Robustness - a challenge also for the 21st century: A review of robustness phenomena in technical, biological and social systems as well as robust approaches in engineering, computer science, operations research and decision aiding

Notions on robustness exist in many facets. They come from different disciplines and reflect different worldviews. Consequently, they contradict each other very often, which makes the term less applicable in a general context. Robustness approaches are often limited to specific problems for which they have been developed. This means, notions and definitions might reveal to be wrong if put into another domain of validity, i.e. context. A definition might be correct in a specific context but need not hold in another. Therefore, in order to be able to speak of robustness we need to specify the domain of validity, i.e. system, property and uncertainty of interest. As proofed by Ho et al. in an optimization context with finite and discrete domains, without prior knowledge about the problem there exists no solution what so ever which is more robust than any other. Similar to the results of the No Free Lunch Theorems of Optimization (NLFTs) we have to exploit the problem structure in order to make a solution more robust. This optimization problem is directly linked to a robustness/fragility tradeoff which has been observed in many contexts, e.g. 'robust, yet fragile' property of HOT (Highly Optimized Tolerance) systems. Another issue is that robustness is tightly bounded to other phenomena like complexity for which themselves exist no clear definition or theoretical framework. Consequently, this review rather tries to find common aspects within many different approaches and phenomena than to build a general theorem for robustness, which anyhow might not exist because complex phenomena often need to be described from a pluralistic view to address as many aspects of a phenomenon as possible. First, many different robustness problems have been reviewed from many different disciplines. Second, different common aspects will be discussed, in particular the relationship of functional and structural properties. This paper argues that robustness phenomena are also a challenge for the 21st century. It is a useful quality of a model or system in terms of the 'maintenance of some desired system characteristics despite fluctuations in the behaviour of its component parts or its environment' (s. [Carlson and Doyle, 2002], p. 2). We define robustness phenomena as solution with balanced tradeoffs and robust design principles and robustness measures as means to balance tradeoffs. --

Procedure to Approximately Estimate the Uncertainty of Material Ratio Parameters due to Inhomogeneity of Surface Roughness

Roughness parameters that characterize contacting surfaces with regard to friction and wear are commonly stated without uncertainties, or with an uncertainty only taking into account a very limited amount of aspects such as repeatability of reproducibility (homogeneity) of the specimen. This makes it difficult to discriminate between different values of single roughness parameters. Therefore uncertainty assessment methods are required that take all relevant aspects into account. In the literature this is scarcely performed and examples specific for parameters used in friction and wear are not yet given. We propose a procedure to derive the uncertainty from a single profile employing a statistical method that is based on the statistical moments of the amplitude distribution and the autocorrelation length of the profile. To show the possibilities and the limitations of this method we compare the uncertainty derived from a single profile with that derived from a high statistics experiment.Comment: submitted to Meas. Sci. Technol., 12 figure

From Chemistry to Functionality: Trends for the Length Dependence of the Thermopower in Molecular Junctions

We present a systematic ab-initio study of the length dependence of the thermopower in molecular junctions. The systems under consideration are small saturated and conjugated molecular chains of varying length attached to gold electrodes via a number of different binding groups. Different scenarios are observed: linearly increasing and decreasing thermopower as function of the chain length as well as positive and negative values for the contact thermopower. Also deviation from the linear behaviour is found. The trends can be explained by details of the transmission, in particular the presence, position and shape of resonances from gateway states. We find that these gateway states do not only determine the contact thermopower, but can also have a large influence on the length-dependence itself. This demonstrates that simple models for electron transport do not apply in general and that chemical trends are hard to predict. Furthermore, we discuss the limits of our approach based on Density Functional Theory and compare to more sophisticated methods like self-energy corrections and the GW theory

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult to interpret. This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time. To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling. This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance. We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original. Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space. This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty. We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set. Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.Comment: Accepted for publication at the Seventh International Conference on Learning Representations (ICLR 2019

Gamma-aminobutyric acid Metabolism in Arabidopsis thaliana

The four-carbon, non-protein amino acid gamma aminobutyric acid (GABA) is found in all species, where it is involved in various signaling processes. GABA is best characterized as the main inhibitory neurotransmitter in mammalia. On the contrary, work in plants focused mainly on a metabolic role; just some recent findings indicate GABA having a possible signaling function in plants as well. In Arabidopsis thaliana, mutants of both catabolic genes (GABA transaminase (GABA-T) and succinic semialdehyde dehydrogenase (SSADH)) display phenotypic deviations to wild type; the former grow vegetatively like wild type, but are less fertile due to a misguidance of pollen tubes. The latter are severely affected in growth and development, probably induced by the accumulation of GABA-shunt metabolites and/or reactive oxygen intermediates. The ssadh phenotype can be suppressed by interrupting the GABA-shunt upstream of the SSADH function. Based on this, two topics should be covered in this thesis. First, the substance causing the ssadh phenotype should be identified; hence, wild type along with GABA-shunt single and double mutants were grown on media containing GABA-shunt intermediates, and plant growth as well as metabolite content in leaf extracts was examined. Second, further genes involved in regulation or function of the GABA-shunt or in sensing of GABA-shunt related metabolites should be identified. Therefore, a population of mutagenized ssadh plants was screened for suppressors; among several stable, non-segregating lines, two new gaba-t alleles were isolated. All GABA-shunt intermediates influence plant growth and development. First, GABA enhances plant growth by inducing expression of nitrate uptake transporters, only very high cellular concentrations have an inhibitory effect on plant growth. Second, succinic semialdehyde (SSA) considerably reduces plant growth when applied with the growth media, and higher concentrations (0.8 mM and more in the media) induce dedifferentiation mainly of hypocotyl cells in a yet unknown manner. Finally, the enzymatic reduction of SSA produces gamma-hydroxybutyric acid (GHB), which leads to reduced root growth in Arabidopsis without major effects on rosette size when accumulating in plant tissue

Veränderung der Aufgabenkultur

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Conductance of Atomic-Sized Lead Contacts in an Electrochemical Environment

Atomic-sized lead (Pb) contacts are deposited and dissolved in an electrochemical environment, and their transport properties are measured. Due to the electrochemical fabrication process, we obtain mechanically unstrained contacts and conductance histograms with sharply resolved, individual peaks. Charge transport calculations based on density functional theory (DFT) for various ideal Pb contact geometries are in good agreement with the experimental results. Depending on the atomic configuration, single-atom-wide contacts of one and the same metal yield very different conductance values.Comment: 5 pages, 4 figure

How dielectric screening in two-dimensional crystals affects the convergence of excited-state calculations: Monolayer MoS₂

We present first-principles many-body calculations of the dielectric constant, quasiparticle band structure, and optical absorption spectrum of monolayer MoS$_2$ using a supercell approach. As the separation between the periodically repeated layers is increased, the dielectric function of the layer develops a strong $q$ dependence around $q=0$. This implies that denser $k$-point grids are required to converge the band gap and exciton binding energies when large supercells are used. In the limit of infinite layer separation, here obtained using a truncated Coulomb interaction, a $45\times45$ $k$-point grid is needed to converge the G$_0$W$_0$ band gap and exciton energy to within 0.1 eV. We provide an extensive comparison with previous studies and explain agreement and variations in the results. It is demonstrated that too coarse $k$-point sampling and the interactions between the repeated layers have opposite effects on the band gap and exciton energy, leading to a fortuitous error cancellation in the previously published results.Comment: 10 pages, 11 figure