We introduce a variant of the voter model in which agents may have different degrees of confidence in their opinions. Those with low confidence are normal voters whose state can change upon a single contact with a different neighboring opinion. However, confidence increases with opinion reinforcement, and above a certain threshold, these agents become zealots, irreducible agents who do not change their opinion. We show that both strategies, normal voters and zealots, may coexist (in the thermodynamical limit), leading to competition between two different kinetic mechanisms: curvature-driven growth and interfacial noise. The kinetically constrained zealots are formed well inside the clusters, away from the different opinions at the surfaces that help limit their confidence. Normal voters concentrate in a region around the interfaces, and their number, which is related to the distance between the surface and the zealotry bulk, depends on the rate at which the confidence changes. Despite this interface being rough and fragmented, typical of the voter model, the presence of zealots in the bulk of these domains induces a curvature-driven dynamics, similar to the low temperature coarsening behavior of the nonconserved Ising model after a temperature quench.
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http://dx.doi.org/10.1103/PhysRevE.106.014121 | DOI Listing |
Lung Cancer
December 2024
Università Vita-Salute San Raffaele, Milan, Italy; Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy.
Background: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records. Their application in clinical routine is still challenging, due to technical limits and to explainability issues in this specific setting. Response to standard first-line immunotherapy (ICI) in metastatic Non-Small-Cell Lung Cancer (NSCLC) is an interesting population for machine learning (ML), since up to 30% of patients do not benefit.
View Article and Find Full Text PDFBackground: Early detection and accurate forecasting of AD progression are crucial for timely intervention and management. This study leverages multi-modal data, including MRI scans, brain volumetrics, and clinical notes, utilizing Machine Learning (ML), Deep Learning (DL) and a range of ensemble methods to enhance the forecasting accuracy of Alzheimer's disease.
Method: We utilize the OASIS-3 longitudinal dataset, tracking 1,098 patients over 30 years.
Reddit is a popular social media platform that is made up of subreddits, a kind of special interest page. One of these is DoctorsUK, which has over 45,000 members and claims to be a community for UK-based doctors. There is, however, no way of verifying who uses the page, as Reddit is essentially anonymous.
View Article and Find Full Text PDFSci Rep
January 2025
School of Information Engineering, Changji University, Changji, 831100, Xinjiang, China.
Healthcare insurance fraud imposes a significant financial burden on healthcare systems worldwide, with annual losses reaching billions of dollars. This study aims to improve fraud detection accuracy using machine learning techniques. Our approach consists of three key stages: data preprocessing, model training and integration, and result analysis with feature interpretation.
View Article and Find Full Text PDFWorld J Mens Health
December 2024
Division of Urology, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan.
Purpose: Information retrieval (IR) and risk assessment (RA) from multi-modality imaging and pathology reports are critical to prostate cancer (PC) treatment. This study aims to evaluate the performance of four general-purpose large language model (LLMs) in IR and RA tasks.
Materials And Methods: We conducted a study using simulated text reports from computed tomography, magnetic resonance imaging, bone scans, and biopsy pathology on stage IV PC patients.
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