ケモカイン・レセプターCCR2欠損マウスにおける,肝星状細胞・マクロファージの動員と新生血管形成の低下を伴った,肝内腫瘍の形成の減弱

取得学位 : 博士（医学）, 学位授与番号 : 医博甲第1719号 , 学位授与年月日 : 平成17年9月30日, 学位授与大学 : 金沢大

Context-sensitive graph representation learning

Graph Convolutional Network (GCN) is a powerful emerging deep learning technique for learning graph data. However, there are still some challenges for GCN. For example, the model is shallow; the performance is poor when labelled nodes are severely scarce. In this paper, we propose a Multi-Semantic Aligned Graph Convolutional Network (MSAGCN), which contains two fundamental operations: multi-angle aggregation and semantic alignment, to resolve two challenges simultaneously. The core of MSAGCN is the aggregation of nodes that belong to the same class from three perspectives: nodes, features, and graph structure, and expects the obtained node features to be mapped nearby. Specifically, multi-angle aggregation is applied to extract features from three angles of the labelled nodes, and semantic alignment is utilised to align the semantics in the extracted features to enhance the similar content from different angles. In this way, the problem of over-smoothing and over-fitting for GCN can be alleviated. We perform the node clustering task on three citation datasets, and the experimental results demonstrate that our method outperforms the state-of-the-art (SOTA) baselines

Feature recommendation strategy for graph convolutional network

Graph Convolutional Network (GCN) is a new method for extracting, learning, and inferencing graph data that builds an embedded representation of the target node by aggregating information from neighbouring nodes. GCN is decisive for node classification and link prediction tasks in recent research. Although the existing GCN performs well, we argue that the current design ignores the potential features of the node. In addition, the presence of features with low correlation to nodes can likewise limit the learning ability of the model. Due to the above two problems, we propose Feature Recommendation Strategy (FRS) for Graph Convolutional Network in this paper. The core of FRS is to employ a principled approach to capture both node-to-node and node-to-feature relationships for encoding, then recommending the maximum possible features of nodes and replacing low-correlation features, and finally using GCN for learning of features. We perform a node clustering task on three citation network datasets and experimentally demonstrate that FRS can improve learning on challenging tasks relative to state-of-the-art (SOTA) baselines

Hydrophobic Silica Microcavities with Sustainable Nonlinear Photonic Performance

Ultrahigh quality factor (Q) microcavities have been emerging as an appealing compact photonic platform for various applications. The Q factor plays a critical role in determining the nonlinear optical performance of a microcavity. However, a silica microcavity suffers from severe degradation of its Q value over time during storage or use in air due to the accumulating surface absorption loss, which would deteriorate their nonlinear photonic performance. Here, we report a new type of ultrahigh Q silica microcavity that effectively prevents the Q degradation over time. The Q values of the devices remain unchanged over time under storage in air. Optical frequency combs are generated with sustainable ultralow threshold performance in the course of time from the devices in open air. This approach would greatly facilitate ultrahigh Q silica-based photonic devices for next generation photonic applications