Letting atomic simulations learn from phase diagrams

A new computational method allows modern atomic models to learn from experimental thermodynamic data, according to a ...
IEEE

An Efficient Hybrid Model Combining LSTM and Kalman Filter for Real-Time Traffic Flow Prediction in Smart Transportation Systems

Abstract: Forecasting traffic flow is essential for contemporary Intelligent Transportation Systems (ITS), but accurately modeling this dynamic process is inherently challenging. This study proposed a ...
American Institute of Physics

Peering inside the machine learning black box for fusion experiments

Statistical insights into machine learning analysis can help researchers evaluate model performance and may even provide new physical understanding.

Microsoft's new AI training method eliminates bloated system prompts without sacrificing model performance

Microsoft researchers have developed On-Policy Context Distillation (OPCD), a training method that permanently embeds ...

Funding Is Not the End of the Line: What Comes Next

Funding Is Not the End of the Line because signing a deal simply pushes a business into a new phase that demands clear ...

The ABC Challenge All Enterprises Face: New Book on AI, Blockchain, and Cybersecurity for Finance

NEW YORK CITY, NY / ACCESS Newswire / February 20, 2026 / In a new book, ABC: AI, Blockchain, and Cybersecurity for ...
IEEE

Topological-Temporal Convolution Transformer Hawkes Process for Causal Structural Learning in Telecom Networks

Abstract: Accurate causal discovery in telecommunication alarm event sequences is crucial for reliable root cause analysis, but presents significant challenges due to complex topological dependencies ...
Scientific Research Publishing

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ()

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...