graph neural networks

REMIND: Processed Spatiotemporal Forecasting Datasets for Traffic, Energy, and Hydrology

This dataset collection provides processed data used in REMIND experiments for spatiotemporal graph forecasting across traffic, energy, and hydrology domains. When to Trust Memory: Retrieval-Guided Probabilistic Spatiotemporal Forecasting under Distribution Shift. (NeurIPS 2026) The collection contains four dataset var...

AI-Driven Models for Organ-Specific Toxicity Assessment

Drug-induced toxicity is a major cause of late-stage drug attrition and post-marketing withdrawal. Conventional in vivo and in vitro toxicity assays are costly, time-consuming, ethically limited, and often poorly predictive. AI-driven in silico toxicology, integrating machine learning, deep learning, and graph neural n...

Topology Conditioned Physics Informed Graph Surrogates for AC Optimal Power Flow Under Line Outages

Security assessment requires repeated alternating current optimal power flow (AC OPF) solutions under changing injections and line outages. We propose a topology conditioned physics informed graph Fourier neural operator (GFNO PINO) that predicts voltage phasors and dispatch from network and operating data. Chebyshev f...

Optical and Photonic Computing for Deep Learning Acceleration: Silicon Photonics, Mach-Zehnder Meshes, and Wavelength-Division Multiplexing

The relentless exponential scaling of foundation models and transformer architectures has pushed conventional silicon complementary metal-oxide-semiconductor (CMOS) digital accelerators (GPUs, TPUs) toward fundamental thermodynamic and interconnect boundaries. Joule heating, resistance-capacitance (RC) interconnect del...

Effects of dance and music training on the plasticity of the core empathy network: intra- and inter-network connectivity

Background Dance and music are two of the oldest and most universal forms of artistic expression, each requiring extensive training. Prolonged training is known to induce neuroplastic changes and enhance socio-emotional abilities such as empathy. The mirror neuron system (MNS) is associated with empathy; however, few s...

PI-STGNN model-driven risk assessment method for leapfrog tripping in photovoltaic power plants

Unintended fault propagation in photovoltaic plants stems primarily from collector-line leapfrog tripping, a hazard characterized by multi-factor coupling, time-varying dynamics and hidden causal links that remain difficult to fully characterize under complex operating conditions. To tackle this challenge, this work pr...

Quantum-Inspired Variational Optimization of neuromorphic self-supervised heterogeneous graph transformers for multimodal Parkinson’s disease representation learning

Early diagnosis of Parkinson’s disease (PD) remains challenging due to the heterogeneous nature of multimodal clinical data, limited annotated datasets, suboptimal multimodal fusion strategies, and the absence of uncertainty-aware decision-making in existing deep learning models. This paper proposes NeuroQ-MGST, a Neur...

A hybrid spatio-temporal deep learning framework for urban travel time prediction and intelligent routing: Integrating recurrent and graph neural networks

Accurate short-term travel-time forecasting is important for intelligent transportation systems, but reported gains can be misleading when temporal leakage, ambiguous graph definitions, normalized error units, weak baselines, or untested routing claims are not controlled. This study evaluates an integrated forecasting...

THE EL-RAKHAWI 24/7 QUANTUM-ENHANCED SOLAR ENERGY ARCHITECTURE (QESEA) An Integrated Framework for Singlet Fission-Augmented Perovskite Tandems, AI-Discovered 2D/3D Stabilization, and Thermoradiative Night-Time Generation

THE EL-RAKHAWI 24/7 QUANTUM-ENHANCED SOLAR ENERGY ARCHITECTURE (QESEA) by Dr. M.K.A. El-Rakhawi (DOI: 10.5281/zenodo.23548358) presents a revolutionary framework shattering the Shockley-Queisser limit and solar intermittency through four synergistic pillars. **Core Innovations:**1. **Singlet Fission Augmentation**: Org...

Enhanced Facial Micro Emotion Detection using Optimized Temporal Attention Recurrent Graph Convolutional Neural Network

Micro-expressions are brief natural facial expressions that reveal concealed emotions. However, traditional facial expression recognition (FER) methods struggled due to minute expression variations, limited dataset availability, and difference in lighting and camera angles, which complicates the classification process....

scHGNN: A single-cell gene regulatory network inference method based on heuristic graph neural networks

Deep Reinforcement Learning with Graph Neural Networks for Multi-Commodity Multi-Modal Network Flow Optimization in Humanitarian Logistics

This study aims to investigate the impact of deep reinforcement learning (DRL) augmented with graph attention networks (GAT) on the efficiency and equity of humanitarian logistics operations. It addresses a critical gap in the existing literature regarding the real-time scalability of multi-commodity, multi-modal netwo...