Zobrazeno 1 - 10
of 6 757
pro vyhledávání: '"An Xuerui"'
Publikováno v:
Open Chemistry, Vol 20, Iss 1, Pp 267-271 (2022)
Pistacia lentiscus L. is an evergreen shrub belonging to the Anacardiaceae family, cultivated exclusively in the southern area of Chios Island. Mastic gum as a unique natural resin of the tree Pistacia lentiscus L. has been used extensively in functi
Externí odkaz:
https://doaj.org/article/574696a877ed42fd83387d24c143f50d
Autor:
Yao, Man, Qiu, Xuerui, Hu, Tianxiang, Hu, Jiakui, Chou, Yuhong, Tian, Keyu, Liao, Jianxing, Leng, Luziwei, Xu, Bo, Li, Guoqi
The ambition of brain-inspired Spiking Neural Networks (SNNs) is to become a low-power alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major challenges in realizing this vision: the performance gap between SNNs a
Externí odkaz:
http://arxiv.org/abs/2411.16061
Continual learning aims to incrementally acquire new concepts in data streams while resisting forgetting previous knowledge. With the rise of powerful pre-trained models (PTMs), there is a growing interest in training incremental learning systems usi
Externí odkaz:
http://arxiv.org/abs/2411.02175
We introduce AiM, an autoregressive (AR) image generative model based on Mamba architecture. AiM employs Mamba, a novel state-space model characterized by its exceptional performance for long-sequence modeling with linear time complexity, to supplant
Externí odkaz:
http://arxiv.org/abs/2408.12245
Spiking Neural Networks (SNNs) have received widespread attention due to their unique neuronal dynamics and low-power nature. Previous research empirically shows that SNNs with Poisson coding are more robust than Artificial Neural Networks (ANNs) on
Externí odkaz:
http://arxiv.org/abs/2407.20099
Recent advances in prompt learning have allowed users to interact with artificial intelligence (AI) tools in multi-turn dialogue, enabling an interactive understanding of images. However, it is difficult and inefficient to deliver information in comp
Externí odkaz:
http://arxiv.org/abs/2407.13596
Spiking Neural Networks (SNNs) are capable of encoding and processing temporal information in a biologically plausible way. However, most existing SNN-based methods for image tasks do not fully exploit this feature. Moreover, they often overlook the
Externí odkaz:
http://arxiv.org/abs/2406.03046
Autor:
Hu, JiaKui, Yao, Man, Qiu, Xuerui, Chou, Yuhong, Cai, Yuxuan, Qiao, Ning, Tian, Yonghong, XU, Bo, Li, Guoqi
Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory requirements during training and increase inference energy cost. Current training methods cannot simultaneously solve both training and inference dilemmas. This w
Externí odkaz:
http://arxiv.org/abs/2405.16466
Recent advancements in neuroscience research have propelled the development of Spiking Neural Networks (SNNs), which not only have the potential to further advance neuroscience research but also serve as an energy-efficient alternative to Artificial
Externí odkaz:
http://arxiv.org/abs/2405.13672
Autor:
Wang, Xinhua, Mao, Xuerui
Publikováno v:
The Aeronautical Journal, 2024
For a class of uncertain systems, a non-overshooting sliding mode control is presented to make them globally exponentially stable and without overshoot. Even when the unknown stochastic disturbance exists, and the time-variant reference trajectory is
Externí odkaz:
http://arxiv.org/abs/2405.01087