Zobrazeno 1 - 10
of 4 827
pro vyhledávání: '"BAI, Lu"'
In the future sixth-generation (6G) era, to support accurate localization sensing and efficient communication link establishment for intelligent agents, a comprehensive understanding of the surrounding environment and proper channel modeling are indi
Externí odkaz:
http://arxiv.org/abs/2411.03711
Collagen, a key structural component of the extracellular matrix, undergoes significant remodeling during carcinogenesis. However, the important role of collagen levels in breast cancer diagnostics still lacks effective in vivo detection techniques t
Externí odkaz:
http://arxiv.org/abs/2410.08496
Noninvasive in vivo detection of collagen facilitates the investigation of mechanisms by which cancer-associated fibroblast (CAF) regulates the extracellular matrix. This study explored the feasibility of photoacoustic spectrum analysis (PASA) in ide
Externí odkaz:
http://arxiv.org/abs/2410.03324
Graph learning methods have been extensively applied in diverse application areas. However, what kind of inherent graph properties e.g. graph proximity, graph structural information has been encoded into graph representation learning for downstream t
Externí odkaz:
http://arxiv.org/abs/2408.03877
Infrared and visible image fusion has been developed from vision perception oriented fusion methods to strategies which both consider the vision perception and high-level vision task. However, the existing task-driven methods fail to address the doma
Externí odkaz:
http://arxiv.org/abs/2407.10047
In this paper, a novel multi-modal intelligent vehicular channel model is proposed by scatterer recognition from light detection and ranging (LiDAR) point clouds via Synesthesia of Machines (SoM). The proposed model can support the design of intellig
Externí odkaz:
http://arxiv.org/abs/2406.19072
Autor:
Qu, Lun, Wu, Wei, Zhang, Di, Wang, Chenxiong, Bai, Lu, Li, Chenyang, Cai, Wei, Ren, Mengxin, Alù, Andrea, Xu, Jingjun
This paper explores the interplay of momentum transfer and nonlinear optical processes through moir\'e phenomena. Momentum transfer plays a crucial role in the interaction between photons and matter. Here, we study stacked metasurfaces with tailored
Externí odkaz:
http://arxiv.org/abs/2406.14133
Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis. To this end, duri
Externí odkaz:
http://arxiv.org/abs/2405.14742
Graph Neural Networks (GNNs) are powerful tools for graph classification. One important operation for GNNs is the downsampling or pooling that can learn effective embeddings from the node representations. In this paper, we propose a new hierarchical
Externí odkaz:
http://arxiv.org/abs/2405.10218
Autor:
He, Zhengqing, Qu, Lun, Wu, Wei, Liu, Jikun, You, Jingfei, Liu, Weiye, Bai, Lu, Jin, Chunyan, Wang, Chenxiong, Gu, Zhidong, Cai, Wei, Ren, Mengxin, Xu, Jingjun
Tunable nonlinearity facilitates the creation of reconfigurable nonlinear metasurfaces, enabling innovative applications in signal processing, light switching, and sensing. This paper presents a novel approach to electrically modulate SHG from a lith
Externí odkaz:
http://arxiv.org/abs/2404.07598