Researchers Are Using A.I. to Decode the Human Genome

AlphaGenome is a leap forward in the ability to study the human blueprint. But the fine workings of our DNA are still largely ...
Psychology Today

Mental Murmuration: A Metaphor for the Workings of the Brain

Modern neuroscience and the computational modeling of the activities of vast, integrated neural networks provide fruitful accounts of how our minds work and learn.
Scientific Research Publishing

Geo-Refined Point Transformer: Coordinate-Aware Excitation and Positional Upsampling for 3D Scene Segmentation ()

The proposed Coordinate-Aware Feature Excitation (CAFE) module and Position-Aware Upsampling (Pos-Up) module both adhere to ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
Frontiers

Multi-Scale graph wavelet convolutional network for hyperspectral image classification

Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...
IEEE

A Holographic Blockchain-Enabled Social Group Consensus Method Using Graph Convolutional Networks With Optimization-Based Sentiment Analysis for IoT Consumer Electronics

Abstract: The integration of holographic counterpart technologies (HCT) and Internet of Things (IoT) has transformed consumer electronics (CE). In this context, consumers typically choose among ...
Frontiers

Association prediction of lncRNAs and diseases using multiview graph convolution neural network

1 College of Computer Science and Engineering, Changsha University, Changsha, Hunan, China 2 Department of Information and Computing Science, College of Mathematics, Changsha University, Changsha, ...
IEEE

Label Guided Graph Optimized Convolutional Network for Semi-Supervised Learning

Abstract: Graph Convolutional Networks (GCNs) have been widely studied for semi-supervised learning tasks. It is known that the graph convolution operations in most of existing GCNs are composed of ...