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A multi-component polyoxometalate based on earth-abundant elements (NH4)10[Co8(H2O)10V10Mo23O104(OH)6]·34.5H2O () has been successfully obtained and characterized. Furthermore, compound acted as a Lewis acid catalyst and promoted the conversion of carbon dioxide to a cyclic carbonate under mild reaction conditions.

The traditional approach to distributed deep neural network (DNN) training is data-distributed learning, which partitions and distributes data to workers. This approach, although has good convergence properties, has high communication cost, which puts a strain especially on edge systems and increases delay. An emerging approach is model-distributed learning, where a training model is distributed across workers. Model-distributed learning is a promising approach to reduce communication and storage costs, which is crucial for edge systems. In this paper, we design ResPipe, a novel resilient model-distributed DNN training mechanism against delayed/failed workers. We analyze the communication cost of ResPipe and demonstrate the trade-off between resiliency and communication cost. We implement ResPipe in a real testbed consisting of Android-based smartphones, and show that it improves the convergence rate and accuracy of training for convolutional neural networks (CNNs).