Semantic information extraction from the parafovea has been reported only in simplified Chinese for a special subset of characters and its generalizability has been questioned. This study uses traditional Chinese, which differs from simplified Chinese in visual complexity and in mapping semantic forms, to demonstrate access to parafoveal semantic information during reading of this script. Preview duration modulates various types (identical, phonological, and unrelated) of parafoveal information extraction. Parafoveal semantic extraction is more elusive in English; therefore, we conclude that such effects in Chinese are presumably caused by substantial cross-language differences from alphabetic scripts. The property of Chinese characters carrying rich lexical information in a small region provides the possibility of semantic extraction in the parafovea.
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http://dx.doi.org/10.1016/j.actpsy.2012.06.004 | DOI Listing |
J Biomed Inform
January 2025
Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, 02115, MA, USA; VA Boston Healthcare System, 150 S Huntington Ave, Boston, 02130, MA, USA. Electronic address:
Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100811, China.
While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth semantic exploration of functions and failing to preserve structural information at the file level. Motivated by the aforementioned challenges, this paper introduces MalHAPGNN, a novel framework for malware detection that leverages a hierarchical attention pooling graph neural network based on enhanced call graphs.
View Article and Find Full Text PDFMaterials (Basel)
January 2025
Hubei Key Laboratory of Plasma Chemistry and Advanced Materials, School of Materials Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
The grain size of metal materials has a significant impact on their macroscopic properties. However, original metallographic images often suffer from issues such as substantial noise, missing grain boundaries, low contrast, and blurred edges. These challenges hinder the accurate extraction of complete grain boundaries, limiting the precision of grain size measurement and material performance prediction.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Department of Medical Device and Healthcare, Dongguk University, Seoul 04620, Republic of Korea.
Liver cancer has a high mortality rate worldwide, and clinicians segment liver vessels in CT images before surgical procedures. However, liver vessels have a complex structure, and the segmentation process is conducted manually, so it is time-consuming and labor-intensive. Consequently, it would be extremely useful to develop a deep learning-based automatic liver vessel segmentation method.
View Article and Find Full Text PDFSci Rep
January 2025
Nanfang College Guangzhou, Guangzhou, 510970, China.
Named Entity Recognition (NER) is an essential component of numerous Natural Language Processing (NLP) systems, with the aim of identifying and classifying entities that have specific meanings in raw text, such as person (PER), location (LOC), and organization (ORG). Recently, Deep Neural Networks (DNNs) have been extensively applied to NER tasks owing to the rapid development of deep learning technology. However, despite their advancements, these models fail to take full advantage of the multi-level features (e.
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