graph neural networks

AIモデルの蒸留・盗用問題と量子実用化(Q-day)による知的所有権の再構築に関する総合調査報告 (英語副題: Comprehensive Investigation on AI Model Distillation, IP Theft, and Post-Q-Day Intellectual Property Restructuring)

日本語概要 (Japanese Abstract) 本調査報告書は、2025年末から2026年にかけて激化した最先端AIフロンティアモデルに対する「産業規模の敵対的蒸留攻撃(Adversarial Distillation Attacks)」の実態を網羅的に分析し、目前に迫る量子コンピューティングの実用化(Q-day)がAIの知的所有権(IP)、モデルの透明性、および産業構造に与える不可逆的な地...

River ST-GNN Forecast: code and trained weights for river-stage forecasting in Ascension Parish, Louisiana

River ST-GNN Forecast provides research code and trained weights for the manuscript "An operational river-stage forecasting system using spatiotemporal graph neural network (ST-GNN) in Ascension Parish, Louisiana", prepared for submission to Hydrology and Earth System Sciences (HESS). The software compares a spatiotemp...

Dataset for "An operational river-stage forecasting system using spatiotemporal graph neural network (ST-GNN) in Ascension Parish, Louisiana"

This dataset accompanies the manuscript "An operational river-stage forecasting system using spatiotemporal graph neural network (ST-GNN) in Ascension Parish, Louisiana", prepared for submission to Hydrology and Earth System Sciences (HESS). It provides the evaluation data for a 68-gauge forecasting network comprising...

The Council That Never Forgets: Resolving the Stability-Plasticity Dilemma via Topological Prime Anchoring and Continual Multi-Agent Deliberation

Full Summary: The Council That Never Forgets Title: The Council That Never Forgets: Resolving the Stability-Plasticity Dilemma via Topological Prime Anchoring and Continual Multi-Agent Deliberation Author: Frank Morales Aguilera, Sovereign Machine Laboratory (SOMALA), Montreal, Quebec, Canada Date: October 1, 2026 Stat...

Microstate-Guided Dynamic Graph Neural Network with Prototypical Contrastive Learning for EEG Emotion Recognition

The Council That Never Forgets: Resolving the Stability-Plasticity Dilemma via Topological Prime Anchoring and Continual Multi-Agent Deliberation

Full Summary: The Council That Never Forgets Title: The Council That Never Forgets: Resolving the Stability-Plasticity Dilemma via Topological Prime Anchoring and Continual Multi-Agent Deliberation Author: Frank Morales Aguilera, Sovereign Machine Laboratory (SOMALA), Montreal, Quebec, Canada Date: October 1, 2026 Stat...

EEG Signal-based Emotion Classification Using Channel Importance in Dual-path Graph Neural Networks and Cheetah Optimization Method

Neuroeconometrics and Neuroconnectometrics: Mathematical Foundations, Causal Identification, and Topological Manifolds

Neuroeconometrics and Neuroconnectometrics: Mathematical Foundations, Causal Identification, and Topological Manifolds presents an axiomatic, measure-theoretic, and differential-topological foundation unifying decision neurobiology, high-dimensional econometrics, and structural connectomics. The monograph resolves the...

SAGE: Signal-Amplified Guided Embeddings for Vulnerability Detection

Software vulnerabilities are a primary threat to modern infrastructure. While static analysis and Graph Neural Networks have long served as the foundation for vulnerability detection, the emergence of Large Language Models (LLMs) has introduced a transformative paradigm driven by superior semantic reasoning and cross-e...

Decoding brain anatomy from neuronal neighborhoods with MYCEL

Extracellular recordings carry information about neuronal location, and pooling per-unit predictions across neighboring electrodes improves its readout, but whether this local pooling should be learned is untested. We present MYCEL (Message-passing Yields Cellular Embeddings of Location), a graph neural network whose n...

DeepGreenGO: a graph neural network-based deep learning model for plant-specific protein function prediction

Automated protein function prediction remains challenging in plants because experimentally supported annotations are limited, particularly for crop and non-model plant species. Integrating deep learning techniques in plant molecular biology can offer a transformative opportunity to innovate research and support sustain...

Graph-Aware Reinforcement Learning for Adaptive APT Threat Hunting in Dynamic Enterprise Networks

Advanced Persistent Threats (APTs) are particularly challenging for enterprise intrusion detection because they are time-evolving, distributed, and difficult to detect under changing network conditions. Conventional machine-learning-based intrusion detection systems (IDSs) often rely on node-level features and may be v...