Configurations with few crossings in topological graphs

AbstractIn this paper we study the problem of computing subgraphs of a certain configuration in a given topological graph G such that the number of crossings in the subgraph is minimum. The configurations that we consider are spanning trees, s–t paths, cycles, matchings, and κ-factors for κ∈{1,2}. We show that it is NP-hard to approximate the minimum number of crossings for these configurations within a factor of k1−ε for any ε>0, where k is the number of crossings in G.We then give a simple fixed-parameter algorithm that tests in O⋆(2k) time whether G has a crossing-free configuration for any of the above, where the O⋆-notation neglects polynomial terms. For some configurations we have faster algorithms. The respective running times are O⋆(1.9999992k) for spanning trees and O⋆((3)k) for s-t paths and cycles. For spanning trees we also have an O⋆(1.968k)-time Monte-Carlo algorithm. Each O⋆(βk)-time decision algorithm can be turned into an O⋆((β+1)k)-time optimization algorithm that computes a configuration with the minimum number of crossings

Proof of concept for wind turbine wake investigations with the RPAS SUMO

The Small Unmanned Meteorological Observer (SUMO) has been operated in the vicinity of five research turbines of the Energy Research Centre of the Netherlands (ECN) at the test site Wieringermeer. The intention of the campaign was to proof the capability of the system for wind turbine wake investigations also for situations above rated wind speed. In rather high wind conditions of 15-20 ms−1 on May 10, 2014, the system showed a satisfying in-flight behavior and performed five racetrack flights. The racetrack patterns flown parallel to the row of the five turbines (four flights downstream the turbine row, one upstream) enable the characterization and investigation of the strength, i.e. the reduction in the mean wind, and structure, i.e. the horizontal extension and turbulent kinetic energy (TKE) distribution of single turbine wakes.publishedVersio

Configuations with few crossings in topological graphs

In this paper we study the problem of computing subgraphs of a certain configuration in a given topological graph G such that the number of crossings in the subgraph is minimum. The configurations that we consider are spanning trees, s-t paths, cycles, matchings, and kappa-factors for kappa in {1,2}. We show that it is NP-hard to approximate the minimum number of crossings for these configurations within a factor of k^(1-epsilon) for any epsilon > 0, where k is the number of crossings in G. We then show that the problems are fixed-parameter tractable if we use the number of crossings in the given graph as the parameter. Finally we present a mixed-integer linear program formulation for each problem and a simple but effective heuristic for spanning trees

Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial

BackgroundMental health problems are prevalent among people with diabetes, yet often under-diagnosed. Smart sensing, utilizing passively collected digital markers through digital devices, is an innovative diagnostic approach that can support mental health screening and intervention. However, the acceptance of this technology remains unclear. Grounded on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study aimed to investigate (1) the acceptance of smart sensing in a diabetes sample, (2) the determinants of acceptance, and (3) the effectiveness of an acceptance facilitating intervention (AFI).MethodsA total of N = 132 participants with diabetes were randomized to an intervention group (IG) or a control group (CG). The IG received a video-based AFI on smart sensing and the CG received an educational video on mindfulness. Acceptance and its potential determinants were assessed through an online questionnaire as a single post-measurement. The self-reported behavioral intention, interest in using a smart sensing application and installation of a smart sensing application were assessed as outcomes. The data were analyzed using latent structural equation modeling and t-tests.ResultsThe acceptance of smart sensing at baseline was average (M = 12.64, SD = 4.24) with 27.8% showing low, 40.3% moderate, and 31.9% high acceptance. Performance expectancy (γ = 0.64, p < 0.001), social influence (γ = 0.23, p = .032) and trust (γ = 0.27, p = .040) were identified as potential determinants of acceptance, explaining 84% of the variance. SEM model fit was acceptable (RMSEA = 0.073, SRMR = 0.059). The intervention did not significantly impact acceptance (γ = 0.25, 95%-CI: −0.16–0.65, p = .233), interest (OR = 0.76, 95% CI: 0.38–1.52, p = .445) or app installation rates (OR = 1.13, 95% CI: 0.47–2.73, p = .777).DiscussionThe high variance in acceptance supports a need for acceptance facilitating procedures. The analyzed model supported performance expectancy, social influence, and trust as potential determinants of smart sensing acceptance; perceived benefit was the most influential factor towards acceptance. The AFI was not significant. Future research should further explore factors contributing to smart sensing acceptance and address implementation barriers

Searching for Realizations of Finite Metric Spaces in Tight Spans

An important problem that commonly arises in areas such as internet traffic-flow analysis, phylogenetics and electrical circuit design, is to find a representation of any given metric $D$ on a finite set by an edge-weighted graph, such that the total edge length of the graph is minimum over all such graphs. Such a graph is called an optimal realization and finding such realizations is known to be NP-hard. Recently Varone presented a heuristic greedy algorithm for computing optimal realizations. Here we present an alternative heuristic that exploits the relationship between realizations of the metric $D$ and its so-called tight span $T_D$. The tight span $T_D$ is a canonical polytopal complex that can be associated to $D$, and our approach explores parts of $T_D$ for realizations in a way that is similar to the classical simplex algorithm. We also provide computational results illustrating the performance of our approach for different types of metrics, including $l_1$-distances and two-decomposable metrics for which it is provably possible to find optimal realizations in their tight spans.Comment: 20 pages, 3 figure

Learning nitrogen-vacancy electron spin dynamics on a silicon quantum photonic simulator

We present the experimental demonstration of quantum Hamiltonian learning. Using an integrated silicon-photonics quantum simulator with the classical machine learning technique, we successfully learn the Hamiltonian dynamics of a diamond nitrogen-vacancy center's electron ground-state spin

Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond

Summary form only given. The efficient characterization and validation of the underlying model of a quantum physical system is a central challenge in the development of quantum devices and for our understanding of foundational quantum physics. However, the impossibility to efficiently predict the behaviour of complex quantum models on classical machines makes this challenge to be intractable to classical approaches. Quantum Hamiltonian Learning (QHL) [1, 2] combines the capabilities of quantum information processing and classical machine learning to allow the efficient characterisation of the model of quantum systems. In QHL the behaviour of a quantum Hamiltonian model is efficiently predicted by a quantum simulator, and the predictions are contrasted with the data obtained from the quantum system to infer the system Hamiltonian via Bayesian methods

Advanced electron cyclotron heating and current drive experiments on the stellarator Wendelstein 7-X

During the first operational phase (OP 1.1) of Wendelstein 7-X (W7-X) electron cyclotron resonance heating (ECRH) was the exclusive heating method and provided plasma start-up, wall conditioning, heating and current drive. Six gyrotrons were commissioned for OP1.1 and used in parallel for plasma operation with a power of up to 4.3 MW. During standard X2-heating the spatially localized power deposition with high power density allowed controlling the radial profiles of the electron temperature and the rotational transform. Even though W7-X was not fully equipped with first wall tiles and operated with a graphite limiter instead of a divertor, electron densities of n e > 3·1019 m-3 could be achieved at electron temperatures of several keV and ion temperatures above 2 keV. These plasma parameters allowed the first demonstration of a multipath O2-heating scenario, which is envisaged for safe operation near the X-cutoff-density of 1.2·1020 m-3 after full commissioning of the ECRH system in the next operation phase OP1.2