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With the rise of immersive media, advertisers have started to use 360° commercials to engage and persuade consumers. Two experiments were conducted to address research gaps and to validate the positive impact of 360° commercials in realistic settings. The first study (N = 62) compared the effects of 360° commercials using either a mobile cardboard head-mounted display (HMD) or a laptop. This experiment was conducted in the participants’ living rooms and incorporated individual feelings of cybersickness as a moderator. The participants who experienced the 360° commercial with the HMD reported higher spatial presence and product evaluation, but their purchase intentions were only increased when their reported cybersickness was low. The second experiment (N = 197) was conducted online and analyzed the impact of 360° commercials that were experienced with mobile (smartphone/tablet) or static (laptop/desktop) devices instead of HMDs. The positive effects of omnidirectional videos were stronger when participants used mobile devices.
Catastrophizing thoughts may contribute to the development of anxiety, but functional emotion regulation may help to improve treatment. No study so far directly compared up- and down-regulation of fear by cognitive reappraisal. Here, healthy individuals took part in a cued fear experiment, in which multiple pictures of faces were paired twice with an unpleasant scream or presented as safety stimuli. Participants (N = 47) were asked (within-subjects) to down-regulate, to up-regulate and to maintain their natural emotional response. Valence and arousal ratings indicated successful up- and down-regulation of the emotional experience, while heart rate and pupil dilation increased during up-regulation, but showed no reduction in down-regulation. State and trait anxiety correlated with evaluations of safety but not threat stimuli, which supports the role of deficient safety learning in anxiety. Reappraisal did not modulate this effect. In conclusion, this study reveals evidence for up-regulation effects in fear, which might be even more efficient than down-regulation on a physiological level and highlights the importance of catastrophizing thoughts for the maintenance of fear and anxiety.
In many real world settings, imbalanced data impedes model performance of learning algorithms, like neural networks, mostly for rare cases. This is especially problematic for tasks focusing on these rare occurrences. For example, when estimating precipitation, extreme rainfall events are scarce but important considering their potential consequences. While there are numerous well studied solutions for classification settings, most of them cannot be applied to regression easily. Of the few solutions for regression tasks, barely any have explored cost-sensitive learning which is known to have advantages compared to sampling-based methods in classification tasks. In this work, we propose a sample weighting approach for imbalanced regression datasets called DenseWeight and a cost-sensitive learning approach for neural network regression with imbalanced data called DenseLoss based on our weighting scheme. DenseWeight weights data points according to their target value rarities through kernel density estimation (KDE). DenseLoss adjusts each data point’s influence on the loss according to DenseWeight, giving rare data points more influence on model training compared to common data points. We show on multiple differently distributed datasets that DenseLoss significantly improves model performance for rare data points through its density-based weighting scheme. Additionally, we compare DenseLoss to the state-of-the-art method SMOGN, finding that our method mostly yields better performance. Our approach provides more control over model training as it enables us to actively decide on the trade-off between focusing on common or rare cases through a single hyperparameter, allowing the training of better models for rare data points.
A reformulation of cardinality-constrained optimization problems into continuous nonlinear optimization problems with an orthogonality-type constraint has gained some popularity during the last few years. Due to the special structure of the constraints, the reformulation violates many standard assumptions and therefore is often solved using specialized algorithms. In contrast to this, we investigate the viability of using a standard safeguarded multiplier penalty method without any problem-tailored modifications to solve the reformulated problem. We prove global convergence towards an (essentially strongly) stationary point under a suitable problem-tailored quasinormality constraint qualification. Numerical experiments illustrating the performance of the method in comparison to regularization-based approaches are provided.
We derive a multi-species BGK model with velocity-dependent collision frequency for a non-reactive, multi-component gas mixture. The model is derived by minimizing a weighted entropy under the constraint that the number of particles of each species, total momentum, and total energy are conserved. We prove that this minimization problem admits a unique solution for very general collision frequencies. Moreover, we prove that the model satisfies an H-Theorem and characterize the form of equilibrium.
In this paper we derive new results on multivariate extremes and D-norms. In particular we establish new characterizations of the multivariate max-domain of attraction property. The limit distribution of certain multivariate exceedances above high thresholds is derived, and the distribution of that generator of a D-norm on R\(^{d}\), whose components sum up to d, is obtained. Finally we introduce exchangeable D-norms and show that the set of exchangeable D-norms is a simplex.
