From LLMs to hallucinations, here’s a simple guide to common AI terms

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most ...
Tech Xplore

Compression technique makes AI models leaner and faster while they're still learning

Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational ...
RMD Open

Emotional well-being at diagnosis may indicate future mental health risk in patients with juvenile idiopathic arthritis: data from the German inception cohort ICON

Objectives To determine the prevalence of depressive and anxiety symptoms among young people 7 years and 9 years after ...
Scientific Research Publishing

Predicting Stock Price Movements Using Deep Learning Techniques ()

Stock Price Prediction, Deep Learning, LSTM, GRU, Attention Mechanism, Financial Time Series Share and Cite: Kirui, D. (2026) ...

Linear City: When Transport Shapes Urban Structure

The transition to linear cities represents a shift in paradigm: from more chaotic urban expansion to guided growth along the ...
Hosted on MSN

Linear Programming problem learn how to solve

Learn how to solve problems using linear programming. A linear programming problem involves finding the maximum or minimum value of an equation, called the objective functions, subject to a system of ...
Supply Chain

The End of Linear PLM, and How 2025 Broke the Model

Analyst Insight: As 2025 comes to an end, one reality has become clear: The traditional linear product lifecycle management model has reached its limits. For years, PLM served retailers well by ...
Wired

AI Models Are Starting to Learn by Asking Themselves Questions

An AI model that learns without human input—by posing interesting queries for itself—might point the way to superintelligence. Save this story Save this story Even the smartest artificial intelligence ...
eLife

A network regularized linear model to infer spatial expression pattern for single cell

The development of glmSMA represents a valuable advancement in spatial transcriptomics analysis, offering a mathematically robust regression-based approach that achieves higher-resolution mapping of ...
jnis.bmj

Statistical principles in neurointervention part 2. Multivariable analysis: generalized linear models, modification, confounding, and mediation

Neurointervention is a highly specialized area of medicine and, as such, neurointerventional research studies are often more challenging to conduct, require large, multicenter efforts and longer study ...