Predicting drag on rough surfaces by transfer learning of empirical correlations

Recent developments in neural networks have shown the potential of estimating drag on irregular rough surfaces. Nevertheless, the difficulty of obtaining a large high-fidelity dataset to train neural networks is deterring their use in practical applications. In this study, we propose a transfer learning framework to model the drag on irregular rough surfaces even with a limited amount of direct numerical simulations. We show that transfer learning of empirical correlations, reported in the literature, can significantly improve the performance of neural networks for drag prediction. This is because empirical correlations include ‘approximate knowledge’ of the drag dependency in high-fidelity physics. The ‘approximate knowledge’ allows neural networks to learn the surface statistics known to affect drag more efficiently. The developed framework can be applied to applications where acquiring a large dataset is difficult but empirical correlations have been reported.

The roles of lower- and higher-order surface statistics in tactile texture perception

Humans can haptically discriminate surface textures when there is a significant difference in the statistics of the surface profile. Previous studies on tactile texture discrimination have emphasized the perceptual effects of lower-order statistical features such as carving depth, inter-ridge distance, and anisotropy, which can be characterized by local amplitude spectra or spatial-frequency/orientation subband histograms. However, the real-world surfaces we encounter in everyday life also differ in the higher-order statistics, such as statistics about correlations of nearby spatial-frequencies/orientations. For another modality, vision, the human brain has the ability to utilize the textural differences in both higher- and lower-order image statistics. In this work, we examined whether the haptic texture perception can utilize higher-order surface statistics as visual texture perception does, by 3D-printing textured surfaces transcribed from different 'photos' of natural scenes such as stones and leaves. Even though the maximum carving depth was well above the haptic detection threshold, some texture pairs were hard to discriminate. Specifically, those texture pairs with similar amplitude spectra were difficult to discriminate, which suggests that the lower-order statistics have the dominant effect on tactile texture discrimination. To directly test the poor sensitivity of the tactile texture perception to higher-order surface statistics, we matched the lower-order statistics across different textures using a texture synthesis algorithm and found that haptic discrimination of the matched textures was nearly impossible unless the stimuli contained salient local features. We found no evidence for the ability of the human tactile system to use higher-order surface statistics for texture discrimination.

Does Every Study? Implementing Ordinal Constraint in Meta-Analysis

The most prominent goal when conducting a meta-analysis is to estimate the true effect size across a set of studies. This approach is problematic whenever the analyzed studies are inconsistent, i.e. some studies show an effect in the predicted direction while others show no effect and still others show an effect in the opposite direction. In case of such an inconsistency, the average effect may be a product of a mixture of mechanisms. The first question in any meta-analysis should therefore be whether all studies show an effect in the same direction. To tackle this question a model with multiple ordinal constraints is proposed---one constraint for each study in the set. This "every study" model is compared to a set of alternative models, such as an unconstrained model that predicts effects in both directions. If the ordinal constraints hold, one underlying mechanism may suffice to explain the results from all studies. A major implication is then that average effects become interpretable. We illustrate the model-comparison approach using Carbajal et al.'s (2020) meta-analysis on the familiar-word-recognition effect, show how predictor analyses can be incorporated in the approach, and provide R-code for interested researchers. As common in meta-analysis, only surface statistics (such as effect size and sample size) are provided from each study, and the modeling approach can be adapted to suit these conditions.

Surface Statistics of an Unknown Language Indicate How to Parse It

We introduce a novel framework for delexicalized dependency parsing in a new language. We show that useful features of the target language can be extracted automatically from an unparsed corpus, which consists only of gold part-of-speech (POS) sequences. Providing these features to our neural parser enables it to parse sequences like those in the corpus. Strikingly, our system has no supervision in the target language. Rather, it is a multilingual system that is trained end-to-end on a variety of other languages, so it learns a feature extractor that works well. We show experimentally across multiple languages: (1) Features computed from the unparsed corpus improve parsing accuracy. (2) Including thousands of synthetic languages in the training yields further improvement. (3) Despite being computed from unparsed corpora, our learned task-specific features beat previous work’s interpretable typological features that require parsed corpora or expert categorization of the language. Our best method improved attachment scores on held-out test languages by an average of 5.6 percentage points over past work that does not inspect the unparsed data (McDonald et al., 2011), and by 20.7 points over past “grammar induction” work that does not use training languages (Naseem et al., 2010).

Unsicherheit von Rauheitskennwerten in der Praxis*/Uncertainty of roughness parameters in practice

In der messtechnischen Praxis zeigt sich, dass Rauheitskennwerte unsicher sind. Dabei machen nicht die geläufigen Einflüsse wie das Messgerät oder die Umgebung den Hauptanteil der Unsicherheit aus, sondern die statistisch verteilte Struktur der Oberfläche. Dazu wurde eine Datenbank mit detaillierten Messungen von Zylinderlaufbahnen erstellt. Anhand der Auswertung vieler Messstrecken kann die Auswirkung der Statistik der Oberfläche auf verschiedene Rauheitskennwerte untersucht werden.   In practical measurements, it is apparent that roughness parameters are uncertain. Main causes of uncertainty are not familiar sources such as measurement devices or environmental effects but the statistically distributed surface structure. As a demonstrator, a database with detailed measurements of cylinder working surfaces has been created. Based on the evaluation of many profiles, the influence of the surface statistics on roughness parameters can be investigated.