Search Results for author:
Found 3 papers, 1 papers with code
Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian
Incorporating the Hamiltonian structure of physical dynamics into deep learning models provides a powerful way to improve the interpretability and prediction accuracy.
Orbital Stability of Circumstellar Planets in Binary Systems
Planets that orbit only one of the stars in stellar binary systems (i. e., circumstellar) are dynamically constrained to a limited range of orbital parameters and thus understanding conditions on their stability is of great importance in exoplanet searches.
Earth and Planetary Astrophysics Solar and Stellar Astrophysics
Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis
In this paper, we propose to identify breast tumor in MRI by Cosine Margin Sigmoid Loss (CMSL) with deep learning (DL) and localize possible cancer lesion by COrrelation Attention Map (COAM) based on the learned features.
