Humans use diverse skills and strategies to effectively manipulate various objects, ranging from dexterous in-hand manipulation (fine motor skills) to complex whole-body manipulation (gross motor skills). The latter involves full-body engagement and ...
Neuromorphic computing has got more attention in various tasks during recent years. The main goal of this field is to explore neural functionality in the brain. The studies of spiking neurons and Spiking Neural Networks (SNNs) are vital to understand...
The integration of AI simulation models within smart electrical prosthetic systems represents a significant advancement in disability disease diagnosis. However, the selection and evaluation of these AI models interpret some multi-criteria decision-m...
The amygdala plays a central role in emotion, memory, and decision-making and comprises approximately 13 distinct nuclei with connectivity. Despite its functional importance, high-resolution subnuclear mapping is challenging. This study aimed to cons...
Conventional approaches to material decomposition in spectral CT face challenges related to precise algorithm calibration across imaged conditions and low signal quality caused by variable object size and reduced dose. In this proof-of-principle stud...
We present MBPert, a generic computational framework for inferring species interactions and predicting dynamics in time-evolving ecosystems from perturbation and time-series data. In this work, we contextualize the framework in microbial ecosystem mo...
Many biological decision-making tasks require classifying high-dimensional chemical states. The biophysical and computational mechanisms that enable classification remain enigmatic. In this work, using Markov jump processes as an abstraction of gener...
To enhance the motion flexibility and environmental adaptability of underwater robots, this study proposes a novel design, Seeker-M, inspired by the locomotion mechanism of the mantis shrimp. The robot imitates the mantis shrimp's multi-pleopod swimm...
Proceedings of the National Academy of Sciences of the United States of America
Jul 31, 2025
Computational models of reinforcement learning (RL) have significantly contributed to our understanding of human behavior and decision-making. Traditional RL models, however, often adopt a linear approach to updating reward expectations, potentially ...
Ibrutinib and acalabrutinib are first- and next-generation Bruton Tyrosine Kinase inhibitors (BTKi), respectively, approved for chronic lymphocytic leukemia (CLL). Ibrutinib has been associated with cardiovascular events, including atrial fibrillatio...
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