graph neural networks

Congestion Propagation Identification and Prediction Using Self Attention–Based Diffusion Convolutional Approach

A propagation probability matrix is utilizes to identify congestion propagation patterns and finds traffic behavior over 24 h, revealing critical insights into congestion trends in a selected road network and proposing a novel self attention–based diffusion convolutional network (SADCN) that effectively predicts traffi...

Dynamic Semantic Topology Learning and Program Reasoning

This paper introduces a novel approach to program understanding and optimization, termed Dynamic Semantic Topology Learning and Program Reasoning. The core claim is that program behavior is not solely determined by its source code, but significantly influenced by the dynamic semantic relationship network within its run...

Based on Multi-modal Data Fusion for Subconscious Decision Simulation

This paper presents a novel approach to simulating subconscious decision-making processes by leveraging multi-modal data fusion. The core idea is to construct a computational model capable of mirroring the complexities of human subconscious decision-making, moving beyond traditional behavioral analysis. We employ a gra...

Learning Topological Features of $\widehat Z$-invariants

This paper initiates a systematic approach to handling mathematical data structured as (truncated) infinite $q-series, or equivalently, infinite series of integers, and demonstrates that neural networks can reliably extract essential topological information, such as homology class and underlying graph structure, direct...

Physics-structured graph neural networks for interpretable fault detection in semiconductor manufacturing processes

Integrating ENSO dynamics into physics-informed Graph Neural Networks for extreme evapotranspiration prediction under climate change

A physics-informed wavelet-based multi-scale graph neural network for aero-engine remaining useful life prediction

Parallel Dual-Level Attention-Enhanced Semantic Segmentation of 3D Point Clouds in Tunnel Construction

Validated on real tunnel data, PDAPointNet++ outperforms models such as PointNet, PointNet++, PointMLP, and dynamic graph convolutional neural network (DGCNN) and the impact of different WDFL balancing coefficients on model performance is discussed.

Graph neural network enhanced large language model for commonsense question answering

Bacteria tracking and life cycle state classification using graph neural networks and pretrained vision transformers.

This work proposes a unified framework based on Graph Neural Networks for simultaneous bacteria tracking, division detection, and life cycle state classification and introduces a network-based approach for segmentation error correction using division and multi-frame correspondence predictions.

PROTEUS: A 40 nm Programmable General-Purpose Digital Compute-In-Memory Accelerator With eNVM and Hierarchical ISA for Versatile Edge AI

We present PROTEUS, an 18 mm2 programmable general-purpose digital compute-in-memory (GP-DCIM) accelerator integrating 4 Mb resistive random access memory (RRAM) and 2.6 Mb tensor static random access memory (SRAM) with a 32-bit hierarchical DCIM instruction set architecture (ISA). PROTEUS features fine-grained 1-D mat...

MADGCN: A Meteorology-Aware Spatio-Temporal Graph Convolution Network for Long-Term Air Pollution Forecasting

Air quality forecasting has attracted increasing attention as global air pollution worsens. Spatiotemporal graph neural networks have become a leading paradigm, thanks to their ability to capture complex spatial and temporal dynamics in Air Quality Index (AQI) data. However, existing methods remain limited by weak mode...