Travelling on Graphs with Small Highway Dimension

We study the Travelling Salesperson (TSP) and the Steiner Tree problem (STP) in graphs of low highway dimension. This graph parameter was introduced by Abraham et al. [SODA 2010] as a model for transportation networks, on which TSP and STP naturally occur for various applications in logistics. It was previously shown [Feldmann et al. ICALP 2015] that these problems admit a quasi-polynomial time approximation scheme (QPTAS) on graphs of constant highway dimension. We demonstrate that a significant improvement is possible in the special case when the highway dimension is 1, for which we present a fully-polynomial time approximation scheme (FPTAS). We also prove that STP is weakly NP-hard for these restricted graphs. For TSP we show NP-hardness for graphs of highway dimension 6, which answers an open problem posed in [Feldmann et al. ICALP 2015]

Computational Cancer Biology: An Evolutionary Perspective

ISSN:1553-734XISSN:1553-735

Holographic Vitrification

We establish the existence of stable and metastable stationary black hole bound states at finite temperature and chemical potentials in global and planar four-dimensional asymptotically anti-de Sitter space. We determine a number of features of their holographic duals and argue they represent structural glasses. We map out their thermodynamic landscape in the probe approximation, and show their relaxation dynamics exhibits logarithmic aging, with aging rates determined by the distribution of barriers.Comment: 100 pages, 25 figure

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

EFSA NDA Panel (EFSA Panel on Dietetic Products, Nutrition and Allergies), 2013 . Scientific opinion on Dietary Reference Values for fluoride

Following a request from the European Commission, the Panel on Dietetic Products, Nutrition and Allergies (NDA) derived Dietary Reference Values (DRVs) for fluoride, which are provided as Adequate Intake (AI) from all sources, including non-dietary sources. Fluoride is not an essential nutrient. Therefore, no Average Requirement for the performance of essential physiological functions can be defined. Nevertheless, the Panel considered that the setting of an AI is appropriate because of the beneficial effects of dietary fluoride on prevention of dental caries. The AI is based on epidemiological studies (performed before the 1970s) showing an inverse relationship between the fluoride concentration of water and caries prevalence. As the basis for defining the AI, estimates of mean fluoride intakes of children via diet and drinking water with fluoride concentrations at which the caries preventive effect approached its maximum whilst the risk of dental fluorosis approached its minimum were chosen. Except for one confirmatory longitudinal study in US children, more recent studies were not taken into account as they did not provide information on total dietary fluoride intake, were potentially confounded by the use of fluoride-containing dental hygiene products, and did not permit a conclusion to be drawn on a dose-response relationship between fluoride intake and caries risk. The AI of fluoride from all sources (including non-dietary sources) is 0.05 mg/kg body weight per day for both children and adults, including pregnant and lactating women. For pregnant and lactating women, the AI is based on the body weight before pregnancy and lactation. Reliable and representative data on the total fluoride intake of the European population are not available

A precision study of the fine tuning in the DiracNMSSM

Recently the DiracNMSSM has been proposed as a possible solution to reduce the fine tuning in supersymmetry. We determine the degree of fine tuning needed in the DiracNMSSM with and without non-universal gaugino masses and compare it with the fine tuning in the GNMSSM. To apply reasonable cuts on the allowed parameter regions we perform a precise calculation of the Higgs mass. In addition, we include the limits from direct SUSY searches and dark matter abundance. We find that both models are comparable in terms of fine tuning, with the minimal fine tuning in the GNMSSM slightly smaller.Comment: 20 pages + appendices, 10 figure

A Least Mean Square Algorithm Based Single-Phase Grid Voltage Parameters Estimation Method

© 2019 IEEE. Grid-synchronization may be the most significant task in order to integrate renewable energy sources (RESs) and electric vehicles (EVs) into the power grid. The popular technique for grid synchronization is the power based phase locked loop (PLL). The major challenges that one encounters to design a robust power based PLL is the filter design inside the power based PLL control loop, and estimating the grid voltage parameters under frequency drift conditions. A wide bandwidth should be considered during filter design if a wide range of frequency variations are predicted in the grid voltage. The traditional filters cause a large phase delay if a wide bandwidth is considered during filter design. As a result, it degrades the transient performance of the power based PLL. In order to improve the transient performance of the PLL, this paper adopted a Fourier linear combiner (FLC) filter inside the PLL control loop. Moreover, a feedback loop is used to make the FLC frequency adaptive in order to estimate the grid voltage parameter when grid frequency drift occurs. Simulation and experimental results are provided to verify the proposed technique