The extent to which higher-order representations can be extracted from more than one word in parallel remains an unresolved issue with theoretical import. Here, we used ERPs to investigate the timing with which semantic information is extracted from parafoveal words. Participants saw animal and non-animal targets paired with response congruent or incongruent flankers in a semantic categorization task. Animal targets elicited smaller amplitude negativities when they were paired with semantically related and response congruent animal flankers (e.g., wolf coyote wolf) compared to unrelated and response incongruent flankers (e.g., sock coyote sock) in the N400 window and a post-N400 window. We interpret the N400 effect in terms of facilitated processing from the joint activation of shared semantic features (e.g., animal, furry) across target and flanker words and the later effect in terms of post-lexical decision-making. Thus, semantic information can be extracted from flankers in parallel and impacts various stages of processing.
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http://dx.doi.org/10.1016/j.bandl.2021.104965 | DOI Listing |
Brief Bioinform
November 2024
Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare, No. 1188 Wuzhong Avenue, Wuzhong District Suzhou, Suzhou 215004, China.
The automatic and accurate extraction of diverse biomedical relations from literature constitutes the core elements of medical knowledge graphs, which are indispensable for healthcare artificial intelligence. Currently, fine-tuning through stacking various neural networks on pre-trained language models (PLMs) represents a common framework for end-to-end resolution of the biomedical relation extraction (RE) problem. Nevertheless, sequence-based PLMs, to a certain extent, fail to fully exploit the connections between semantics and the topological features formed by these connections.
View Article and Find Full Text PDFJ Imaging
January 2025
RCAM Laboratory, Telecommunications Department, Sidi Bel Abbes University, Sidi Bel Abbes 22000, Algeria.
In recent years, deep-network-based hashing has gained prominence in image retrieval for its ability to generate compact and efficient binary representations. However, most existing methods predominantly focus on high-level semantic features extracted from the final layers of networks, often neglecting structural details that are crucial for capturing spatial relationships within images. Achieving a balance between preserving structural information and maximizing retrieval accuracy is the key to effective image hashing and retrieval.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
College of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China.
During the rice harvesting process, severe occlusion and adhesion exist among multiple targets, such as rice, straw, and leaves, making it difficult to accurately distinguish between rice grains and impurities. To address the current challenges, a lightweight semantic segmentation algorithm for impurities based on an improved SegFormer network is proposed. To make full use of the extracted features, the decoder was redesigned.
View Article and Find Full Text PDFBMC Emerg Med
January 2025
Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
Background: Volunteers providing nursing services are among the first individuals to arrive at the scene after an incident; therefore, they must use their skills and capabilities to provide necessary care for the injured to prevent problems from worsening and complications from arising. Consequently, having structured empowerment courses for volunteers before disasters seems essential. This research aimed to determine the dimensions and components of empowering volunteer nursing service providers in disasters.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
January 2025
Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
Purpose: Semantic segmentation and landmark detection are fundamental tasks of medical image processing, facilitating further analysis of anatomical objects. Although deep learning-based pixel-wise classification has set a new-state-of-the-art for segmentation, it falls short in landmark detection, a strength of shape-based approaches.
Methods: In this work, we propose a dense image-to-shape representation that enables the joint learning of landmarks and semantic segmentation by employing a fully convolutional architecture.
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