As social beings, humans like to interact with each other, including other creatures of God, such as animals, and keep them as pets. Many pets, such as dogs, cats, birds, fish, rabbits, and so on, include unusual pets...
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Herein, a low-cost and readily available sodium aluminate (NaAlO2) was used as a solid base catalyst for the depolymerization of polycarbonate (PC) via methanolysis in the presence of tetrahydrofuran (THF) as a solven...
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Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes. Conformal inference algorithms constr...
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Radiology reports contain complex medical terminology and specialized knowledge, making them difficult for both patients and medical professionals to interpret. This study aims to address this challenge by developing ...
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ISBN:
(纸本)9791188428137
Radiology reports contain complex medical terminology and specialized knowledge, making them difficult for both patients and medical professionals to interpret. This study aims to address this challenge by developing a large-scale language model specifically designed for interpreting chest radiology reports. We focus on four key natural language processing (NLP) tasks—summarization, paraphrasing, abbreviation interpretation, and question answering—using a synthetic dataset derived from the MIMIC-CXR reports and GPT-3.5 Turbo. To enhance the model’s performance, we propose a two-stage supervised fine-tuning (SFT) process, incorporating real-world medical data from PubMedQA and MedQA, in addition to the synthetic dataset. The resulting models, Model-1 and Model-2, were evaluated based on accuracy, conciseness, and clarity, using test data not seen during training. Experimental results demonstrated that the proposed two-stage SFT method achieved strong performance across all four tasks, providing comparable performance to models such as GPT-3.5, Bard, Llama2, and MedAlpaca in key evaluation metrics, despite using a relatively smaller number of parameters. These findings suggest that synthetic data, when combined with domain-specific datasets, can significantly improve the interpretive capabilities of large-scale language models in the medical domain. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.
A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect ...
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To ensure the sustainability of their operations, higher education institutions have to establish a robust strategic plan. Higher education institutions have an obligation to continuously assess the quality of their e...
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In organizational knowledge management, Large Language Model (LLM) caches act as a semantic repository gathered from previous LLM responses. Due to intensive calls from multiple users, LLM may suffer from high inferen...
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Archaeogeophysics, i.e., the application and integration of geophysics into archaeological investigations, is an exciting and growing field of study and an international collaboration at the intersection of the physic...
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As one of the most effective methods to improve the accuracy and robustness of speech tasks,the audio-visual fusion approach has recently been introduced into the field of Keyword Spotting(KWS).However,existing audio-...
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As one of the most effective methods to improve the accuracy and robustness of speech tasks,the audio-visual fusion approach has recently been introduced into the field of Keyword Spotting(KWS).However,existing audio-visual keyword spotting models are limited to detecting isolated words,while keyword spotting for unconstrained speech is still a challenging *** this end,an Audio-Visual Keyword Transformer(AVKT)network is proposed to spot keywords in unconstrained video *** authors present a transformer classifier with learnable CLS tokens to extract distinctive keyword features from the variable-length audio and visual *** outputs of audio and visual branches are combined in a decision fusion *** humans can easily notice whether a keyword appears in a sentence or not,our AVKT network can detect whether a video clip with a spoken sentence contains a pre-specified ***,the position of the keyword is localised in the attention map without additional position ***-imental results on the LRS2-KWS dataset and our newly collected PKU-KWS dataset show that the accuracy of AVKT exceeded 99%in clean scenes and 85%in extremely noisy *** code is available at https://***/jialeren/AVKT.
Depression affects a significant number of people, and it has a significant impact not only on their lives but also on society as a whole. In light of this, we require more advanced methods that are capable of locatin...
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