The possibility of Bell inequality violations in quantum theory had a profound impact on our understanding of the correlations that can be shared by distant parties. Generalizing the concept of Bell nonlocality to networks leads to novel forms of correlations, the characterization of which is, however, challenging. Here, we investigate constraints on correlations in networks under the natural assumptions of no-signaling and independence of the sources. We consider the triangle network with binary outputs, and derive strong constraints on correlations even though the parties receive no input, i.e., each party performs a fixed measurement. We show that some of these constraints are tight, by constructing explicit local models (i.e. where sources distribute classical variables) that can saturate them. However, we also observe that other constraints can apparently not be saturated by local models, which opens the possibility of having nonlocal (but non-signaling) correlations in the triangle network with binary outputs.
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http://dx.doi.org/10.1038/s41467-020-16137-4 | DOI Listing |
Neuroradiol J
January 2025
Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, USA.
This study evaluates the efficacy of deep learning models in identifying infarct tissue on computed tomography perfusion (CTP) scans from patients with acute ischemic stroke due to large vessel occlusion, specifically addressing the potential influence of varying noise reduction techniques implemented by different vendors. We analyzed CTP scans from 60 patients who underwent mechanical thrombectomy achieving a modified thrombolysis in cerebral infarction (mTICI) score of 2c or 3, ensuring minimal changes in the infarct core between the initial CTP and follow-up MR imaging. Noise reduction techniques, including principal component analysis (PCA), wavelet, non-local means (NLM), and a no denoising approach, were employed to create hemodynamic parameter maps.
View Article and Find Full Text PDFSci Rep
January 2025
School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, 110159, China.
Sci Rep
December 2024
Weifang Education Investment Group Co., Ltd., Weifang, 261108, China.
Vehicle re-identification (re-id) technology refers to a vehicle matching under a non-overlapping domain, that is, to confirm whether the vehicle target taken by cameras in different positions at different times is the same vehicle. Different identities of the same type of vehicles are one of the most challenging factors in the field of vehicle re-identification. The key to solve this difficulty is to make full use of the multiple discriminative features of vehicles.
View Article and Find Full Text PDFAm J Otolaryngol
December 2024
Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin 300192, China; Institute of Otolaryngology of Tianjin, Tianjin, China; Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China; Key Clinical Discipline of Tianjin (Otolaryngology), Tianjin, China; Otolaryngology Clinical Quality Control Centre, Tianjin, China.
Purpose: To use deep learning technology to design and implement a model that can automatically classify laryngoscope images and assist doctors in diagnosing laryngeal diseases.
Materials And Methods: The experiment was based on 3057 images (normal, glottic cancer, granuloma, Reinke's Edema, vocal cord cyst, leukoplakia, nodules and polyps) from the dataset Laryngoscope8. A classification model based on deep neural networks was developed and tested.
J Chem Phys
December 2024
Computational Science Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.
Graph neural network interatomic potentials (GNN-IPs) are gaining significant attention due to their capability of learning from large datasets. Specifically, universal interatomic potentials based on GNN, usually trained with crystalline geometries, often exhibit remarkable extrapolative behavior toward untrained domains, such as surfaces and amorphous configurations. However, the origin of this extrapolation capability is not well understood.
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