Beyond the lakehouse: Fundamental's NEXUS bypasses manual ETL with a native foundation model for tabular data

Emerging from stealth, the company is debuting NEXUS, a Large Tabular Model (LTM) designed to treat business data not as a simple sequence of words, but as a complex web of non-linear relationships.

Fundamental emerges from stealth with first major foundation model trained for tabular data

Emerging from stealth, the company is debuting NEXUS, a Large Tabular Model (LTM) designed to treat business data not as a ...

Simulations and experiments meet: Machine learning predicts gold nanocluster structures

Researchers at University of Jyväskylä (Finland) advance understanding of gold nanocluster behavior at elevated temperatures ...

Are LTMs the next LLMs? This new type of AI can do what large-language models can’t

Fundamental, which just closed a $225 million funding round, develops "large tabular models" (LTMs) for structured data like ...
The Next Platform

Dassault And Nvidia Bring Industrial World Models To Physical AI

During his more than two decades with Nvidia, Rev Lebaredian has had a ringside seat to the show that has been the evolution of modern AI, from the ...
IEEE

Hybrid Models for Photovoltaic Power Forecasting under Different Physical Modeling Approaches

Abstract: Reliable solar Photovoltaic (PV) power forecasting is critical for efficient grid management. This study evaluates hybrid models integrating physical and Machine Learning (ML) techniques for ...
GitHub

Nonlinear Model-Predictive Control Using Koopman Operator

This document provides a detailed explanation of the MATLAB code that demonstrates the application of the Koopman operator theory for controlling a nonlinear system using Model Predictive Control (MPC ...
IEEE

Silicon-Photonics-Empowered Physical Unclonable Functions Using Chaotic Resonances

Abstract: We report optical physical unclonable functions based on a deformed microcavity leveraging silicon photonics technology. The adopted quadrupolar microdisk design with an adjustable ...