IEEE

Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers

Abstract: Forecasting within signal processing pipelines is crucial for mitigating delays, particularly in predicting the dynamic movements of objects such as NBA players. This task poses significant ...
TechAnnouncer

Discover the Best Python Book PDF for Your Learning Journey

Finding the right book can make a big difference, especially when you’re just starting out or trying to get better. We’ve ...
GitHub

UrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate Prediction

This repository contains the official PyTorch implementation and the UMC4/12 Dataset for the paper: [UrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate ...
IEEE

ScaDyG: A New Paradigm for Large-Scale Dynamic Graph Learning

Abstract: Dynamic graphs (DGs), which capture time-evolving relationships between graph entities, have widespread real-world applications. To efficiently encode DGs for downstream tasks, most DG ...
Hacker

Your First Interactive Plot in Python: A Hands-On Plotly Guide

A software engineer and book author with many years of experience, I have dedicated my career to the art of automation. A software engineer and book author with many years of experience, I have ...