Hand and Eye Dominance in Sport: Are Cricket Batters Taught to Bat Back-to-Front?

Background: When first learning to bimanually use a tool to hit a target (e.g., when chopping wood or hitting a golf ball), most people assume a stance that is dictated by their dominant hand. By convention, this means that a ‘right-handed’ or ‘left-handed’ stance that places the dominant hand closer to the striking end of the tool is adopted in many sports. Objective: The aim of this study was to investigate whether the conventional stance used for bimanual hitting provides the best chance of developing expertise in that task. Methods: Our study included 43 professional (international/first-class) and 93 inexperienced (<5 years’ experience) cricket batsmen. We determined their batting stance (plus hand and eye dominance) to compare the proportion of batters who adopted a reversed stance when batting (that is, the opposite stance to that expected based on their handedness). Results: We found that cricket batsmen who adopted a reversed stance had a stunning advantage, with professional batsmen 7.1 times more likely to adopt a reversed stance than inexperienced batsmen, independent of whether they batted right or left handed or the position of their dominant eye. Conclusion: Findings imply that batsmen who adopt a conventional stance may inadvertently be batting ‘back-to-front’ and have a significant disadvantage in the game. Moreover, the results may generalize more widely, bringing into question the way in which other bimanual sporting actions are taught and performed

Adaptive multiscale superpixel embedding convolutional neural network for land use classification

Currently, a large number of remote sensing images with different resolutions are available for Earth observation and land monitoring, which are inevitably demanding intelligent analysis techniques for accurately identifying and classifying land use (LU). This article proposes an adaptive multiscale superpixel embedding convolutional neural network architecture (AMUSE-CNN) for tackling LU classification. Initially, the images are parsed via the superpixel representation so that the object-based analysis (via a superpixel embedding convolutional neural network scheme) can be carried out with the pixel context and neighborhood information. Then, a multiscale convolutional neural network (MS-CNN) is proposed to classify the superpixel-based images by identifying object features across a variety of scales simultaneously, in which multiple window sizes are used to fit to the various geometries of different LU classes. Furthermore, a proposed adaptive strategy is applied to best exert the classification capability of the MS-CNN. Subsequently, two modules are developed to fully implement the AMUSE-CNN architecture. More specifically, Module I is to determine the most suitable classes for each window size (scale) by applying majority voting to a series of MS-CNNs Module II carries out the classification of the classes identified in Module I for the given scale used in the MS-CNN and, therefore, complete the LU classification of the entire classes. The proposed AMUSE-CNN architecture is both quantitatively and qualitatively validated using remote sensing data collected from two cities, Kano and Lagos in Nigeria, due to the spatially complex LU distribution. Experimental results show the superior performance of our approach against several state-of-the-art techniques

Towards the Geometry of Model Sensitivity: An Illustration

In statistical practice model building, sensitivity and uncertainty are major concerns of the analyst. This paper looks at these issues from an information geometric point of view. Here, we define sensitivity to mean understanding how inference about a problem of interest changes with perturbations of the model. In particular it is an example of what we call computational information geometry. The embedding of simple models in much larger information geometric spaces is shown to illuminate these critically important issues