Zobrazeno 1 - 10
of 22 991
pro vyhledávání: '"YAN, FENG"'
Multimode interference (MMI) is a fundamental physical principle that plays a crucial role in modern communication technologies for wave splitting, filtering, switching and multiplexing. Typically, the generation of multimodes is highly dependent on
Externí odkaz:
http://arxiv.org/abs/2412.09038
Autor:
Geng, Zhi-Ming, Yao, Jin-Shan, Cheng, Ying-Bin, Yan, Xue-Jun, Zhou, Jian, Zhang, En-Rui, Li, Jia-Yi, Yuan, Ming-Qian, Fan, Xing, Deng, Yu, Lu, Hong, Lu, Ming-Hui, Chen, Yan-Feng
Phonons, the quanta of lattice vibrations, are primary heat carriers for semiconductors and dielectrics. The demand of effective phonon manipulation urgently emerges, because the thermal management is crucial for the ongoing development of micro/nano
Externí odkaz:
http://arxiv.org/abs/2412.08461
Autor:
Huang, Zhijian, Feng, Chengjian, Yan, Feng, Xiao, Baihui, Jie, Zequn, Zhong, Yujie, Liang, Xiaodan, Ma, Lin
Large Multimodal Models (LMMs) have demonstrated exceptional comprehension and interpretation capabilities in Autonomous Driving (AD) by incorporating large language models. Despite the advancements, current data-driven AD approaches tend to concentr
Externí odkaz:
http://arxiv.org/abs/2412.07689
Autor:
Yan, Feng, Liu, Fanfan, Zheng, Liming, Zhong, Yufeng, Huang, Yiyang, Guan, Zechao, Feng, Chengjian, Ma, Lin
In recent years, robotics has advanced significantly through the integration of larger models and large-scale datasets. However, challenges remain in applying these models to 3D spatial interactions and managing data collection costs. To address thes
Externí odkaz:
http://arxiv.org/abs/2412.07215
Autor:
Zhang, Tunhou, Cheng, Dehua, He, Yuchen, Chen, Zhengxing, Dai, Xiaoliang, Xiong, Liang, Liu, Yudong, Cheng, Feng, Cao, Yufan, Yan, Feng, Li, Hai, Chen, Yiran, Wen, Wei
Publikováno v:
ACM Transactions on Recommender Systems (TORS) 2024
The increasing popularity of deep learning models has created new opportunities for developing AI-based recommender systems. Designing recommender systems using deep neural networks requires careful architecture design, and further optimization deman
Externí odkaz:
http://arxiv.org/abs/2411.07569
Autor:
Wu, Yan-Feng, Hu, Jian-Qiang
We introduce ajdmom, a Python package designed for automatically deriving moment formulas for the well-established affine jump diffusion (AJD) processes. ajdmom can produce explicit closed-form expressions for moments or conditional moments of any or
Externí odkaz:
http://arxiv.org/abs/2411.06484
Addressing critical challenges in Lamb wave resonators, this paper presents the first validation of resonators incorporating sub-wavelength through-holes. Using the A3 mode resonator based on a LiNbO3 single-crystal thin film and operating in the K b
Externí odkaz:
http://arxiv.org/abs/2409.00783
We develop moment estimators for the parameters of affine stochastic volatility models. We first address the challenge of calculating moments for the models by introducing a recursive equation for deriving closed-form expressions for moments of any o
Externí odkaz:
http://arxiv.org/abs/2408.09185
Gradient-variation online learning aims to achieve regret guarantees that scale with variations in the gradients of online functions, which has been shown to be crucial for attaining fast convergence in games and robustness in stochastic optimization
Externí odkaz:
http://arxiv.org/abs/2408.09074
A standard two-qubit joint measurement is the well-known Bell state measurement (BSM), in which each reduced state (traced out one qubit) is the completely mixed state. Recently, a novel quantum joint measurement named elegant joint measurement (EJM)
Externí odkaz:
http://arxiv.org/abs/2408.06179