Enterprises face key challenges in harnessing unstructured data so they can make the most of their investments in AI, but several vendors are addressing these challenges.

Abstract: Model-Driven Engineering (MDE) aims to improve the development of software systems by utilizing models as primary source artifacts and leveraging automation, such as model-to-code ...

In large public multi-site fMRI datasets, the sample characteristics, data acquisition methods, and MRI scanner models vary across sites and datasets. This non-neural variability obscures neural ...

Abstract: Model-based reinforcement learning (RL), which learns an environment model from the offline dataset and generates more out-of-distribution model data, has become an effective approach to the ...

-- Bruton’s tyrosine kinase (BTK) SH2 inhibitor reduced skin inflammation in a clinically-relevant model of chronic spontaneous urticaria (CSU) --BTK inhibition through the SH2 domain results in ...

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Generating time series data is important for many applications, including data augmentation, synthetic datasets, and scenarios. However, when there is more than one, this process becomes too complex ...

Introduction: Unsupervised domain adaptation (UDA) aims to adapt a model learned from the source domain to the target domain. Thus, the model can obtain transferable knowledge even in target domain ...