Adults' performance on a variety of tasks suggests that phonological processing of nonwords is grounded in generalizations about sublexical patterns over all known words. A small body of research suggests that children's phonological acquisition is similarly based on generalizations over the lexicon. To test this account, production accuracy and fluency were examined in nonword repetitions by 104 children and 22 adults. Stimuli were 22 pairs of nonwords, in which one nonword contained a low-frequency or unattested two-phoneme sequence and the other contained a high-frequency sequence. For a subset of these nonword pairs, segment durations were measured. The same sound was produced with a longer duration (less fluently) when it appeared in a low-frequency sequence, as compared to a high-frequency sequence. Low-frequency sequences were also repeated with lower accuracy than high-frequency sequences. Moreover, children with smaller vocabularies showed a larger influence of frequency on accuracy than children with larger vocabularies. Taken together, these results provide support for a model of phonological acquisition in which knowledge of sublexical units emerges from generalizations made over lexical items.
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http://dx.doi.org/10.1044/1092-4388(2004/034) | DOI Listing |
Nat Microbiol
December 2024
Plant-Microbe Interactions, Institute of Environmental Biology, Department of Biology, Science4Life, Utrecht University, Utrecht, the Netherlands.
Potato vigour, the growth potential of seed potatoes, is a key agronomic trait that varies significantly across production fields due to factors such as genetic background and environmental conditions. Seed tuber microbiomes are thought to influence plant health and crop performance, yet the precise relationships between microbiome composition and potato vigour remain unclear. Here we conducted microbiome sequencing on seed tuber eyes and heel ends from 6 potato varieties grown in 240 fields.
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December 2024
College of Sciences, National University of Defense Technology, 410073, Changsha, China.
Deep Convolutional Neural Networks (DCNNs), due to their high computational and memory requirements, face significant challenges in deployment on resource-constrained devices. Network Pruning, an essential model compression technique, contributes to enabling the efficient deployment of DCNNs on such devices. Compared to traditional rule-based pruning methods, Reinforcement Learning(RL)-based automatic pruning often yields more effective pruning strategies through its ability to learn and adapt.
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December 2024
Henan University of Engineering, Zhengzhou, 451191, China.
Social media generates vast amounts of spatio-temporal sequential data. However, current methods often ignore the complex spatio-temporal correlations within these data. This oversight makes it difficult to fully capture the dynamic features of the data.
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December 2024
College of Electrical Engineering, Anhui Polytechnic University, Wuhu, 241000, Anhui, China.
The quantity of cable conductors is a crucial parameter in cable manufacturing, and accurately detecting the number of conductors can effectively promote the digital transformation of the cable manufacturing industry. Challenges such as high density, adhesion, and knife mark interference in cable conductor images make intelligent detection of conductor quantity particularly difficult. To address these challenges, this study proposes the YOLO-cable model, which is an improvement made upon the YOLOv10 model.
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December 2024
School of Fashion Media, Jiangxi Institute of Fashion Technology, Nanchang, 330000, China.
This study proposes a novel artificial intelligence (AI)-assisted design model that combines Variational Autoencoders (VAE) with reinforcement learning (RL) to enhance innovation and efficiency in cultural and creative product design. By introducing AI-driven decision support, the model streamlines the design workflow and significantly improves design quality. The study establishes a comprehensive framework and applies the model to four distinct design tasks, with extensive experiments validating its performance.
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