Loss functions and constraints improve sea surface height prediction

In order to understand currents, tides and other ocean dynamics, scientists need to accurately capture sea surface height, or ...
IEEE

Fusion Graph Structure Learning-Based Multivariate Time Series Anomaly Detection With Structured Prior Knowledge

Abstract: Multivariate time series anomaly detection (MTSAD) plays a crucial role in the Internet of Things (IoT) to identify device malfunction or system attacks. Graph neural networks (GNN) are ...
NextBigFuture

XAI Grok 4.20 and OpenAI GPT 5.2 Are Solving Significant Previously Unsolved Math Proofs

A Mathematician with early access to XAI Grok 4.20, found a new Bellman function for one of the problems he had been working ...
University of Geneva

David Cimasoni

Gaetan Simian: PhD student (co-direction with Anthony Conway ), started February 2023. Miguel Orbegozo Rodriguez: postdoc, started September 2025. Livio Ferretti: postdoc, September 2023-February 2025 ...
Quanta Magazine

Using AI, Mathematicians Find Hidden Glitches in Fluid Equations

A $1 million prize awaits anyone who can show where the math of fluid flow breaks down. With specially trained AI systems, ...
IEEE

Imagery Speech Classification Based on General Successive Multivariate Variational Mode Decomposition With Different Objective Functions Having Different Weights

Abstract: The imagery speech (IS) is the speech that the human beings are thinking in their brain. A brain computer interface (BCI) system is employed to translate the speech thinking in the brain to ...
Frontiers

A radiomic model leveraging conventional and Hessian matrix-based radiomic features from DCE-MRI for predicting efficacy of neoadjuvant chemotherapy in patients with HER2-low ...

Purpose: This study aimed to develop a predictive model for assessing the efficacy of neoadjuvant chemotherapy (NAC) in patients with Human Epidermal Growth Factor Receptor 2 (HER2)-low breast cancer, ...
GitHub

Mathematics of Machine Learning

Mathematics of Machine Learning provides a rigorous yet accessible introduction to the mathematical underpinnings of machine learning, designed for engineers, developers, and data scientists ready to ...
GitHub

NVIDIA Context Aware RAG

Context Aware RAG is a flexible library designed to seamlessly integrate into existing data processing workflows to build customized data ingestion and retrieval (RAG) pipelines. With Context Aware ...