Abstract: Neural networks are collaboratively trained with parallel operations by performing operations in a plurality of consecutive time periods including a first plurality of consecutive time periods during which the server receives a set of activations, applies the server partition to a set of activations, applies a set of output instances to a loss function, and computes a set of gradient vectors, and a second plurality of consecutive time periods during which the server transmits a set of gradient vectors.

Abstract: The disclosure relates to devices and methods to quantify traction forces created by neutrophil cells from a blood sample. The disclosure relates to the use of these quantified traction forces to detect and diagnose diseases with altered neutrophil mobility and mechanics. The disclosure describes use of the quantified traction forces in the detection and diagnosis of sepsis.

Abstract: Disclosed are a reinforcement learning model training method and apparatus, a dialogue processing method and a dialogue system, and a computer-readable storage medium. The reinforcement learning model training method includes: acquiring unlabelled data and labelled data which are used for training a reinforcement learning model; on the basis of the unlabelled data, generating, with reference to the labelled data, an experience pool for training the reinforcement learning model; and using the experience pool to train the reinforcement learning model.