What we attend to at any moment determines what we learn at that moment, and this also depends on our past learning. This focused conceptual paper concentrates on a single well-documented attention mechanism - highlighting. This phenomenon - well studied in non-linguistic but not in linguistic contexts - should be highly relevant to language learning because it is a process that (1) specifically protects past learning from being disrupted by new (and potentially spurious) associations in the learning environment, and (2) strongly constrains new learning to new information. Within the language learning context, highlighting may disambiguate ambiguous references and may be related to processes of lexical competition that are known to be critical to on-line sentence comprehension. The main sections of the paper will address (1) the highlighting phenomenon in the literature; (2) its relevancy to language learning; (3) the highlighting effect in children; (4) developmental studies concerning the effect in different contexts; and (5) a developmental mechanism for highlighting in language learning.
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http://dx.doi.org/10.3389/fpsyg.2012.00262 | DOI Listing |
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
In cybersecurity, anomaly detection in tabular data is essential for ensuring information security. While traditional machine learning and deep learning methods have shown some success, they continue to face significant challenges in terms of generalization. To address these limitations, this paper presents an innovative method for tabular data anomaly detection based on large language models, called "Tabular Anomaly Detection via Guided Prompts" (TAD-GP).
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January 2025
Division of Nephrology and Hypertension, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
Artificial intelligence (AI) has shown promise in revolutionizing medical triage, particularly in the context of the rising prevalence of kidney-related conditions with the aging global population. This study evaluates the utility of ChatGPT, a large language model, in triaging nephrology cases through simulated real-world scenarios. Two nephrologists created 100 patient cases that encompassed various aspects of nephrology.
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January 2025
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China.
Aspect Category Sentiment Analysis (ACSA) is a fine-grained sentiment analysis task aimed at predicting the sentiment polarity associated with aspect categories within a sentence.Most existing ACSA methods are based on a given aspect category to locate sentiment words related to it. When irrelevant sentiment words have semantic meaning for the given aspect category, it may cause the problem that sentiment words cannot be matched with aspect categories.
View Article and Find Full Text PDFNPJ Precis Oncol
January 2025
CRCL, Centre Léon Bérard, Lyon, France.
Publicly available trial matching tools can improve the access to therapeutic innovations, but errors may expose to over-solicitation and disappointment. We performed a pragmatic non-interventional prospective evaluation on sequential patients at the Molecular Tumor Board of Centre Leon Berard. During 10 weeks in 2024, we analysed 157 patients with four clinical trial matching tools from the 19 screened: Klineo, ScreenAct, Trialing and DigitalECMT.
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