Zobrazeno 1 - 10
of 6 987
pro vyhledávání: '"TANG, Chao"'
Task-oriented grasping (TOG) is crucial for robots to accomplish manipulation tasks, requiring the determination of TOG positions and directions. Existing methods either rely on costly manual TOG annotations or only extract coarse grasping positions
Externí odkaz:
http://arxiv.org/abs/2409.16033
In the realm of modern mobile E-commerce, providing users with nearby commercial service recommendations through location-based online services has become increasingly vital. While machine learning approaches have shown promise in multi-scene recomme
Externí odkaz:
http://arxiv.org/abs/2408.07278
Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on clients to collaboratively train a model.In FSSL, the heterogeneous data can introduce prediction bias into the model, causing the model's prediction to skew towar
Externí odkaz:
http://arxiv.org/abs/2405.19789
We demonstrate an invertible all-optical gate on chip, with the roles of control and signal switchable by slightly adjusting their relative arrival time at the gate. It is based on quantum Zeno blockade driven by sum-frequency generation in a periodi
Externí odkaz:
http://arxiv.org/abs/2405.00150
Task-oriented grasping (TOG), which refers to synthesizing grasps on an object that are configurationally compatible with the downstream manipulation task, is the first milestone towards tool manipulation. Analogous to the activation of two brain reg
Externí odkaz:
http://arxiv.org/abs/2404.10399
Object search is a fundamental skill for household robots, yet the core problem lies in the robot's ability to locate the target object accurately. The dynamic nature of household environments, characterized by the arbitrary placement of daily object
Externí odkaz:
http://arxiv.org/abs/2404.00343
We propose a new approach for non-Cartesian magnetic resonance image reconstruction. While unrolled architectures provide robustness via data-consistency layers, embedding measurement operators in Deep Neural Network (DNN) can become impractical at l
Externí odkaz:
http://arxiv.org/abs/2403.17905
We demonstrate parametric all-optical modulation in a periodically-poled lithium niobate microring resonator on chip. It employs quantum Zeno blockade between two distinct waves, a signal and a pump, through their sum-frequency generation at a large
Externí odkaz:
http://arxiv.org/abs/2402.10367
Plug-and-Play (PnP) algorithms are appealing alternatives to proximal algorithms when solving inverse imaging problems. By learning a Deep Neural Network (DNN) denoiser behaving as a proximal operator, one waives the computational complexity of optim
Externí odkaz:
http://arxiv.org/abs/2312.07137
As Earth science enters the era of big data, artificial intelligence (AI) not only offers great potential for solving geoscience problems, but also plays a critical role in accelerating the understanding of the complex, interactive, and multiscale pr
Externí odkaz:
http://arxiv.org/abs/2311.04940