no code implementations • 15 Mar 2024 • Zixin Yang, Richard Simon, Kelly Merrell, Cristian. A. Linte
Our method is evaluated and compared to both a learning-based method and a traditional method that features FEM regularization using data collected on our custom-developed phantom, as well as two publicly available datasets.
no code implementations • 5 Feb 2023 • Zixin Yang, Richard Simon, Cristian A. Linte
Yet, as a large laparoscopic dataset for training learning-based methods does not exist and the generalization ability of networks remains to be improved, the incorporation of the proposed disparity refinement framework into existing networks will contribute to improving their overall accuracy and robustness associated with depth estimation.
no code implementations • 19 Nov 2022 • Qi Deng, Lunge Dai, Zixin Yang, Zhong-guo Zhou, Monica Hussein, Dingyi Chen, Mick Swartz
Based on our findings, we argue that the differences among the levels and determinants of initial return, monthly return (intrinsic value) and intramonth return (overreaction) in different time periods can be largely explained by regulation regime changes along two dimensions: 1) approval vs. registration and 2) listing day trading curbs and return limits.
no code implementations • 7 Nov 2022 • Zixin Yang, Richard Simon, Cristian A. Linte
To assist with this task, we explore the use of learning-based feature descriptors, which, to our best knowledge, have not been explored for use in laparoscopic liver registration.
no code implementations • 19 Feb 2021 • Zhengwen Liu, Rafael A. Porto, Zixin Yang
Building upon the worldline effective field theory (EFT) formalism for spinning bodies developed for the Post-Newtonian regime, we generalize the EFT approach to Post-Minkowskian (PM) dynamics to include rotational degrees of freedom in a manifestly covariant framework.
High Energy Physics - Theory General Relativity and Quantum Cosmology