Machine learning
Researchers from Technology and Innovation Centre have proposed a new algorithm called Constrained Expectation Maximization (CEM) to address a limitation in teaching motion skills to robots through demonstrations. The CEM algorithm enforces time-sensitive constraints (TSC) during the learning process, enabling improved and more efficient reproduction of demonstration data. This breakthrough
Cardiovascular diseases
Researchers at the University of Genoa in Italy have developed a machine learning tool that can predict survival in peripheral artery disease (PAD) patients who undergo surgical treatment. The study used data from 1,615 patients who underwent surgery between 2005 and 2020 and found that the tool can accurately
Artificial neural networks
Research from Shanghai University proposes a novel framework called Barrier-integrated Adaptive Neural Model Predictive Control (BAN-MPC) that integrates neural networks' fast computation with Model Predictive Control's (MPC) constraint-handling capability. The framework is designed to ensure strict safety in robotics and automation systems while reducing online computational complexity.
Imperial College London
Researchers at Imperial College London have found that machine learning can accurately predict treatment response in patients with complex health conditions, but only when comprehensive clinical phenotyping is present. The study, published in PLOS One, simulated a clinical trial to compare the effectiveness of machine learning with traditional analysis in
Artificial neural networks
Research from Shanghai University proposes a novel framework called Barrier-integrated Adaptive Neural Model Predictive Control (BAN-MPC) that integrates neural networks' fast computation with Model Predictive Control's (MPC) constraint-handling capability. The framework is designed to ensure strict safety in robotics and automation systems while reducing online computational complexity.
Cardiovascular diseases
Researchers at the University of Genoa in Italy have developed a machine learning tool that can predict survival in peripheral artery disease (PAD) patients who undergo surgical treatment. The study used data from 1,615 patients who underwent surgery between 2005 and 2020 and found that the tool can accurately
Machine learning
Researchers from Technology and Innovation Centre have proposed a new algorithm called Constrained Expectation Maximization (CEM) to address a limitation in teaching motion skills to robots through demonstrations. The CEM algorithm enforces time-sensitive constraints (TSC) during the learning process, enabling improved and more efficient reproduction of demonstration data. This breakthrough
Game theory
Scientists at the Georgia Institute of Technology have discovered limitations in differential dynamic games (DDG) that often result in non-unique and infinite Nash Equilibria (NE). The study highlights the need for a novel framework to address the equilibrium coordination problem, which can lead to cost inefficiencies and catastrophic outcomes. Researchers
Artificial intelligence
Researchers at the University of Cordoba have proposed two methodologies using artificial intelligence to predict extreme wind speeds, enhancing the efficiency of wind power in Spain. Wind energy accounted for 23.2% of the Spanish electricity system's energy injection last year, according to Red Electrica's 2024
Machine learning
Terrence Sejnowski, a renowned neuroscientist at the Salk Institute, has been selected to receive a 2025 NIH Director's Pioneer Award. The prestigious award honors scientists proposing exceptionally creative and high-risk, high-reward research. Sejnowski, a professor and head of the Computational Neurobiology Laboratory, will receive $3.5 million over
Artificial intelligence
As the healthcare industry continues to evolve, artificial intelligence (AI) has emerged as a promising tool for improving patient outcomes, streamlining clinical workflows, and optimizing health system performance. However, the development, evaluation, regulation, and implementation of AI in healthcare are shrouded in uncertainty, and its full potential remains to be
Machine learning
Researchers from the Albert Einstein College of Medicine have developed a novel machine learning architecture that integrates structured electronic health records (EHRs) with unstructured free-text inputs to predict perioperative safety indicators for spinal surgery patients. This preoperative multimodal model demonstrated superior performance in predicting extended length of stay, 90-day reoperation,
