Abstract. Velocity measurements, previously carried out using both a miniature current flowmeter and an acoustic Doppler velocimeter, are used to verify the applicability of the incomplete self-similarity theory to deduce the velocity profile in a gravel bed channel. Then, for the velocity profiles having the maximum value below the free surface and for the S-shaped profiles, the power velocity distribution is corrected using a new divergence function. For each value of the depth sediment ratio the nondimensional friction factor parameter is calculated by integration of the measured velocity distributions in the different verticals of the cross section. Finally, a semilogarithmic flow resistance equation is empirically deduced.

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Since the Transformer architecture was introduced in 2017 there has been many attempts to bring the selfattention paradigm in the field of computer vision. In this paper we propose a novel self-attention module that can be easily integrated in virtually every convolutional neural network and that is specifically designed for computer vision, the LHC: Local (multi) Head Channel (self-attention). LHC is based on two main ideas: first, we think that in computer vision the best way to leverage the self-attention paradigm is the channel-wise application instead of the more explored spatial attention and that convolution will not be replaced by attention modules like recurrent networks were in NLP; second, a local approach has the potential to better overcome the limitations of convolution than global attention. With LHC-Net we managed to achieve a new state of the art in the famous FER2013 dataset with a significantly lower complexity and impact on the "host" architecture in terms of computational cost when compared with the previous SOTA.