FLIpping the Switch: Boosting stem cell numbers for therapies
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Updates every hour. Last Updated: 12-May-2025 07:09 ET (12-May-2025 11:09 GMT/UTC)
In a recent Engineering article, researchers Jinghai Li and Li Guo discuss the future of data science and its significance for AI. They point out the challenges in scientific data systems, suggest principles for data collection and processing, and stress the importance of data system logic and architecture for the development of AI and data science.
MIT researchers developed a framework that lets a user correct a robot’s behavior during deployment using simple interactions, such as by pointing to an item, tracing a trajectory, or nudging the robot’s arm.
AI models often rely on “spurious correlations,” making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem.
In a new study published in Engineering, researchers from Huazhong University of Science and Technology and the Technical University of Munich have developed an improved proximal policy optimization (IPPO) method. This method is designed to solve the distributed heterogeneous hybrid blocking flow-shop scheduling problem (DHHBFSP), aiming to minimize total tardiness and total energy consumption. The research offers a practical approach for manufacturing scheduling, and its experimental results show better performance compared with other methods.
EPFL researchers have achieved a remarkable result: capturing and studying phase changes in quantum hardware, which hold hold promise for next-generation technologies like quantum computing and ultra-sensitive sensors.