Minghao Chi Strengthens Pension Fund Risk Management with Data-Driven Index Weight Prediction Research

A predictive modeling framework integrating machine learning with real-time trading strategies generates over $500,000 documented profitability ...
Opinion

With Consumer Data Models, Who Needs Government Data?

In a data-driven world, pauses in government economic data do more than inconvenience economists, they create dangerous blind spots for investors and business leaders.
eLife

Unbend: Correction of local beam-induced sample motion in cryo-EM images using a 3D spline model

This paper describes Unbend - a new method for measuring and correcting motions in cryo-EM images, with a particular emphasis on more challenging in situ samples such as lamella and whole cells. The ...
Tech Xplore

Self-evolving edge AI enables real-time learning and forecasting in small devices

Researchers from The University of Osaka's Institute of Scientific and Industrial Research (SANKEN) have successfully ...
Nature

This ‘impressive’ AI model predicted Hurricane Melissa’s perilous growth

Compared with other hurricane models used by the NHC, the DeepMind model is “definitely toward the front of the pack if not ...
MIT Technology Review

DeepSeek may have found a new way to improve AI’s ability to remember

The technique can also be used to produce more training data for AI models. Model developers are currently grappling with a ...
Frontiers

Advancing Computational Material Science and Mechanics through Integrated Deep Learning Models

The field of computational materials science has been profoundly transformed by integrating deep learning and other machine learning methodologies. These sophisticated data-driven approaches have ...
IEEE

Automated Epilepsy Detection System Based On Tertiary Wavelet Model(TWM) Techniques

Abstract: A computerized method for classifying epilepsy from electroencephalogram (EEG) data using a Tertiary Wavelet Model (TWM) as a feature extractor is detailed. Before extracting features such ...
IEEE

Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling

Abstract: This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus ...