Scientists asked ChatGPT to solve a math problem from more than 2,000 years ago — how it answered it surprised them

We've wondered for centuries whether knowledge is latent and innate or learned and grasped through experience, and a new ...
IEEE

Research on the Construction and Application of Earthquake Emergency Information Knowledge Graph Based on Large Language Models

Abstract: To address the challenges of semantic parsing of multi-source heterogeneous information and the delayed emergency response decisions caused by insufficient relational reasoning capabilities ...

Researchers Propose a Novel Representation Learning Framework to Address the Lack of Causal Characterization in Deep Learning Models

Recently, researchers introduced a new representation learning framework that integrates causal inference with graph neural networks—CauSkelNet, which can be used to model the causal relationships and ...

Breakthrough in Quantum Network Routing: Combining Graph Neural Networks with Partially Observable Markov Decision Processes to Enhance Signal Transmission Efficiency

The core of quantum network research lies in efficiently and reliably establishing entanglement between nodes; however, the challenges of maintaining fragile quantum states are far more complex than ...
GitHub

VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context

This repository contains the official implementation of our ICML 2024 paper, VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context. VisionGraph, is a benchmark ...
IEEE

Study on the Application of Graph Theory Algorithms and Attack Graphs in Cybersecurity Assessment

Abstract: Postulating the behavior of attackers is important in the design of cybersecurity protection measures. Attack graph is a technique employed for this purpose, which aids in identifying and ...