Adaptive Regularization in Neural Network Modeling

. In this paper we address the important problem of optimizing regularization parameters in neural network modeling. The suggested optimization scheme is an extended version of the recently presented algorithm [24]. The idea is to minimize an empirical estimate -- like the cross-validation estimate -- of the generalization error with respect to regularization parameters. This is done by employing a simple iterative gradient descent scheme using virtually no additional programming overhead compared to standard training. Experiments with feed-forward neural network models for time series prediction and classification tasks showed the viability and robustness of the algorithm. Moreover, we provided some simple theoretical examples in order to illustrate the potential and limitations of the proposed regularization framework. 1 Introduction Neural networks are flexible tools for time series processing and pattern recognition. By increasing the number of hidden neurons in a 2-layer architec..

First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

Fog and low clouds (FLCs) are a typical feature along the southwestern African coast, especially in the central Namib, where fog constitutes a valuable resource of water for many ecosystems. In this study, a novel algorithm is presented to detect FLCs over land from geostationary satellite data using only infrared observations. The algorithm is the first of its kind as it is stationary in time and thus able to reveal a detailed view of the diurnal and spatial patterns of FLCs in the Namib region. A validation against net radiation measurements from a station network in the central Namib reveals a high overall accuracy with a probability of detection of 94%, a false-alarm rate of 12% and an overall correctness of classification of 97%. The average timing and persistence of FLCs seem to depend on the distance to the coast, suggesting that the region is dominated by advection-driven FLCs. While the algorithm is applied to study Namib-region fog and low clouds, it is designed to be transferable to other regions and can be used to retrieve long-term data sets

How thermodynamic environments control stratocumulus microphysics and interactions with aerosols

Aerosol–cloud interactions are central to climate system changes and depend on meteorological conditions. This study identifies distinct thermodynamic regimes and proposes a conceptual framework for interpreting aerosol effects. In the analysis, ten years (2003–2012) of daily satellite-derived aerosol and cloud products are combined with reanalysis data to identify factors controlling Southeast Atlantic stratocumulus microphysics. Considering the seasonal influence of aerosol input from biomass burning, thermodynamic environments that feature contrasting microphysical cloud properties and aerosol–cloud relations are classified. While aerosol impact is stronger in unstable environments, it is mostly confined to situations with low aerosol loading (aerosol index AI ≲ 0.15), implying a saturation of aerosol effects. Situations with high aerosol loading are associated with weaker, seasonally contrasting aerosol-droplet size relationships, likely caused by thermodynamically induced processes and aerosol swelling

Molecular mechanisms of in vivo metal chelation: implications for clinical treatment of metal intoxications.

Successful in vivo chelation treatment of metal intoxication requires that a significant fraction of the administered chelator in fact chelate the toxic metal. This depends on metal, chelator, and organism-related factors (e.g., ionic diameter, ring size and deformability, hardness/softness of electron donors and acceptors, route of administration, bioavailability, metabolism, organ and intra/extracellular compartmentalization, and excretion). In vivo chelation is not necessarily an equilibrium reaction, determined by the standard stability constant, because rate effects and ligand exchange reactions considerably influence complex formation. Hydrophilic chelators most effectively promote renal metal excretion, but they complex intracellular metal deposits inefficiently. Lipophilic chelators can decrease intracellular stores but may redistribute toxic metals to, for example, the brain. In chronic metal-induced disease, where life-long chelation may be necessary, possible toxicity or side effects of the administered chelator may be limiting. The metal selectivity of chelators is important because of the risk of depletion of the patient's stores of essential metals. Dimercaptosuccinic acid and dimercaptopropionic sulfonate have gained more general acceptance among clinicians, undoubtedly improving the management of many human metal intoxications, including lead, arsenic, and mercury compounds. Still, development of new safer chelators suited for long-term oral administration for chelation of metal deposits (mainly iron), is an important research challenge for the future

Plant Biomarker Pattern, Screening Programme for Phytochemical Differences in Plants Exposed to Stress

A screening programme is developed to investigate phytochemical differences in plants xposed to stress compared with non-exposed plants. The screening programme, in its resent form or in a more simplified form, can be utilized in several different areas as a preliminary broad screening. The screening programme covers the most general groups of compounds found in plants. The following groups of phytochemical compounds are included in the programme: Unspecific compounds, organic acids, lipids, phenolic compounds, carbohydrates, terpenoids and N-, S- and P-containing compounds