Bell nonlocality and Kochen-Specker contextuality are among the main topics in the foundations of quantum theory. Both of them are related to stronger-than-classical correlations, with the former usually referring to spatially separated systems, while the latter considers a single system. In recent works, a unified framework for these phenomena was presented. This article reviews, expands, and obtains new results regarding this framework. Contextual and disturbing features inside the local models are explored, which allows for the definition of different local sets with a non-trivial relation among them. The relations between the set of quantum correlations and these local sets are also considered, and post-quantum local behaviours are found. Moreover, examples of correlations that are both local and non-contextual but such that these two classical features cannot be expressed by the same hidden variable model are shown. Extensions of the Fine-Abramsky-Brandenburger theorem are also discussed.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10529458 | PMC |
http://dx.doi.org/10.3390/e25091276 | DOI Listing |
Insights Imaging
January 2025
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Objectives: To develop and validate radiomics and deep learning models based on contrast-enhanced MRI (CE-MRI) for differentiating dual-phenotype hepatocellular carcinoma (DPHCC) from HCC and intrahepatic cholangiocarcinoma (ICC).
Methods: Our study consisted of 381 patients from four centers with 138 HCCs, 122 DPHCCs, and 121 ICCs (244 for training and 62 for internal tests, centers 1 and 2; 75 for external tests, centers 3 and 4). Radiomics, deep transfer learning (DTL), and fusion models based on CE-MRI were established for differential diagnosis, respectively, and their diagnostic performances were compared using the confusion matrix and area under the receiver operating characteristic (ROC) curve (AUC).
Sci Rep
January 2025
Center for Informatics Science (CIS), School of Information Technology and Computer Science, Nile University, 26th of July Corridor, Sheikh Zayed City, Giza, 12588, Egypt.
Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. We merged datasets from the Molecular Taxonomy of Breast Cancer International Consortium, Memorial Sloan Kettering Cancer Center, Duke University, and the SEER program, creating a comprehensive dataset of 272, 252 rows and 23 columns.
View Article and Find Full Text PDFSAR QSAR Environ Res
January 2025
Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, USA.
A scheme for constructing models of the 'structure-glass transition temperature of a polymer' is proposed. It involves the representation of the molecular structure of a polymer through the architecture of monomer units represented through a simplified molecular input-line entry system (SMILES) and the fragments of local symmetry (FLS). The statistical quality of such models is quite good: the determination coefficient values for active training set, passive training set, calibration set, and validation set are 0.
View Article and Find Full Text PDFMalawi Med J
January 2025
Access Health Africa.
Aim: An end colostomy is a potentially life-saving surgical intervention, but postoperative ostomy management is challenging in resource-limited settings. Socioeconomic, health system, and surgical capacity barriers may delay colostomy reversal. A surgery camp model for addressing the burden of unreversed colostomies has not previously been undertaken in Malawi.
View Article and Find Full Text PDFRadiol Med
January 2025
Department of Translational Medicine, University of Ferrara, Ferrara, Italy.
Purpose: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [F]FDG PET/CT.
Material And Methods: Primary tumor and the most significant lymph node metastasis were manually segmented in baseline [F]FDG PET/CT of 52 newly diagnosed BC patients. Clinical parameters, NAC and conventional semiquantitative PET parameters were collected.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!