Complex-valued Adaptive System Identification via Low-Rank Tensor Decomposition

Machine learning (ML) and tensor-based methods have been of significant interest for the scientific community for the last few decades. In a previous work we presented a novel tensor-based system identification framework to ease the computational burden of tensor-only architectures while still being able to achieve exceptionally good performance. However, the derived approach only allows to process real-valued problems and is therefore not directly applicable on a wide range of signal processing and communications problems, which often deal with complex-valued systems. In this work we therefore derive two new architectures to allow the processing of complex-valued signals, and show that these extensions are able to surpass the trivial, complex-valued extension of the original architecture in terms of performance, while only requiring a slight overhead in computational resources to allow for complex-valued operations

Enhanced Nonlinear System Identification by Interpolating Low-Rank Tensors

Function approximation from input and output data is one of the most investigated problems in signal processing. This problem has been tackled with various signal processing and machine learning methods. Although tensors have a rich history upon numerous disciplines, tensor-based estimation has recently become of particular interest in system identification. In this paper we focus on the problem of adaptive nonlinear system identification solved with interpolated tensor methods. We introduce three novel approaches where we combine the existing tensor-based estimation techniques with multidimensional linear interpolation. To keep the reduced complexity, we stick to the concept where the algorithms employ a Wiener or Hammerstein structure and the tensors are combined with the well-known LMS algorithm. The update of the tensor is based on a stochastic gradient decent concept. Moreover, an appropriate step size normalization for the update of the tensors and the LMS supports the convergence. Finally, in several experiments we show that the proposed algorithms almost always clearly outperform the state-of-the-art methods with lower or comparable complexity.Comment: 12 pages, 4 figures, 3 table

Correcting Knowledge Base Assertions

The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or semantic confusion. We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using DBpedia and an enterprise medical KB

Vitamin D deficiency as a risk factor for dementia: a systematic review and meta-analysis

Abstract Background Sunlight exposure and high vitamin D status have been hypothesised to reduce the risk of developing dementia. The objective of our research was to determine whether lack of sunlight and hypovitaminosis D over time are associated with dementia. Methods We systematically searched MEDLINE (via PubMed), Cochrane Library, EMBASE, SCOPUS, Web of Science, ICONDA, and reference lists of pertinent review articles from 1990 to October 2015. We conducted random effects meta-analyses of published and unpublished data to evaluate the influence of sunlight exposure or vitamin D as a surrogate marker on dementia risk. Results We could not identify a single study investigating the association between sunlight exposure and dementia risk. Six cohort studies provided data on the effect of serum vitamin D concentration on dementia risk. A meta-analysis of five studies showed a higher risk for persons with serious vitamin D deficiency (<25\ua0nmol/L or 7\u201328\ua0nmol/L) compared to persons with sufficient vitamin D supply (\u226550\ua0nmol/L or 54\u2013159\ua0nmol/L) (point estimate 1.54; 95% CI 1.19\u20131.99, I 2 \u2009=\u200920%). The strength of evidence that serious vitamin D deficiency increases the risk of developing dementia, however, is very low due to the observational nature of included studies and their lack of adjustment for residual or important confounders (e.g. ApoE \u3b54 genotype), as well as the indirect relationship between Vitamin D concentrations as a surrogate for sunlight exposure and dementia risk. Conclusions The results of this systematic review show that low vitamin D levels might contribute to the development of dementia. Further research examining the direct and indirect relationship between sunlight exposure and dementia risk is needed. Such research should involve large-scale cohort studies with homogeneous and repeated assessment of vitamin D concentrations or sunlight exposure and dementia outcomes

PowerDynamics.jl—An experimentally validated open-source package for the dynamical analysis of power grids

PowerDynamics.jl is a Julia package for time-domain modeling of power grids that is specifically designed for the stability analysis of systems with high shares of renewable energies. It makes use of Julia’s state-of-the-art differential equation solvers and is highly performant even for systems with a large number of components. Further, it is compatible with Julia’s machine learning libraries and allows for the utilization of these methods for dynamical optimization and parameter fitting. The package comes with a number of predefined models for synchronous machines, transmission lines and inverter systems. However, the strict open-source approach and a macro-based user-interface also allows for an easy implementation of custom-built models which makes it especially interesting for the design and testing of new control strategies for distributed generation units. This paper presents how the modeling concept, implemented component models and fault scenarios have been experimentally tested against measurements in the microgrid lab of TECNALIA.This research has been performed using the ERIGrid Research Infrastructure and is part of a project that has received funding from the European Union’s Horizon 2020 Research and Innova-tion Programme under the Grant Agreement No. 654113. The support of the European Research Infrastructure ERIGrid and its partner TECNALIA is very much appreciated. We further acknowl-edge the Support by BMBF(CoNDyNet2FK.03EK3055A), the DFG (ExSyCo-Grid, 410409736), the Leibniz competition (T42/2018) and the Federal Ministry of Economics (MAriE, FK. 03Ei4012B)

Multilingualität und Linked Data

Cimiano P, Unger C. Multilingualität und Linked Data. In: Pellegrini T, Sack H, Auer S, eds. Linked Enterprise Data. Management und Bewirtschaftung vernetzter Unternehmensdaten mit Semantic Web Technologien. Berlin, Heidelberg: Springer; 2014: 153-175

Adaptive System Identification via Low-Rank Tensor Decompositi

Tensor-based estimation has been of particular interest of the scientific community for several years now. While showing promising results on system estimation and other tasks, one big downside is the tremendous amount of computational power and memory required – especially during training – to achieve satisfactory performance. We present a novel framework for different classes of nonlinear systems, that allows to significantly reduce the complexity by introducing a least-mean-squares block before, after, or between tensors to reduce the necessary dimensions and rank required to model a given system. Our simulations show promising results that outperform traditional tensor models, and achieve equal performance to comparable algorithms for all problems considered while requiring significantly less operations per time step than either of the state-of-the-art architectures