Barron's

Fed Officials Signal More Cuts Could Come in 2026: Minutes

Federal Reserve officials were more divided over December’s rate cut than the tally of votes from the meeting suggested. The minutes showed that several policymakers who supported the move said they ...
IEEE

Control Mode Switching-Enabled Physics- Guided Multiagent Graph Learning for Real-Time AC/DC Power Flow

Abstract: Existing ac/dc power flow computations necessitate sequential convergence-oriented trial-and-error under various dc control modes, rising computational ...
IEEE

Graph Convolutional Network with Local Topology and Spectral Feature Representation for Multispectral Point Cloud Classification

Abstract: Multispectral LiDAR contributes to the rapid acquisition of 3D spatial and spectral information of land covers, providing more comprehensive features for classification. Despite the ...
IEEE

Music-based Graph Convolution Neural Network with ECG, Respiration, Pulse Signal as a Diagnostic Tool for Hypertension

Abstract: Hypertension is one of the prime risk factors of cardiovascular disease. Music has been shown to be beneficial for lowering blood pressure. Here, we investigate if music can help in ...
IEEE

Enhancing EEG Motor Imagery Time Point Signal Classification through Reinforcement Learning and Graph Neural Networks

Abstract: Brain-Computer Interfaces (BCIs) rely on accurately decoding electroencephalography (EEG) motor imagery (MI) signals for effective device control. Graph Neural Networks (GNNs) outperform ...
IEEE

Petri net-based QUBO Model Formulation for Multi-Resource Flow-Shop Scheduling Problems

Abstract: This paper introduces a Petri net-based approach to addressing multi-resource flow-shop scheduling problems within multi-objective quantum optimization. The multi-resource flow-shop problem, ...
IEEE

Multi-Person Pose Tracking With Sparse Key-Point Flow Estimation and Hierarchical Graph Distance Minimization

Abstract: In this paper, we propose a novel framework for multi-person pose estimation and tracking on challenging scenarios. In view of occlusions and motion blurs which hinder the performance of ...
IEEE

Multi-modal Dynamic Point Cloud Geometric Compression Based on Bidirectional Recurrent Scene Flow*

Abstract: Deep learning methods have recently shown significant promise in compressing the geometric features of point clouds. However, challenges arise when consecutive point clouds contain holes, ...
IEEE

Optimizing Access Point Placement in Industrial IoT: A Deep Reinforcement Learning Approach With Q-Learning Verification and Signal Heatmap Visualization

Abstract: Optimal access point (AP) placement inside an industrial layout is important to ensure excellent connectivity. However, wireless fidelity (Wi-Fi) AP placement is complicated because ...
IEEE

Edge Enhancement Oriented Graph Convolutional Networks for Point Cloud Segmentation

Abstract: Aiming at the problem of poor edge effect segmentation in point cloud segmentation, which fails to fully utilize the correlation between the local geometric and semantic features of point ...
IEEE

Visual Evolutionary Optimization on Graph-Structured Combinatorial Problems With MLLMs: A Case Study of Influence Maximization

Abstract: Graph-structured combinatorial problems in complex networks are prevalent in many domains, and are computationally demanding due to their complexity and non-linear nature. Traditional ...
IEEE

Dynamic Graph Convolutional Recurrent Network With Spatiotemporal Category Information Embedding for Traffic Flow Prediction

Abstract: Traffic flow prediction is a challenging spatiotemporal prediction task due to its spatiotemporal dynamics and uncertainty. In recent years, graph convolutional neural networks (GCNs) have ...