TMCnet

MicroCloud Hologram Inc. Releases Hybrid Quantum-Classical Three-Dimensional Object Technology for Multi-Channel Quantum Convolutional Neural Networks

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released a forward-looking technological achievement: the hybrid quantum-classical three-dimensional ...
Law

The Distinction Between Abuse of Process and Wrongful Use of Civil Proceedings

The Distinction Between Abuse of Process and Wrongful Use of Civil Proceedings Some recent opinions have highlighted one of the key differences between the two types of claims, the distinction between ...
Scientific Research Publishing

Research on Structural Damage Identification Method Based on Convolutional Neural Network ()

1 College of Finance and Commerce, Guangzhou Railway Polytechnic, Guangzhou, China. 2 School of Intelligent Construction and Civil Engineering, Zhongyuan University of Technology, Zhengzhou, China. 3 ...
Frontiers

High-efficiency sparse convolution operator for event-based cameras

Event-based cameras are bio-inspired vision sensors that mimic the sparse and asynchronous activation of the animal retina, offering advantages such as low latency and low computational load in ...
IEEE

From Cluster Assumption to Graph Convolution: Graph-Based Semi-Supervised Learning Revisited

Abstract: Graph-based semi-supervised learning (GSSL) has long been a research focus. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional ...
Frontiers

The super-resolution reconstruction algorithm of multi-scale dilated convolution residual network

Aiming at the problems of traditional image super-resolution reconstruction algorithms in the image reconstruction process, such as small receptive field, insufficient multi-scale feature extraction, ...
IEEE

Graph Multi-Convolution and Attention Pooling for Graph Classification

Abstract: Many studies have achieved excellent performance in analyzing graph-structured data. However, learning graph-level representations for graph classification is still a challenging task.