We investigate the convergence of the proximal gradient method applied to control problems with non-smooth and non-convex control cost. Here, we focus on control cost functionals that promote sparsity, which includes functionals of L\(^{p}\)-type for p\in [0,1). We prove stationarity properties of weak limit points of the method. These properties are weaker than those provided by Pontryagin’s maximum principle and weaker than L-stationarity.
Sequential optimality conditions for cardinality-constrained optimization problems with applications
(2021)
Recently, a new approach to tackle cardinality-constrained optimization problems based on a continuous reformulation of the problem was proposed. Following this approach, we derive a problem-tailored sequential optimality condition, which is satisfied at every local minimizer without requiring any constraint qualification. We relate this condition to an existing M-type stationary concept by introducing a weak sequential constraint qualification based on a cone-continuity property. Finally, we present two algorithmic applications: We improve existing results for a known regularization method by proving that it generates limit points satisfying the aforementioned optimality conditions even if the subproblems are only solved inexactly. And we show that, under a suitable Kurdyka–Łojasiewicz-type assumption, any limit point of a standard (safeguarded) multiplier penalty method applied directly to the reformulated problem also satisfies the optimality condition. These results are stronger than corresponding ones known for the related class of mathematical programs with complementarity constraints.
Heart failure with preserved ejection fraction (HFpEF) is highly prevalent in patients on maintenance haemodialysis (HD) and lacks effective treatment. We investigated the effect of spironolactone on cardiac structure and function with a specific focus on diastolic function parameters. The MiREnDa trial examined the effect of 50 mg spironolactone once daily versus placebo on left ventricular mass index (LVMi) among 97 HD patients during 40 weeks of treatment. In this echocardiographic substudy, diastolic function was assessed using predefined structural and functional parameters including E/e'. Changes in the frequency of HFpEF were analysed using the comprehensive 'HFA-PEFF score'. Complete echocardiographic assessment was available in 65 individuals (59.5 ± 13.0 years, 21.5% female) with preserved left ventricular ejection fraction (LVEF > 50%). At baseline, mean E/e' was 15.2 ± 7.8 and 37 (56.9%) patients fulfilled the criteria of HFpEF according to the HFA-PEFF score. There was no significant difference in mean change of E/e' between the spironolactone group and the placebo group (+ 0.93 ± 5.39 vs. + 1.52 ± 5.94, p = 0.68) or in mean change of left atrial volume index (LAVi) (1.9 ± 12.3 ml/m\(^{2}\) vs. 1.7 ± 14.1 ml/m\(^{2}\), p = 0.89). Furthermore, spironolactone had no significant effect on mean change in LVMi (+ 0.8 ± 14.2 g/m\(^{2}\) vs. + 2.7 ± 15.9 g/m\(^{2}\); p = 0.72) or NT-proBNP (p = 0.96). Treatment with spironolactone did not alter HFA-PEFF score class compared with placebo (p = 0.63). Treatment with 50 mg of spironolactone for 40 weeks had no significant effect on diastolic function parameters in HD patients.
Planting non-native tree species, like Douglas fir in temperate European forest systems, is encouraged to mitigate effects of climate change. However, Douglas fir monocultures often revealed negative effects on forest biota, while effects of mixtures with native tree species on forest ecosystems are less well understood. We investigated effects of three tree species (Douglas fir, Norway spruce, native European beech), on ground beetles in temperate forests of Germany. Beetles were sampled in monocultures of each tree species and broadleaf-conifer mixtures with pitfall traps, and environmental variables were assessed around each trap. We used linear mixed models in a two-step procedure to disentangle effects of environment and tree species identity on ground beetle abundance, species richness, functional diversity and species assemblage structure. Contradictory to our expectations, ground beetle abundance and functional diversity was highest in pure Douglas fir stands, while tree mixtures showed intermediate values between pure coniferous and pure beech stands. The main drivers of these patterns were only partially dependent on tree species identity, which highlights the importance of structural features in forest stands. However, our study revealed distinct shifts in assemblage structure between pure beech and pure Douglas fir stands, which were only partially eased through mixture planting. Our findings suggest that effects of planting non-native trees on associated biodiversity can be actively modified by promoting beneficial forest structures. Nevertheless, integrating non-native tree species, even in mixtures with native trees, will invariably alter assemblage structures of associated biota, which can compromise conservation efforts targeted at typical species composition.