We investigated the effect of learning one category structure on the learning of a related category structure. Photograph-name combinations, called identifiers, were associated with values of four demographic attributes. Two problems were related by analogous demographic attributes, common identifiers, or both to examine the impact of common identifier, related general characteristics, and the interaction of the two variables in mediating learning transfer from one category structure to another. Problems sharing the same identifier information prompted greater positive transfer than those not sharing the same identifier information. In contrast, analogous defining characteristics in the two problems did not facilitate transfer. We computed correlations between responses to first-problem stimuli and responses to analogous second-problem stimuli for each participant. The analogous characteristics produced a tendency to respond in the same way to corresponding stimuli in the two problems. The results support an alignment between category structures related by analogous defining characteristics, which is facilitated by specific identifier information shared by two category structures.
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Lang Speech
January 2025
Department of Communication Sciences and Disorders, University of Haifa, Israel.
This study investigated the role of systematicity in word learning, focusing on Semitic morpho-phonology where words exhibit multiple levels of systematicity. Building upon previous research on phonological templates, we explored how systematicity based on such templates, whether they encode meanings or not, influenced word learning in preschool-age Hebrew-speaking children. We examined form-meaning systematicity, where words share phonological templates and carry similar categorical meanings of manner-of-motion (e.
View Article and Find Full Text PDFCureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFPowerful generative AI models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly model multiple binding ligands concurrently or have been rigorously benchmarked on pharmacologically relevant drug targets, hindering their widespread adoption in drug discovery efforts. In this work, we propose FlowDock, the first deep geometric generative model based on conditional flow matching that learns to directly map unbound (apo) structures to their bound (holo) counterparts for an arbitrary number of binding ligands.
View Article and Find Full Text PDFThe neural networks offer iteration capability for low-density parity-check (LDPC) decoding with superior performance at transmission. However, to cope with increasing code length and rate, the complexity of the neural network increases significantly. This is due to the large amount of feature extraction required to maintain the error correction capability.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Bangkok Hospital Dental Center Holistic Care and Dental Implant, Bangkok Hospital, Bangkok, 10310, Thailand.
Background: Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determining the angulation, position, classification and difficulty index (DI) of ILTM. Additionally, we compared these parameters and the time required for interpretation among deep learning (DL) models, sixth-year dental students (DSs), and general dental practitioners (GPs) with and without CNN assistance.
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