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This arXiv paper presents a shape-constrained predictive modeling approach to estimate nanoparticle size and dispersity in continuous flow nanodrug production, reducing the need for extensive experimental screening.
This paper compares five feature selection methods for EHR diagnosis codes in opioid use disorder prediction, finding that NTK sensitivity offers the best accuracy-stability balance while LLM-guided selection adds complementary clinical signals.
Google DeepMind describes how they used their 'Predicting the Past' AI skill to analyze ancient texts and uncover historical mysteries, including tracking a Roman ring thief and mapping networks of visitors to a Greek oracle.
The MS AI Frontiers team introduces BenchPress, a method that uses matrix completion to predict LLM benchmark scores from just five probes, showing the score matrix is effectively rank-2.
This study presents a hybrid predictive framework using CatBoost and SHAP to identify risk factors in tree-involved traffic crashes, highlighting restraint non-use as the most critical predictor of severe injury.
TRIBE v2 is a new predictive foundation model designed to understand how the human brain processes complex stimuli.