Abstract: According to an aspect, a computer-implemented method includes intercepting a call to invoke execution of a service as part of performing a synchronous transaction. A current state of the synchronous transaction is captured and persisted in a transaction object corresponding to the synchronous transaction. Execution of the service is invoked.

Abstract: A semiconductor device includes a nitride-based transistor, a first metal layer, a second metal layer, a third metal layer, a source pad, and a drain pad. The first metal layer is disposed over the nitride-based transistor. The second metal layer is disposed over the first metal layer. The third metal layer is disposed over the second metal layer and includes a first pattern and a second pattern which are spaced apart from each other. The source pad is immediately above the first metal layer, the second metal layer, and the first pattern of the third metal layer and is electrically coupled with the nitride-based transistor. The drain pad is immediately above the first metal layer, the second metal layer, and the second pattern of the third metal layer and is electrically coupled with the nitride-based transistor.

Abstract: A method and system of training an interpretable deep learning model includes receiving an input set of data, which may be complex. The input set of data is provided to deep learning model for feature extraction. In an exemplary embodiment, the deep learning model generates a disentangled latent space of features from the feature extraction. The features may comprise semantically meaningful data which is then provided to a low-complexity learning model. The low-complexity learning model generates output based on a specified task (for example, classification or regression). Being a low-complexity learning model provides confidence that the data output from the deep learning model is inherently interpretable.