It remains an open question whether the amplitude of N400 reflects combinatory postlexical semantic integration processing. To examine the issue, we repeatedly presented strictly simplified, N400-eliciting three-word structures for seven times, mixed with their plausible counterparts, followed immediately by a much more enriched and informative sentence containing two keywords of the incongruous structure, for the purpose of reinitiating semantic integration processing. Event-related potentials were recorded and compared at the first, fourth, seventh, and eighth time. It was found that multiple repetitions attenuated the N400 effect to almost nonexistent and that the follow-up semantic integration reinitiating sentence did not recover N400 amplitude. The results suggest that combinatory postlexical semantic integration does not significantly modulate N400 amplitude, and provide evidence for noncombinatory processes underlying N400 such as automatic spreading activation and expectancy/prediction.
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http://dx.doi.org/10.1097/WNR.0000000000001108 | DOI Listing |
J Biomed Semantics
December 2024
Medical BioSciences Department, Radboud University Medical Center, Nijmegen, The Netherlands.
Motivation: We are witnessing an enormous growth in the amount of molecular profiling (-omics) data. The integration of multi-omics data is challenging. Moreover, human multi-omics data may be privacy-sensitive and can be misused to de-anonymize and (re-)identify individuals.
View Article and Find Full Text PDFNeural Netw
December 2024
School of Computer Science, Wuhan University, Luojiashan Road, Wuchang District., Wuhan, 430072, Hubei Province, China; Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, No. 8, Yangqiaohu Avenue, Zanglong Island Development Zone, Jiangxia District, Wuhan, 2007, Hubei Province, China. Electronic address:
The remarkable success of Graph Neural Networks underscores their formidable capacity to assimilate multimodal inputs, markedly enhancing performance across a broad spectrum of domains. In the context of molecular modeling, considerable efforts have been made to enrich molecular representations by integrating data from diverse aspects. Nevertheless, current methodologies frequently compartmentalize geometric and semantic components, resulting in a fragmented approach that impairs the holistic integration of molecular attributes.
View Article and Find Full Text PDFComput Biol Med
December 2024
Khalifa University, Abu Dhabi, United Arab Emirates.
Background And Objective: Accurate extraction of retinal vascular components is vital in diagnosing and treating retinal diseases. Achieving precise segmentation of retinal blood vessels is challenging due to their complex structure and overlapping vessels with other anatomical features. Existing deep neural networks often suffer from false positives at vessel branches or missing fragile vessel patterns.
View Article and Find Full Text PDFSci Rep
December 2024
School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao, 266520, China.
This paper presents a deep learning model based on an active learning strategy. The model achieves accurate identification of vegetation types in the study area by utilizing multispectral data obtained from preprocessing of unmanned aerial vehicle (UAV) remote sensing equipment. This approach offers advantages such as high data accuracy, mobility, and easy data collection.
View Article and Find Full Text PDFSci Rep
December 2024
University of Central Florida, Orlando, FL, 32816, USA.
Large Language Models (LLMs) are gaining significant popularity in recent years for specialized tasks using prompts due to their low computational cost. Standard methods like prefix tuning utilize special, modifiable tokens that lack semantic meaning and require extensive training for best performance, often falling short. In this context, we propose a novel method called Semantic Knowledge Tuning (SK-Tuning) for prompt and prefix tuning that employs meaningful words instead of random tokens.
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