Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; performing feedback-based training on the trained anomaly detection machine learning model; and using the retrained anomaly detection machine learning model.

Abstract: The principal object of embodiments herein is to disclose methods and systems for a secure and user-friendly face liveness detection. Another object of embodiments herein is to disclose a two layered approach for the face liveness detection, wherein the two-layered approach comprises an active and a passive face liveness detection.

Abstract: The present disclosure, in some aspects, is directed to methods for screening and identifying compounds that modulate one or more characteristics associated with a condensate comprising a RBM20 polypeptide and/or the RBM20 polypeptide. In other aspects, the disclosure is directed to systems and composition components thereof, such as cellular models useful for the methods described herein.