Representative learning design is a key feature of the theory of ecological dynamics, conceptualising how task constraints can be manipulated in training designs to help athletes self-regulate during their interactions with information-rich performance environments. Implementation of analytical methodologies can support representative designs of practice environments by practitioners recording how interacting constraints influence events, that emerge under performance conditions. To determine key task constraints on kicking skill performance, the extent to which interactions of constraints differ in prevalence and influence on kicking skills was investigated across competition tiers in Australian Football (AF). A data sample of kicks (n = 29,153) was collected during junior, state-level and national league matches. Key task constraints were recorded for each kick, with performance outcome recorded as effective or ineffective. Rules were based on frequency and strength of associations between constraints and kick outcomes, generated using the Apriori algorithm. Univariate analysis revealed that low kicking effectiveness was associated with physical pressure (37%), whereas high efficiency emerged when kicking to an open target (70%). Between-competition comparisons showed differences in constraint interactions through seven unique rules and differences in confidence levels in shared rules. Results showed how understanding of key constraints interactions, and prevalence during competitive performance, can be used to inform representative learning designs in athlete training programmes. Findings can be used to specify how the competitive performance environment differs between competition tiers, supporting the specification of information in training designs, representative of different performance levels.
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http://dx.doi.org/10.1016/j.humov.2019.06.013 | DOI Listing |
Biomimetics (Basel)
December 2024
School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China.
In this paper, a deep reinforcement learning (DRL) approach based on generative adversarial imitation learning (GAIL) and long short-term memory (LSTM) is proposed to resolve tracking control problems for robotic manipulators with saturation constraints and random disturbances, without learning the dynamic and kinematic model of the manipulator. Specifically, it limits the torque and joint angle to a certain range. Firstly, in order to cope with the instability problem during training and obtain a stability policy, soft actor-critic (SAC) and LSTM are combined.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China.
Enabling a robot to learn skills from a human and adapt to different task scenarios will enable the use of robots in manufacturing to improve efficiency. Movement Primitives (MPs) are prominent tools for encoding skills. This paper investigates how to learn MPs from a small number of human demonstrations and adapt to different task constraints, including waypoints, joint limits, virtual walls, and obstacles.
View Article and Find Full Text PDFJ Comput Graph Stat
March 2024
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by complex survey sampling, with entries following different canonical exponential distributions and subject to heterogeneous missingness. To tackle this challenging task, we propose a two-stage procedure: in the first stage, we model the entry-wise missing mechanism by logistic regression, and in the second stage, we complete the target parameter matrix by maximizing a weighted log-likelihood with a low-rank constraint.
View Article and Find Full Text PDFHeliyon
December 2024
College of Foreign Languages and Cultures, Xiamen University, Xiamen, China.
This study explored emoji-based sarcastic statements made by Chinese teenagers using the WeChat app. Two experiments ( = 597) were conducted to investigate both language production and comprehension. In Experiment 1 ( = 236), a free-response task was used to determine which emoji Chinese teenagers would use to signal their sarcastic intentions.
View Article and Find Full Text PDFArXiv
December 2024
Department of Physics, Princeton University.
Transformers exhibit in-context learning (ICL): the ability to use novel information presented in the context without additional weight updates. Recent work shows that ICL emerges when models are trained on a sufficiently diverse set of tasks and the transition from memorization to generalization is sharp with increasing task diversity. One interpretation is that a network's limited capacity to memorize favors generalization.
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