Researchers from MBZUAI introduce Forget-MI, a machine unlearning method tailored for multimodal medical data, enhancing privacy by removing specific patient data from AI models. Forget-MI utilizes loss functions and perturbation techniques to unlearn both unimodal and joint data representations. The method demonstrates superior performance in reducing Membership Inference Attacks and improving data removal compared to existing techniques, while preserving overall model performance and enabling data forgetting.
This paper introduces MOTOR, a multimodal retrieval and re-ranking approach for medical visual question answering (MedVQA) that uses grounded captions and optimal transport to capture relationships between queries and retrieved context, leveraging both textual and visual information. MOTOR identifies clinically relevant contexts to augment VLM input, achieving higher accuracy on MedVQA datasets. Empirical analysis shows MOTOR outperforms state-of-the-art methods by an average of 6.45%.
Researchers at KAUST and KACST have developed a composite material that enhances solar cell performance by absorbing air moisture at night and releasing it during the day. When applied to solar cells in Saudi Arabia, the material increased power output by 12.9% and extended cell lifespan by over 200%. The passive cooling technology also reduced electricity generation costs by 18%. Why it matters: This innovation addresses a key challenge in solar energy adoption in hot climates, potentially making solar power more efficient and cost-effective in the region.