Abstract: A data processing system for training a neural network, the data processing system comprising: a first set of one or more processing units running one model of the neural network, a second set of one or more processing units running another model of the neural network, a data storage, and an interconnect between the first set of one or more processing units, the second set of processing units and the data storage, wherein the data storage is configured to provide over the interconnect, training data to the first set of one or more processing units and the second set of one more processing units, wherein each of the first and second set of processing units is configured to, when performing the training, evaluate loss for the respective training iteration including a measure of the dissimilarity between the output values calculated based on the different modes running on the first and second set of processing units, wherein the dissimilarity measure is weighted in the evaluation of the loss in accordance with a

Abstract: A data processing system for training a neural network, the data processing system comprising: a first set of one or more processing units running one model of the neural network, a second set of one or more processing units running another model of the neural network, a data storage, and an interconnect between the first set of one or more processing units, the second set of processing units and the data storage, wherein the data storage is configured to provide over the interconnect, training data to the first set of one or more processing units and the second set of one more processing units, wherein each of the first and second set of processing units is configured to, when performing the training, evaluate loss for the respective training iteration including a measure of the dissimilarity between the output values calculated based on the different modes running on the first and second set of processing units, wherein the dissimilarity measure is weighted in the evaluation of the loss in accordance with a

Abstract: A data processing system for training a neural network, the data processing system comprising: a first set of one or more processing units, a second set of one or more processing units, a data storage, and an interconnect between the first set of one or more processing units, the second set of processing units and the data storage, wherein the data storage is configured to provide over the interconnect, training data to the first set of one or more processing units and the second set of one more processing units, wherein each of the first and second set of processing units is configured to, when performing the training, evaluate loss for the respective training iteration including a metric measuring the dissimilarity between the output values calculated by the first and second set of processing units, wherein the metric is weighted in the evaluation of the loss in accordance with a parameter that is updated between different training iterations.

Abstract: In order to target a diseased material in a subject, a monocyte, or monocyte-derived cell, such as a macrophage, which incorporates a magnetic material, such as a magnetic particle or a ferrofluid, preferably having a biocompatible coating, is proposed to be administered. A magnetic energy source may then be applied to the subject to destroy, rupture or inactivate the diseased material. Alternatively, the monocyte or monocyte derived cell may additionally include a therapeutic agent, which is thereby targeted at the diseased material.