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http://dx.doi.org/10.1021/acs.nanolett.5c04185 | DOI Listing |
Disabil Rehabil Assist Technol
September 2025
School of Foreign Languages, Ningbo University of Technology, Ningbo, China.
The speech and language rehabilitation are essential to people who have disorders of communication that may occur due to the condition of neurological disorder, developmental delays, or bodily disabilities. With the advent of deep learning, we introduce an improved multimodal rehabilitation pipeline that incorporates audio, video, and text information in order to provide patient-tailored therapy that adapts to the patient. The technique uses a cross-attention fusion multimodal hierarchical transformer architectural model that allows it to jointly design speech acoustics as well as the facial dynamics, lip articulation, and linguistic context.
View Article and Find Full Text PDFNano Lett
September 2025
Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States.
Fecundity measurements play a crucial role in life history research, providing insights into reproductive fitness, population dynamics, and environmental responses. In the model nematode , fecundity assays are widely used to study development, aging, and genetic or environmental influences on reproduction. hermaphrodites have large numbers of offspring (>100), so manual counting of viable offspring is time-consuming and susceptible to human error.
View Article and Find Full Text PDFStud Health Technol Inform
September 2025
Chair of Medical Informatics, Institute of AI and Informatics in Medicine (AIIM), TUM University Hospital, Technical University of Munich, Munich, Germany.
Introduction: Medical entity linking is an important task in biomedical natural language processing, aiming to align textual mentions of medical concepts with standardized concepts in ontologies. Most existing approaches rely on supervised models or domain-specific embeddings, which require large datasets and significant computational resources.
Objective: The objective of this work is (1) to investigate the effectiveness of large language models (LLMs) in improving both candidate generation and disambiguation for medical entity linking through synonym expansion and in-context learning, and (2) to evaluate this approach against traditional string-matching and supervised methods.
Various Foundation Models (FMs) have been built based on the pre-training and fine-tuning framework to analyze single-cell data with different degrees of success. In this manuscript, we propose a method named scELMo (Single-cell Embedding from Language Models), to analyze single-cell data that utilizes Large Language Models (LLMs) as a generator for both the description of metadata information and the embeddings for such descriptions. We combine the embeddings from LLMs with the raw data under the zero-shot learning framework to further extend its function by using the fine-tuning framework to handle different tasks.
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