomparison of aerosol backscatter lidar algorithms was performed in 2001 within the framework of the European Aerosol Research Lidar Network to Establish an Aerosol Climatology (EARLINET). The objective of this research was to test the correctness of the algorithms and the influence of the lidar ratio used by the various lidar teams involved in the EARLINET for calculation of backscatter-coefficient profiles from the lidar signals. The exercise consisted of processing synthetic lidar signals of various degrees of difficulty. One of these profiles contained height- dependent lidar ratios to test the vertical influence of those profiles on the various retrieval algorithms. Furthermore, a realistic incomplete overlap of laser beam and receiver field of view was introduced to remind the teams to take great care in the nearest range to the lidar. The intercomparison was performed in three stages with increasing knowledge on the input parameters. First, only the lidar signals were distributed; this is the most realistic stage. Afterward the lidar ratio profiles and the reference values at calibration height were provided. The unknown height- dependent lidar ratio had the largest influence on the retrieval, whereas the unknown reference value was of minor importance. These results show the necessity of making additional independent measurements, which can provide us with a suitable approximation of the lidar ratio. The final stage proves in general, that the data evaluation schemes of the different groups of lidar systems work well. (C) 2004 Optical Society of America
Understanding the natural history of model organisms is important for the effective use of their genomic resourses. Arabidopsis lyrata has emerged as a useful plant for studying ecological and evolutionary genetics, based on its extensive natural variation, sequenced genome and close relationship to A. thaliana. We studied genetic diversity across the entire range of European Arabidopsis lyrata ssp. petraea, in order to explore how population history has influenced population structure. We sampled multiple populations from each region, using nuclear and chloroplast genome markers, and combined population genetic and phylogeographic approaches. Within-population diversity is substantial for nuclear allozyme markers (mean P = 0.610, A(e) = 1.580, H-e = 0.277) and significantly partitioned among populations (F- ST = 0.271). The Northern populations have modestly increased inbreeding (F-IS = 0.163 verses F-IS = 0.093), but retain comparable diversity to central European populations. Bottlenecks are common among central and northern Europe populations, indicating recent demographic history as a dominant factor in structuring the European diversity. Although the genetic structure was detected at all geographic scales, two clear differentiated units covering northern and central European areas (F-CT = 0.155) were identified by Bayesian analysis and supported by regional pairwise F-CT calculations. A highly similar geographic pattern was observed from the distribution of chloroplast haplotypes, with the dominant northern haplotypes absent from central Europe. We conclude A. l. petraea's cold-tolerance and preference for disturbed habitats enabled glacial survival between the alpine and Nordic glaciers in central Europe and an additional cryptic refugium. While German populations are probable peri-glacial leftovers, Eastern Austrian populations have diversity patterns possibly compatible with longer-term survival.
Modeling and Formal Analysis of Meta-Ecosystems with Dynamic Structure using Graph Transformation
(2022)
The dynamics of ecosystems is of crucial importance. Various model-based approaches exist to understand and analyze their internal effects. In this paper, we model the space structure dynamics and ecological dynamics of meta-ecosystems using the formal technique of Graph Transformation (short GT). We build GT models to describe how a meta-ecosystem (modeled as a graph) can evolve over time (modeled by GT rules) and to analyze these GT models with respect to qualitative properties such as the existence of structural stabilities. As a case study, we build three GT models describing the space structure dynamics and ecological dynamics of three different savanna meta-ecosystems. The first GT model considers a savanna meta-ecosystem that is limited in space to two ecosystem patches, whereas the other two GT models consider two savanna meta-ecosystems that are unlimited in the number of ecosystem patches and only differ in one GT rule describing how the space structure of the meta-ecosystem grows. In the first two GT models, the space structure dynamics and ecological dynamics of the meta-ecosystem shows two main structural stabilities: the first one based on grassland-savanna-woodland transitions and the second one based on grassland-desert transitions. The transition between these two structural stabilities is driven by high-intensity fires affecting the tree components. In the third GT model, the GT rule for savanna regeneration induces desertification and therefore a collapse of the meta-ecosystem. We believe that GT models provide a complementary avenue to that of existing approaches to rigorously study ecological phenomena.
The automated detection of sequential anomalies in time series is an essential task for many applications, such as the monitoring of technical systems, fraud detection in high-frequency trading, or the early detection of disease symptoms. All these applications require the detection to find all sequential anomalies possibly fast on potentially very large time series. In other words, the detection needs to be effective, efficient and scalable w.r.t. the input size. Series2Graph is an effective solution based on graph embeddings that are robust against re-occurring anomalies and can discover sequential anomalies of arbitrary length and works without training data. Yet, Series2Graph is no t scalable due to its single-threaded approach; it cannot, in particular, process arbitrarily large sequences due to the memory constraints of a single machine. In this paper, we propose our distributed anomaly detection system, short DADS, which is an efficient and scalable adaptation of Series2Graph. Based on the actor programming model, DADS distributes the input time sequence, intermediate state and the computation to all processors of a cluster in a way that minimizes communication costs and synchronization barriers. Our evaluation shows that DADS is orders of magnitude faster than S2G, scales almost linearly with the number of processors in the cluster and can process much larger input sequences due to its scale-out property.
DualPanto
(2018)
We present a new haptic device that enables blind users to continuously track the absolute position of moving objects in spatial virtual environments, as is the case in sports or shooter games. Users interact with DualPanto by operating the me handle with one hand and by holding on to the it handle with the other hand. Each handle is connected to a pantograph haptic input/output device. The key feature is that the two handles are spatially registered with respect to each other. When guiding their avatar through a virtual world using the me handle, spatial registration enables users to track moving objects by having the device guide the output hand. This allows blind players of a 1-on-1 soccer game to race for the ball or evade an opponent; it allows blind players of a shooter game to aim at an opponent and dodge shots. In our user study, blind participants reported very high enjoyment when using the device to play (6.5/7).