Evolutionary graph generation with terminal-colour constraint for heterogeneous circuit synthesis

2001 ◽  
Vol 37 (13) ◽  
pp. 808 ◽  
Author(s):  
M. Natsui ◽  
T. Aoki ◽  
T. Higuchi
Keyword(s):  
Circuit Synthesis ◽  
Graph Generation

Attack Graph Generation and Visualization for Industrial Control Network

2020 ◽  
Author(s):  
Yanli Feng ◽  
Gongliang Sun ◽  
Zhiyao Liu ◽  
Chenrui Wu ◽  
Xiaoyang Zhu ◽  
...  
Keyword(s):  
Control Network ◽  
Industrial Control ◽  
Attack Graph ◽  
Graph Generation

Subgraph and object context-masked network for scene graph generation

2020 ◽  
Vol 14 (7) ◽  
pp. 546-553
Author(s):  
Zhenxing Zheng ◽  
Zhendong Li ◽  
Gaoyun An ◽  
Songhe Feng
Keyword(s):  
Scene Graph ◽  
Graph Generation

Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

2021 ◽  
Vol 10 (7) ◽  
pp. 488
Author(s):  
Peng Li ◽  
Dezheng Zhang ◽  
Aziguli Wulamu ◽  
Xin Liu ◽  
Peng Chen
Keyword(s):  
Remote Sensing ◽  
Scene Understanding ◽  
Deep Understanding ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Scene Graph ◽  
Multi Scale ◽  
Relationship Extraction ◽  
High Level ◽  
Graph Generation

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.

Towards Optimal Topology Aware Quantum Circuit Synthesis

2020 ◽  
Author(s):  
Marc G. Davis ◽  
Ethan Smith ◽  
Ana Tudor ◽  
Koushik Sen ◽  
Irfan Siddiqi ◽  
...  
Keyword(s):  
Quantum Circuit ◽  
Optimal Topology ◽  
Circuit Synthesis
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