graph neural networks

Topology, Autocatalysis, and Epigenetic Control: How Network Architecture Shapes Biological State Transitions Across Scales

Version 3 (2026-09-26) corrects errors found by an independent audit of version 2 and by re-checking every cited arXiv abstract. It removes two overstatements from the abstract (that autocatalytic completeness defines a lower bound on network complexity, and that topological data analysis and GNN attribution independen...

Trained checkpoints: graph neural network Hamiltonian and direct models for DNA charge transport

Trained PyTorch checkpoints for every run reported in the accompanying paper: graph neural network (GAT) models that predict DNA transmission and density-of-states spectra, either through a learned reduced-order Hamiltonian passed through an NEGF layer (Hamiltonian model) or directly from pooled graph features (direct...

Editorial: Advances in neuroimaging, genetic, and stimulation methods for cognitive and behavioral function investigation

Understanding cognitive and behavioral function increasingly requires linking distributed neural activity with physiological, clinical, and system-level processes. Advances in neuroimaging, genetic and epigenetic approaches, and non-invasive brain stimulation, alongside quantitative modeling and electrophysiology, now...

AI-Augmented Logic Synthesis for Edge-Deployed Swimmer Detection Systems

Vision-based swimmer and drowning detection systems, built primarily on lightweight YOLO architectures, have achieved measurable gains in accuracy while shrinking model size for resource-constrained edge hardware. However, Field-Programmable Gate Array (FPGA) accelerators hosting these models continue to rely on conven...

Topology-aware E(3)-equivariant learning for physically consistent piezoelectric tensor prediction

Accurate prediction of piezoelectric tensors in crystals remains challenging because valid responses must satisfy both continuous geometric symmetries and discrete crystallographic constraints under periodic boundary conditions. Existing learning-based approaches typically emphasize equivariance or periodic geometry, b...

A lightweight evidence-guided acoustic diagnostic network for automotive component fault detection in maintenance scenarios

Non-contact acoustic diagnosis is attractive for automotive maintenance because abnormal sounds can be recorded without installing additional sensors on compact or inaccessible components. Practical service recordings, however, are affected by low-frequency dominance, transient impacts, device differences, propagatio...

Messages Passed Along the Edges: A Contemporary Synthesis Review of Graph Neural Networks for Relational Data

This article presents a narrative review of Graph Neural Networks for Relational Data in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive...

TransDyG: An Anomaly Detection Method Integrating Dynamic Graphs and Transformers

marimo-flow

Reactive marimo notebooks for ML experimentation, with MLflow tracking, PINA physics-informed neural networks, and a multi-agent team built on pydantic-graph and Ollama Cloud.

Graph Neural Networks for Predicting Digital Circuit Performance in Electronic Design Automation

Electronic Design Automation (EDA) is important in the development of modern digitalcircuits because it helps designers analyze and improve circuits before they are manufactured. Ascircuits become more complex, traditional methods can require many design iterations beforeimportant performance factors can be estimated a...

Parsing Hierarchical Document Structure with Graph Neural Networks

Research on cross-ledger synchronization of real-time accounting system based on Spark Streaming and YARN scheduling

Abstract This paper proposes a real-time accounting system that integrates Spark Streaming (Spark Streaming -based stream execution) with YARN priority scheduling to mitigate task latency, resource imbalance, and cross-ledger inconsistency in large-scale streaming accounting workloads. A priority-aware scheduling frame...