This verifies the writing style contains valuable information that could improve the performance of the event extraction task.
The testing results show that the proposed method can achieve image reconstruction at a very low sampling rate (0. 38$\%$).
Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a long-standing challenge.
Ghost imaging (GI) has been paid attention gradually because of its lens-less imaging capability, turbulence-free imaging and high detection sensitivity.
The proposed method can improve the problems caused by conventional recognition methods that based on target image information, and provide a certain turbulence-free ability.
Bidirectional Encoder Representations from Transformers (BERT) have shown to be a promising way to dramatically improve the performance across various Natural Language Processing tasks [Devlin et al., 2019].
General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics.
To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods.
Current autonomous driving systems are composed of a perception system and a decision system.
Although deep reinforcement learning (deep RL) methods have lots of strengths that are favorable if applied to autonomous driving, real deep RL applications in autonomous driving have been slowed down by the modeling gap between the source (training) domain and the target (deployment) domain.
We have designed a single-pixel camera with imaging around corners based on computational ghost imaging.