Zobrazeno 1 - 10
of 27 670
pro vyhledávání: '"Sun , Jian"'
Introducing foreign ions, atoms, or molecules into emerging functional materials is crucial for manipulating material physical properties and innovating device applications. The intercalation of emerging new materials can induce multiple intrinsic ch
Externí odkaz:
http://arxiv.org/abs/2410.10301
All-microwave control of fixed-frequency superconducting quantum systems offers the potential to reduce control circuit complexity and increase system coherence. Nevertheless, due to the limited control flexibility in qubit parameters, one has to add
Externí odkaz:
http://arxiv.org/abs/2410.07817
In recent years, there has been considerable interest in designing stochastic first-order algorithms to tackle finite-sum smooth minimax problems. To obtain the gradient estimates, one typically relies on the uniform sampling-with-replacement scheme
Externí odkaz:
http://arxiv.org/abs/2410.04761
At present, Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory. Cooperative driving leverages the connectivity ability of C
Externí odkaz:
http://arxiv.org/abs/2409.12812
The development of autonomous vehicles has shown great potential to enhance the efficiency and safety of transportation systems. However, the decision-making issue in complex human-machine mixed traffic scenarios, such as unsignalized intersections,
Externí odkaz:
http://arxiv.org/abs/2409.05712
The rapid advancement in point cloud processing technologies has significantly increased the demand for efficient and compact models that achieve high-accuracy classification. Knowledge distillation has emerged as a potent model compression technique
Externí odkaz:
http://arxiv.org/abs/2409.02020
Advances in self-supervised learning are essential for enhancing feature extraction and understanding in point cloud processing. This paper introduces PMT-MAE (Point MLP-Transformer Masked Autoencoder), a novel self-supervised learning framework for
Externí odkaz:
http://arxiv.org/abs/2409.02007
This study addresses the computational inefficiencies in point cloud classification by introducing novel MLP-based architectures inspired by recent advances in CNN optimization. Traditional neural networks heavily rely on multiplication operations, w
Externí odkaz:
http://arxiv.org/abs/2409.01998
The proliferation of e-commerce and urbanization has significantly intensified delivery operations in urban areas, boosting the volume and complexity of delivery demand. Data-driven predictive methods, especially those utilizing machine learning tech
Externí odkaz:
http://arxiv.org/abs/2408.17258
Segment Anything Model (SAM) has demonstrated impressive performance on a wide range of natural image segmentation tasks. However, its performance significantly deteriorates when directly applied to medical domain, due to the remarkable differences b
Externí odkaz:
http://arxiv.org/abs/2408.09886