Q.ANT, the pioneer in commercial photonic computing, today demonstrated the first complex, production-relevant AI workloads on its photonic hardware. Q.ANT successfully demonstrated a diffusion model ...

In this tutorial, we build an end-to-end spatial graph learning pipeline using city2graph. We start by collecting real urban POI data and street network information from OpenStreetMap, with a ...

AI success depends on whether enterprise data is ready, reachable, and close enough to the workloads that need it. In this eSpeaks episode, Dell Technologies’ Vrashank Jain explains why fragmented ...

Abstract: Deep learning has witnessed rapid progress through frameworks such as PyTorch, which has become the dominant choice for researchers and practitioners due to its dynamic computation, ...

ABSTRACT: This paper deals with three traditional measures of concentration: (Corrado) Gini coefficient, adjusted Gini coefficient, and AUC. These metrics are popular methods in assessing the ...

In recent years, advancements in machine learning and electronic stethoscope technology have enabled high-precision recording and analysis of lung sounds, significantly enhancing pulmonary disease ...

Accurate assessment of midpalatal suture (MPS) maturation is critical in orthodontics, particularly for planning treatment strategies in patients with maxillary transverse deficiency (MTD). Although ...

Abstract: Colorizing grayscale photos is a difficult process that has important uses in the creative industries, media improvement, and historical photo restoration. By utilizing advances in neural ...

A production-ready deep learning project for time-series image classification using EfficientNet/NFNet with PyTorch Lightning. This project implements transfer learning for multi-class classification ...