Abstract Graph convolutional networks (GCNs) have become the de facto approaches and achieved state-of-the-art results for circumventing many real-world problems on graph-structured data. However, these are usually shallow due to over-smoothing of GCNs with layers, which limits expressive power learning graph representations. The current methods solving limitations bottlenecks high complexity p...

the purpose of this paper is to study and give the necessary andsufficient condition of strong convergence of the multi-step iterative algorithmwith errors for a finite family of generalized asymptotically quasi-nonexpansivemappings to converge to common fixed points in banach spaces. our resultsextend and improve some recent results in the literature (see, e.g. [2, 3, 5, 6, 7, 8,11, 14, 19]).

The generation and the properties of the multi-step point predictors of the Self Exciting Threshold AutoRegressive Moving Average model has been examined. In the first part we focus the attention on the main properties of this class of models and on their different representations that can be derived extending, to the non linear case, some well know results given in Box and Jenkins (1976). Star...

This paper presents multi-step capacitor switching methods for SAR ADCs based on precharge with floating capacitors and charge sharing. The proposed switching methods further reduce the transient power of the split monotonic switching method (an improved version of the monotonic switching method). Compared to the split monotonic switching, adding charge sharing achieves around 50% reduction in ...