The influence of object-category on the representation of semantic knowledge remains unresolved. We present a functional magnetic resonance imaging study that investigates whether there are distinct neural substrates for semantic knowledge of kinds of people (e.g., lawyer, nurse etc.) and places (e.g., bank, prison etc.). Access to semantic details about kinds of people produced selective responses in the precuneus, medial prefrontal cortex, left anterior temporal lobe, posterior middle temporal gyrus and the temporoparietal junction. Corresponding place-selective responses were present in the parahippocampal gyrus and retrosplenial complex. Category selectivity was found to be less pronounced when conceptual information was accessed about kinds of people compared to unique people (e.g., Obama). We attribute this to the greater importance of cross-categorical semantic knowledge in the conceptual representation of kinds. Together, these results show the importance of object-category in non-perceptual semantic representations and indicate the manner in which these systems may interact to create full conceptual representations.
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http://dx.doi.org/10.1016/j.cortex.2013.05.010 | DOI Listing |
Front Big Data
January 2025
AI Institute, University of South Carolina, Columbia, SC, United States.
The emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model training and hyperparameter tuning of end-to-end AI pipelines. However, other crucial stages of these pipelines such as dataset selection, feature engineering, and model optimization for deployment have received less attention. Improving efficiency of end-to-end AI pipelines requires metadata of past executions of AI pipelines and all their stages.
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January 2025
Zhengzhou University of Light Industry, Zhengzhou, 450001, China.
Visual-language models (VLMs) excel in cross-modal reasoning by synthesizing visual and linguistic features. Recent VLMs use prompt learning for fine-tuning, allowing adaptation to various downstream tasks. TCP applies class-aware prompt tuning to improve VLMs generalization, yet its reliance on fixed text templates as prior knowledge can limit adaptability to fine-grained category distinctions.
View Article and Find Full Text PDFFront Nutr
January 2025
Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia.
Introduction: Contemporary data and knowledge management and exploration are challenging due to regular releases, updates, and different types and formats. In the food and nutrition domain, solutions for integrating such data and knowledge with respect to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles are still lacking.
Methods: To address this issue, we have developed a data and knowledge management system called NutriBase, which supports the compilation of a food composition database and its integration with evidence-based knowledge.
Bioinformatics
January 2025
College of Artificial Intelligence, Nankai University, Tianjin, 300350, China.
Motivation: The drug-disease, gene-disease, and drug-gene relationships, as high-frequency edge types, describe complex biological processes within the biomedical knowledge graph. The structural patterns formed by these three edges are the graph motifs of (disease, drug, gene) triplets. Among them, the triangle is a steady and important motif structure in the network, and other various motifs different from the triangle also indicate rich semantic relationships.
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