Research has revealed that individual soccer goalkeepers respond differently to penalty shots, depending on their specific perceptual and motor capabilities. However, it remains unclear whether analogous differences exist between individual penalty takers, and if specialising in penalty taking affects the occurrence of differences between individuals. The present study examined individual differences in penalty shot speed and accuracy for specialists in penalty taking versus non-specialists. Expert specialist field hockey drag flickers and equivalently skilled non-specialists performed drag flicks towards predetermined targets placed in the face of a standard field hockey goal. Comparisons in shot speed and accuracy were made at a group level (specialists vs. non-specialists) as well as between individuals. Results revealed differences in both speed and accuracy between specialists, but only differences in speed between non-specialists. Specialists generated significantly greater shot speed than non-specialists (P < .001) and were more accurate to some, but not all, targets (top left, P < .006, bottom left P < .001). In addition, it was found that in specialists increasing practice correlated with decreasing accuracy. This may indicate that excessive practice could potentially reduce a specialist's accuracy in shooting towards specific targets.
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http://dx.doi.org/10.1080/02640414.2016.1180422 | DOI Listing |
Nano Lett
January 2025
Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States.
Quantitative optical phase information provides an alternative method to observe biomedical properties, where conventional phase imaging fails. Phase retrieval typically requires multiple intensity measurements and iterative computations to ensure uniqueness and robustness against detection noise. To increase the measurement speed, we propose a single-shot quantitative phase imaging method with metasurface optics that can be conveniently integrated into conventional imaging systems with minimal modification.
View Article and Find Full Text PDFIntegr Comp Biol
January 2025
Centro de investigación Colibrí Gorriazul, Cundinamarca, Colombia.
Wingbeat frequency estimation is an important aspect for the study of avian flight, energetics, and behavioral patterns, among others. Hummingbirds, in particular, are ideal subjects to test a method for this estimation due to their fast wing motions and unique aerodynamics, which results from their ecological diversification, adaptation to high-altitude environments, and sexually selected displays. Traditionally, wingbeat frequency measurements have been done via "manual" image/sound processing.
View Article and Find Full Text PDFComput Vis ECCV
November 2024
University of Minnesota, Minneapolis.
Diffusion models have emerged as powerful generative techniques for solving inverse problems. Despite their success in a variety of inverse problems in imaging, these models require many steps to converge, leading to slow inference time. Recently, there has been a trend in diffusion models for employing sophisticated noise schedules that involve more frequent iterations of timesteps at lower noise levels, thereby improving image generation and convergence speed.
View Article and Find Full Text PDFScience
January 2025
Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO, USA.
Optical frequency combs have enabled unique advantages in broadband, high-resolution spectroscopy and precision interferometry. However, quantum mechanics ultimately limits the metrological precision achievable with laser frequency combs. Quantum squeezing has led to significant measurement improvements with continuous wave lasers, but experiments demonstrating metrological advantage with squeezed combs are less developed.
View Article and Find Full Text PDFHealthc Technol Lett
December 2024
Robotics and Control Laboratory, Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada.
The Segment Anything model (SAM) is a powerful vision foundation model that is revolutionizing the traditional paradigm of segmentation. Despite this, a reliance on prompting each frame and large computational cost limit its usage in robotically assisted surgery. Applications, such as augmented reality guidance, require little user intervention along with efficient inference to be usable clinically.
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