Sepsis Prediction Using Graph Convolutional Networks over Patient-Feature-Value Triplets

President Trump has announced a significant policy shift allowing Nvidia to sell its advanced H200 AI chips to approved customers in China, effectively lifting a previous export ban imposed due to national security concerns. This decision includes a 25% surcharge on sales, which experts have criticized as illogical given the context of the deal.

The Bay Area is poised to play a crucial role in the Genesis Mission, a multibillion-dollar initiative launched by the Trump administration aimed at enhancing the United States' artificial intelligence capabilities amid growing competition from China. This mission will leverage federal scientific datasets to train AI models and develop AI agents, marking a significant government effort to boost innovation in this critical sector.

US startups, including Oakland-based Brimstone, are intensifying efforts to develop domestic sources of critical raw materials essential for technology, an area historically dominated by China. This initiative is part of a broader strategy to enhance supply chain resilience and reduce dependence on foreign sources.

DeepSeek, a Chinese AI startup, is reportedly developing its next AI model using several thousand Nvidia Blackwell chips that were smuggled into China through third-party countries. This development raises concerns regarding compliance with international trade regulations and the implications of using restricted technology.

The former chief executive of Nexperia, a Dutch semiconductor company, revealed that Dutch officials were aware for years of the company's Chinese owner’s intentions to transfer technology to China. This revelation follows the Dutch government's seizure of Nexperia in September, amid rising concerns over technology security.

A new study introduces a Long Short-Term Memory (LSTM) model designed to predict NBA game outcomes using a comprehensive dataset spanning from the 2004-05 to 2024-25 seasons. This model utilizes an extensive sequence of 9,840 games to effectively capture evolving team dynamics and dependencies across seasons, addressing challenges faced by traditional prediction models.

RaX-Crash has been developed as a resource-efficient and explainable small model pipeline aimed at predicting injury severity from motor vehicle collisions in New York City, utilizing a dataset with over one hundred thousand records. The model employs compact tree-based ensembles, specifically Random Forest and XGBoost, achieving notable accuracy compared to small language models.

A new ensemble-based machine learning model has been developed to enhance early diabetes prediction using the BRFSS dataset, which includes over 253,000 health records. The model employs techniques like SMOTE and Tomek Links to address class imbalance and achieves a strong ROC-AUC score of approximately 0.96 through various algorithms, including Random Forest and XGBoost.