Výsledky vyhledávání - "Huang, Jinghan"

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Topological Cycle Graph Attention Network for Brain Functional Connectivity

Autor: Huang, Jinghan, Chen, Nanguang, Qiu, Anqi

This study, we introduce a novel Topological Cycle Graph Attention Network (CycGAT), designed to delineate a functional backbone within brain functional graph--key pathways essential for signal transmissio--from non-essential, redundant connections t

Externí odkaz: http://arxiv.org/abs/2403.19149

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Advancing Graph Neural Networks with HL-HGAT: A Hodge-Laplacian and Attention Mechanism Approach for Heterogeneous Graph-Structured Data

Autor: Huang, Jinghan, Chen, Qiufeng, Bian, Yijun, Zhu, Pengli, Chen, Nanguang, Chung, Moo K., Qiu, Anqi

Graph neural networks (GNNs) have proven effective in capturing relationships among nodes in a graph. This study introduces a novel perspective by considering a graph as a simplicial complex, encompassing nodes, edges, triangles, and $k$-simplices, e

Externí odkaz: http://arxiv.org/abs/2403.06687

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Demystifying CXL Memory with Genuine CXL-Ready Systems and Devices

Autor: Sun, Yan, Yuan, Yifan, Yu, Zeduo, Kuper, Reese, Song, Chihun, Huang, Jinghan, Ji, Houxiang, Agarwal, Siddharth, Lou, Jiaqi, Jeong, Ipoom, Wang, Ren, Ahn, Jung Ho, Xu, Tianyin, Kim, Nam Sung

The ever-growing demands for memory with larger capacity and higher bandwidth have driven recent innovations on memory expansion and disaggregation technologies based on Compute eXpress Link (CXL). Especially, CXL-based memory expansion technology ha

Externí odkaz: http://arxiv.org/abs/2303.15375

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Heterogeneous Graph Convolutional Neural Network via Hodge-Laplacian for Brain Functional Data

Autor: Huang, Jinghan, Chung, Moo K., Qiu, Anqi

Publikováno v: IPMI 2023

This study proposes a novel heterogeneous graph convolutional neural network (HGCNN) to handle complex brain fMRI data at regional and across-region levels. We introduce a generic formulation of spectral filters on heterogeneous graphs by introducing

Externí odkaz: http://arxiv.org/abs/2302.09323

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ORCA: A Network and Architecture Co-design for Offloading us-scale Datacenter Applications

Autor: Yuan, Yifan, Huang, Jinghan, Sun, Yan, Wang, Tianchen, Nelson, Jacob, Ports, Dan R. K., Wang, Yipeng, Wang, Ren, Tai, Charlie, Kim, Nam Sung

Responding to the "datacenter tax" and "killer microseconds" problems for datacenter applications, diverse solutions including Smart NIC-based ones have been proposed. Nonetheless, they often suffer from high overhead of communications over network a

Externí odkaz: http://arxiv.org/abs/2203.08906

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