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There has been an increase in recognition of the important role that the boundary layer turbulent flow structure has on wake recovery and concomitant wind farm efficiency. Most research thus far has focused on onshore wind farms, in which the ground surface is static. With the expected growth of offshore wind farms, there is increased interest in turbulent flow structures above wavy, moving surfaces and their effects on offshore wind farms. In this study, experiments are performed to analyze the turbulent structure above the waves in the wake of a fixed-bottom model wind farm, with special emphasis on the conditional averaged Reynolds stresses, using a quadrant analysis. Phase-averaged profiles show a correlation between the Reynolds shear stresses and the curvature of the waves. Using a quadrant analysis, Reynolds stress dependence on the wave phase is observed in the phase-dependent vertical position of the turbulence events. This trend is primarily seen in quadrants 1 and 3 (correlated outward and inward interactions). Quantification of the correlation between the Reynolds shear stress events and the surface waves provides insight into the turbulent flow mechanisms that influence wake recovery throughout the wake region and should be taken into consideration in wind turbine operation and placement. 

An important task in human-computer interaction is to rank speech samples according to their expressive content. A preference learning framework is appropriate for obtaining an emotional rank for a set of speech samples. However, obtaining reliable labels for training a preference learning framework is a challenging task. Most existing databases provide sentence-level absolute attribute scores annotated by multiple raters, which have to be transformed to obtain preference labels. Previous studies have shown that evaluators anchor their absolute assessments on previously annotated samples. Hence, this study proposes a novel formulation for obtaining preference learning labels by only considering annotation trends assigned by a rater to consecutive samples within an evaluation session. The experiments show that the use of the proposed anchor-based ordinal labels leads to significantly better performance than models trained using existing alternative labels. 

The field of speech emotion recognition (SER) aims to create scientifically rigorous systems that can reliably characterize emotional behaviors expressed in speech. A key aspect for building SER systems is to obtain emotional data that is both reliable and reproducible for practitioners. However, academic researchers encounter difficulties in accessing or collecting naturalistic, large-scale, reliable emotional recordings. Also, the best practices for data collection are not necessarily described or shared when presenting emotional corpora. To address this issue, the paper proposes the creation of an affective naturalistic database consortium (AndC) that can encourage multidisciplinary cooperation among researchers and practitioners in the field of affective computing. This paper’s contribution is twofold. First, it proposes the design of the AndC with a customizable-standard framework for intelligently-controlled emotional data collection. The focus is on leveraging naturalistic spontaneous record- ings available on audio-sharing websites. Second, it presents as a case study the development of a naturalistic large-scale Taiwanese Mandarin podcast corpus using the customizable- standard intelligently-controlled framework. The AndC will en- able research groups to effectively collect data using the provided pipeline and to contribute with alternative algorithms or data collection protocols. 

The performance of facial expression recognition (FER) systems has improved with recent advances in machine learning. While studies have reported impressive accuracies in detecting emotion from posed expressions in static images, there are still important challenges in developing FER systems for videos, especially in the presence of speech. Speech articulation modulates the orofacial area, changing the facial appearance. These facial movements induced by speech introduce noise, reducing the performance of an FER system. Solving this problem is important if we aim to study more naturalistic environment or applications in the wild. We propose a novel approach to compensate for lexical information that does not require phonetic information during inference. The approach relies on a style extractor model, which creates emotional-to-neutral transformations. The transformed facial representations are spatially contrasted with the original faces, highlighting the emotional information conveyed in the video. The results demonstrate that adding the proposed style extractor model to a dynamic FER system improves the performance by 7% (absolute) compared to a similar model with no style extractor. This novel feature representation also improves the generaliza- tion of the model. 

Geophysical flows occur over a large range of scales, with Reynolds numbers and Richardson numbers varying over several orders of magnitude. For this study, jets of different densities were ejected vertically into a large ambient region, considering conditions relevant to some geophysical phenomena. Using particle image velocimetry, the velocity fields were measured for three different gases exhausting into air – specifically helium, air and argon. Measurements focused on both the jet core and the entrained ambient. Experiments considered relatively low Reynolds numbers from approximately 1500 to 10 000 with Richardson numbers near 0.001 in magnitude. These included a variety of flow responses, notably a nearly laminar jet, turbulent jets and a transitioning jet in between. Several features were studied, including the jet development, the local entrainment ratio, the turbulent Reynolds stresses and the eddy strength. Compared to a fully turbulent jet, the transitioning jet showed up to 50 % higher local entrainment and more significant turbulent fluctuations. For this condition, the eddies were non-axisymmetric and larger than the exit radius. For turbulent jets, the eddies were initially smaller and axisymmetric while growing with the shear layer. At lower turbulent Reynolds number, the turbulent stresses were more than 50 % higher than at higher turbulent Reynolds number. In either case, the low-density jet developed faster than a comparable non-buoyant jet. Quadrant analysis and proper orthogonal decomposition were also utilized for insight into the entrainment of the jet, as well as to assess the energy distribution with respect to the number of eigenmodes. Reynolds shear stresses were dominant in Q1 and Q3 and exhibited negligible contributions from the remaining two quadrants. Both analysis techniques showed that the development of stresses downstream was dependent on the Reynolds number while the spanwise location of the stresses depended on the Richardson number.