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Focusing Effects on Laser-Induced Plasma Parameters: Applications to a Graphite Target Under Martian Atmospheric Conditions.
Appl Spectrosc
January 2025
Department of Physics & Applied Physics, Kennedy College of Sciences, University of Massachusetts Lowell, Lowell, Massachusetts, USA.
Under various atmospheric conditions, laser-induced breakdown spectroscopy (LIBS) is a powerful technique for elemental analysis, including in Earth- and Mars-like environments. However, understanding the plasma behavior and its dependence on ambient pressure and laser parameters remains a challenge. In this study, a numerical model based on a three-temperature Eulerian radiation framework under non-local thermodynamic equilibrium conditions is employed to investigate the interaction of a nanosecond laser pulse with a graphite target under helium (He) and carbon dioxide (CO atmospheres.
View Article and Find Full Text PDFLocal-non-local complementary learning network for 3D point cloud analysis.
Sci Rep
January 2025
School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, 110159, China.
Article Synopsis
- Point cloud analysis is crucial for applications like mapping and autonomous driving, but challenges arise due to the unordered nature of point clouds that complicate feature extraction.
- To improve this, the LNLCL-Net framework is introduced, which effectively separates local and non-local features and utilizes partial convolution to enhance feature representation.
- The framework's Complementary Interactive Attention module allows for the adaptive integration of these features, leading to improved performance in classification and segmentation tasks across various benchmark datasets.
Monogamy relations for relativistically causal correlations.
Nat Commun
January 2025
Département de Physique Appliquée, Université de Genève, Genève, Switzerland.
Non-signalling conditions encode minimal requirements that any (quantum) systems must satisfy in order to be consistent with special relativity. Recent works have argued that in scenarios involving more than two parties, correlations compatible with relativistic causality do not have to satisfy all possible non-signalling conditions but only a subset of them. Here we show that correlations satisfying only this subset of constraints have to satisfy highly non-local monogamy relations between the effects of space-like separated random variables.
View Article and Find Full Text PDFA non-local dual-stream fusion network for laryngoscope recognition.
Am 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.
How graph neural network interatomic potentials extrapolate: Role of the message-passing algorithm.
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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