Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution.
Our experiments show that TestAug has three advantages over the existing work on behavioral testing: (1) TestAug can find more bugs than existing work; (2) The test cases in TestAug are more diverse; and (3) TestAug largely saves the manual efforts in creating the test suites.
In this work, for the first time, we propose to investigate this problem where only a small number of labeled training samples are available.
We find that using the same time budget, HPO often fails to outperform grid search due to two reasons: insufficient time budget and overfitting.
Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution for inferring a latent source space of neural activity.
Furthermore, a data-driven model predictive controller with the learned Koopman model is designed for path tracking control of autonomous vehicles.
Many imaging technologies rely on tomographic reconstruction, which requires solving a multidimensional inverse problem given a finite number of projections.
no code implementations • 15 Jan 2020 • Haoran Sun, Xueqing Liu, Xinyang Feng, Chen Liu, Nanyan Zhu, Sabrina J. Gjerswold-Selleck, Hong-Jian Wei, Pavan S. Upadhyayula, Angeliki Mela, Cheng-Chia Wu, Peter D. Canoll, Andrew F. Laine, J. Thomas Vaughan, Scott A. Small, Jia Guo
Together, these studies validate our hypothesis that a deep learning approach can potentially replace the need for GBCAs in brain MRI.
Automated generation of high-quality topical hierarchies for a text collection is a dream problem in knowledge engineering with many valuable applications.