New AI framework can uncover space physics equations in raw data

Artificial intelligence (AI) systems, particularly artificial neural networks, have proved to be highly promising tools for ...
Interesting Engineering

‘World’s first AI-defined’ hybrid EV gets battery boost, charges 80% in 12 mins

Xpeng made its official entry into the range-extended EV market with the debut of its Kunpeng super electric system last ...
Semiconductor Engineering

Algorithm–HW Co-Design Framework for Accelerating Attention in Large-Context Scenarios (Cornell)

Compute-Enabled Memory to Accelerate Large-Context LLMs via Sparse Attention” was published by researchers at Cornell ...
Phys.org

Interpretable deep learning network significantly improves tropical cyclone intensity forecast accuracy

Predicting tropical cyclones (TCs) accurately is crucial for disaster mitigation and public safety. Although the forecasting accuracy of TC tracks has improved substantially in recent decades, ...
Scientific Research Publishing

Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., et al. (2016) Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images. IEEE ...

ABSTRACT: Liver cancer is one of the most prevalent and lethal forms of cancer, making early detection crucial for effective treatment. This paper introduces a novel approach for automated liver tumor ...
Game Rant

Best Loadouts for Terminid Hive World Missions in Helldivers 2

Marc Santos is a Guides Staff Writer from the Philippines. He's a seasoned writer with over four years of industry experience, and he is an enjoyer of all things difficult in gaming. If Marc's not ...
IEEE

Online Public Opinion Situation Assessment by Deep Sparse Autoencoder and Time-Series Modeling Convolutional Neural Network

Abstract: We have developed an innovative framework for analyzing online public sentiment, referred to as the Online Public Opinion Situation Assessment (OPOSA) model. This model employs Deep Sparse ...
Frontiers

BANSMDA: a computational model for predicting potential microbe-disease associations based on bilinear attention networks and sparse autoencoders

Introduction: Predicting the relationship between diseases and microbes can significantly enhance disease diagnosis and treatment, while providing crucial scientific support for public health, ...