graph neural networks

Demand prediction model based on spatiotemporal graph neural network and external knowledge fusion

A novel hybrid prediction model STKG-DemandNet that integrates spatio-temporal graph neural networks and external knowledge and integrates multi-source external knowledge to achieve fine-grained and highly robust demand forecasting is proposed.

DGCR-Net: Dynamic Graph Contextual Reasoning Network for Semantic Segmentation of Remote Sensing Imagery

DGCR-Net integrates a ResNet18 encoder with a multi-stage decoder composed of cascaded dynamic graph reasoning blocks (DGRBs), which adaptively infer complex contextual dependencies among irregular objects and progressively refine multi-scale semantic representations, ensuring robust contextual reasoning.

Autonomous Ground Vehicles in Robotics: Kinematics, Dynamics, and Fleet Control

An open-access curriculum module examining the kinematics, dynamic control, perception architectures, and multi-agent coordination of Autonomous Ground Vehicles (AGVs). Details unicycle (differential-drive) and Ackermann bicycle steering models, tire-road lateral friction boundaries, Stanley and Pure Pursuit path-track...

An e-commerce agricultural product recommendation algorithm based on intention perception and graph neural networks

Evaluation of student social networks using graph neural networks (GNN)

A Multi-Channel Decoupled Attention Graph Neural Network (MCDA-GNN) is proposed for the comprehensive evaluation of student social networks, providing a scientific basis for personalized educational interventions, campus community optimization, and mental health early warning.

Decoding microstructure-property relationships in polycrystals: an explainable GNN surrogate for crystal plasticity

In this work, a surrogate modeling approach based on graph neural networks (GNNs) is presented to rapidly predict the homogenized stress-strain response of polycrystalline microstructure volume elements (MVEs). Each grain is represented as a node in a graph, with features such as crystallographic orientation, grain siz...

DeepMoCo: graph neural network-based adaptive correction for motion artifacts in fluorescence microscopy

DeepMoCo is proposed, an innovative deep learning-based framework that integrates a graph neural network (GNN) to establish spatiotemporal feature correlations between biological motion patterns and imaging artifacts and presents a robust and universal solution for high-precision in vivo fluorescence imaging studies.

Biopesticidal Potential of the Cannabis sativa L. Metabolites: A Denoised, Docking-Informed QSAR Model

Overall, the combined structural and docking-informed workflow identified a small, chemically diverse set of high-ranking C. sativa compounds that can now be prioritized for experimental validation.

Deterministic Topological Retrieval-Augmented Generation via Discrete Dynamical Basins

Retrieval-Augmented Generation (RAG) systems navigate a fundamental trade-off between the computational simplicity of flat vector indices and the structural richness of graph representations. Standard dense retrieval collapses document discourse into isolated embeddings, causing semantic fragmentation and context loss....

Learning Molecular Representations from Imperfect Experimental Data: Models, Datasets, and Benchmarking for Biochemical Systems

This dissertation develops machine learning methods for learning molecular and biomolecular representations directly from imperfect experimental data and for evaluating such models in a principled and reproducible manner. The first part focuses on representation learning for the Nuclear Magnetic Resonance (NMR) spectra...

Spatially guided translation from histology images to transcriptomic profiles using foundation model-driven contrastive learning

Spatial transcriptomics (ST) enhances single-cell RNA sequencing by revealing transcript distribution, offering critical insights into heterogeneous diseases such as breast cancer. However, the high cost and lengthy processes of generating high-quality ST data limit clinical application. Recent deep learning methods pr...

A graph-based reinforcement-learning framework for logical qubit allocation in fault-tolerant quantum architectures

A graph-based reinforcement-learning framework is proposed in which a graph neural network is first pretrained to predict the ancilla-qubit cost of circuit-allocation pairs from allocation-aware circuit graphs, and the resulting encoder is transferred to a reinforcement learning agent that assigns tiles one at a time.