graph neural networks

Benchmarking Deformable-Attention Graph Networks and Other Aggregators on Prov-GigaPath Features for Canine Cutaneous Tumor Classification

Whole-slide image (WSI) classification in veterinary computational pathology usually freezes a human-pretrained pathology foundation model as the tile encoder and trains a light slide-level aggregator on top. Whether the choice of aggregator matters for small canine cohorts has not been tested under a common protocol....

Predicting LogP values using multi-strategy Graph Neural Networks: a complementary approach to protein-based biomarker discovery in cancer research

Predictive Response Engine for Dynamic Intelligence and Cyber Threat Mitigation (PRE-DICT): Architecture, Mathematical Formulation, and Empirical Evaluation

Modern enterprise infrastructures face threats from complex multi-stage cyber attacks carried out by smart and evolving adversaries. Traditional tools like Security Information and Event Management (SIEM) and Security Orchestration, Automation and Response (SOAR) operate in a way. They depend on Indicators of Compromis...

TrajGNN-RL: Adaptive Mobile Sink Intelligence for Lifetime-Aware Wireless Sensor Networks

Mobile wireless sensor networks require adaptive routing and sink mobility strategies to reduce energy imbalance, congestion, and communication delay. In this study, we propose a new method called TrajGNN-RL that combines the topology learning method based on a graph neural network with the deep reinforcement learning...

Prediction of MOF Adsorption Isotherms and High-Throughput Screening Using a Physics-Informed Membranes Graph Neural Network

Metal-organic frameworks (MOFs) are prime CO 2 -capture candidates, but grand canonical Monte Carlo (GCMC) screening is costly when full isotherms, not single uptakes, are required; machine learning surrogates rarely resolve the continuous pressure response. We report a physics-informed pressure-aware multi-task graph...

Correction: The body does not keep the score: trauma, predictive coding, and the restoration of metastability

1. Geraerts et al. (2007) was described as establishing that suggestion can produce damaging false memories and somatic feelings of trauma. The study examined corroboration of reported memories and did not establish the claim as stated. A correction has been made to the section main text, paragraph 14, beginning "No on...

Spatio-Temporal Disinformation Detection Using GNN–LSTM and Cross-Attention for South China Sea Information Analysis

The rapid propagation of disinformation through social media presents a significant challenge because information diffusion is characterized by complex interactions among users and continuously evolving temporal patterns. Conventional deep-learning and transformer-based approaches primarily focus on textual or sequenti...

A review on advances in chemical compound toxicology prediction

The accurate prediction of chemical toxicity remains a major challenge in drug development and chemical safety assessment because conventional approaches can be costly, time-consuming, ethically constrained, and limited in predictive performance. This review examines how artificial intelligence (AI), particularly machi...

Geometry vs Binary: Quantum Physics Puzzles — quantum geometry problems where classical geometri — E8 Intelligence Research

## Binary Failure → Geometry Solution **Analysis of Findings:** The search results point to a critical, often-overlooked failure mode in quantum machine learning: **the assumption that quantum states and their evolution can be effectively modeled as generic high-dimensional statistical distributions.** Melanie Weber's...

Lenticulum.jl: learning relations on factor graphs

Lenticulum.jl is a Julia library for learning relations instead of functions. A neural network learns a map from inputs to outputs and runs in one direction. Lenticulum learns a relation on a joint space and decides at query time which variables are observed and which are inferred, so one trained model answers "y given...

Foundations of Multivariate Learning

Many modern scientific studies record more variables than observations. A single experiment may measure thousands of quantities while collecting only hundreds of samples. Inthis regime the classical multivariate toolbox stops working. Principal component analysis,the standard tool for keeping the few strongest patterns...

FedGNN cross-silo fraud detection: reproducibility code and results

Code and results reproducing every number, table, and figure in “Towards Reliable Federated Graph Neural Networks for Fraud Detection in Small Cross-Silo Consortia: New Evidence on Heterogeneity, Robust Aggregation, and Schema Alignment” (submitted to IEEE Access). All experiments run on the public YelpChi and Amazon f...