The accurate estimation of predictive uncertainty carries importance in medical scenarios such as lung node segmentation. Unfortunately, most existing works on predictive uncertainty do not return calibrated uncertainty estimates, which could be used in practice. In this work we exploit multi-grader annotation variability as a source of 'groundtruth' aleatoric uncertainty, which can be treated as a target in a supervised learning problem. We combine this groundtruth uncertainty with a Probabilistic U-Net and test on the LIDC-IDRI lung nodule CT dataset and MICCAI2012 prostate MRI dataset. We find that we are able to improve predictive uncertainty estimates. We also find that we can improve sample accuracy and sample diversity.

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Designing atomically dispersed metal catalysts for the electrocatalytic hydrogen evolution reaction (HER) is a promising strategy to efficiently convert electrical energy to chemical fuels. This strategy, however, still faces the challenge of finding a catalyst with high activity and long-term durability. Here, single-atom platinum and cobalt-platinum-alloy nanocrystals in a nitrogen-doped porous-carbon framework (CoPt-Pt SA /NDPCF) is confined. The electrocatalyst exhibits ultralow overpotentials under both alkaline and acidic conditions at a high current density of −300 mA cm −2 and excellent long-term durability up to 100 h or 10 000 cycles, which are attributed to the synergetic effects of Pt SA and tuning of the CoPt alloy in the NDPCF. Firstprinciples calculations suggested that Pt SA aided by the CoPt alloy has high d-band occupation for promoting the reaction kinetics. This study opens a new avenue for designing heterostructures with the synergic effects of single metal atoms and metal alloys with outstanding performance in the HER.

Abstract. As hardware capabilities increase, low-power devices such as smartphones represent a natural environment for the efficient implementation of cryptographic pairings. Few works in the literature have considered such platforms despite their growing importance in a post-PC world. In this paper, we investigate the efficient computation of the Optimal-Ate pairing over Barreto-Naehrig curves in software at different security levels on ARM processors. We exploit state-of-the-art techniques and propose new optimizations to speed up the computation in the tower field and curve arithmetic. In particular, we extend the concept of lazy reduction to inversion in extension fields, analyze an efficient alternative for the sparse multiplication used inside the Miller's algorithm and reduce further the cost of point/line evaluation formulas in affine and projective homogeneous coordinates. In addition, we study the efficiency of using M-type sextic twists in the pairing computation and carry out a detailed comparison between affine and projective coordinate systems. Our implementations on various mass-market smartphones and tablets significantly improve the state-of-the-art of pairing computation on ARM-powered devices, outperforming by at least a factor of 3.7 the best previous results in the literature.

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