Noise suppressing sensor encoding and neural signal orthonormalization

In this paper we regard first the situation where parallel channels are disturbed by noise. With the goal of maximal information conservation we deduce the conditions for a transform which "immunizes" the channels against noise influence before the signals are used in later operations. It shows up that the signals have to be decorrelated and normalized by the filter which corresponds for the case of one channel to the classical result of Shannon. Additional simulations for image encoding and decoding show that this constitutes an efficient approach for noise suppression. Furthermore, by a corresponding objective function we deduce the stochastic and deterministic learning rules for a neural network that implements the data orthonormalization. In comparison with other already existing normalization networks our network shows approximately the same in the stochastic case but, by its generic deduction ensures the convergence and enables the use as independent building block in other contexts, e.g. whitening for independent component analysis. Keywords: information conservation, whitening filter, data orthonormalization network, image encoding, noise suppression

The Tayler instability of toroidal magnetic fields in a columnar gallium experiment

The nonaxisymmetric Tayler instability of toroidal magnetic fields due to axial electric currents is studied for conducting incompressible fluids between two coaxial cylinders without endplates. The inner cylinder is considered as so thin that even the limit of R_in \to 0 can be computed. The magnetic Prandtl number is varied over many orders of magnitudes but the azimuthal mode number of the perturbations is fixed to m=1. In the linear approximation the critical magnetic field amplitudes and the growth rates of the instability are determined for both resting and rotating cylinders. Without rotation the critical Hartmann numbers do {\em not} depend on the magnetic Prandtl number but this is not true for the growth rates. For given product of viscosity and magnetic diffusivity the growth rates for small and large magnetic Prandtl number are much smaller than those for Pm=1. For gallium under the influence of a magnetic field at the outer cylinder of 1 kG the resulting growth time is 5 s. The minimum electric current through a container of 10 cm diameter to excite the kink-type instability is 3.20 kA. For a rotating container both the critical magnetic field and the related growth times are larger than for the resting column.Comment: 7 pages, 9 figures, submitted to Astron. Nach

Helicity and alpha-effect by current-driven instabilities of helical magnetic fields

Helical magnetic background fields with adjustable pitch angle are imposed on a conducting fluid in a differentially rotating cylindrical container. The small-scale kinetic and current helicities are calculated for various field geometries, and shown to have the opposite sign as the helicity of the large-scale field. These helicities and also the corresponding $\alpha$-effect scale with the current helicity of the background field. The $\alpha$-tensor is highly anisotropic as the components $\alpha_{\phi\phi}$ and $\alpha_{zz}$ have opposite signs. The amplitudes of the azimuthal $\alpha$-effect computed with the cylindrical 3D MHD code are so small that the operation of an $\alpha\Omega$ dynamo on the basis of the current-driven, kink-type instabilities of toroidal fields is highly questionable. In any case the low value of the $\alpha$-effect would lead to very long growth times of a dynamo in the radiation zone of the Sun and early-type stars of the order of mega-years.Comment: 6 pages, 7 figures, submitted to MNRA

Credit card fraud detection by adaptive neural data mining

The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate

Neural data mining for credit card fraud detection

The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate

A typology of cooperation strategies in the telecommunication industry: An exploratory analysis and theoretical foundations

The value chain of the telecommunication industry is subject to a continuing disintegration which is caused by outsourced network operation, the provisioning of wholesale interfaces to competing service providers and the cooperative provisioning of broadband access. Thus, many companies regard cooperation as an element of cooperate strategy. In this paper we propose a cooperation topology for the telecommunication industry and identify drivers of cooperation based on the assessment of case studies. The results indicate that drivers of cooperation differ with respect to the cooperation direction and that the combination of complementary resources is the dominating driver of cooperation. --Cooperation,telecommunication,typology of cooperation strategies,transaction costs

Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria

The detection of multiple outliers can be interpreted as a model selection problem. Models that can be selected are the null model, which indicates an outlier free set of observations, or a class of alternative models, which contain a set of additional bias parameters. A common way to select the right model is by using a statistical hypothesis test. In geodesy data snooping is most popular. Another approach arises from information theory. Here, the Akaike information criterion (AIC) is used to select an appropriate model for a given set of observations. The AIC is based on the Kullback-Leibler divergence, which describes the discrepancy between the model candidates. Both approaches are discussed and applied to test problems: the fitting of a straight line and a geodetic network. Some relationships between data snooping and information criteria are discussed. When compared, it turns out that the information criteria approach is more simple and elegant. Along with AIC there are many alternative information criteria for selecting different outliers, and it is not clear which one is optimal

Tracing Real-Time Transnational Hydrologic Sensitivity and Crop Irrigation in the Upper Rhine Area over the Exceptional Drought Episode 2018–2020 Using Open Source Sentinel-2 Data

Climate and regional land-use and landcover change (LUCC) impact the ecosystem of the Upper Rhine Area (URA) and transform large parts of the landscape into strongly irrigated agricultural cropland. The increase of long-term drought periods and the trend towards low summer precipitation totals trigger an increase in groundwater scarcity and amplify the negative effects of extensive irrigation purposes and freshwater consumption in a hydrologically sensitive region in Central Europe. This article presents qualitative transnational open source remote sensing temporal series of vegetation indices (NDVI) and groundwater level development to tracing near real-time vegetation change and socio-ecological feedbacks during periods of climate extremes in the Upper Rhine Area (2018–2020). Increased freshwater consumption caused a dramatic drop in groundwater availability, which eventually led to a strong degradation of the vegetation canopy and caused governmental regulations in July 2020. Assessing vegetation growth behavior and linking groundwater reactions in the URA through open source satellite data contributes to a rapidly accessible understanding of the ecosystem’s feedbacks on the local to the transnational scale and further enables risk management and eco-political regulations in current and future decisionmaking processes.Climate and regional land-use and landcover change (LUCC) impact the ecosystem of the Upper Rhine Area (URA) and transform large parts of the landscape into strongly irrigated agricultural cropland. The increase of long-term drought periods and the trend towards low summer precipitation totals trigger an increase in groundwater scarcity and amplify the negative effects of extensive irrigation purposes and freshwater consumption in a hydrologically sensitive region in Central Europe. This article presents qualitative transnational open source remote sensing temporal series of vegetation indices (NDVI) and groundwater level development to tracing near real-time vegetation change and socio-ecological feedbacks during periods of climate extremes in the Upper Rhine Area (2018–2020). Increased freshwater consumption caused a dramatic drop in groundwater availability, which eventually led to a strong degradation of the vegetation canopy and caused governmental regulations in July 2020. Assessing vegetation growth behavior and linking groundwater reactions in the URA through open source satellite data contributes to a rapidly accessible understanding of the ecosystem’s feedbacks on the local to the transnational scale and further enables risk management and eco-political regulations in current and future decisionmaking processes