Volume 3

  • No. 5 May 2021

    Neural architecture search for computational genomics

    Applying deep learning models requires the tuning of network architectures for optimum performance, which can require substantial machine learning expertise. In this issue, Zijun Zhang et al. present a fully automated framework, AMBER, to design and apply convolutional neural networks for genomic sequences using neural architecture search. In an accompanying News & Views, Yi Zhang, Yang Liu and X. Shirley Liu discuss the AMBER technique and its potential to improve deep learning models in genomics.

    See Zhang et al. and Zhang, Liu and Liu

  • No. 4 April 2021

    Expanding dimensions

    In the field of computational materials design, 3D microstructural datasets are crucial for understanding structure–performance relationships through physical modelling. However, 3D imaging can be slow and often has limited resolution compared to its 2D counterparts. In this issue, Steve Kench and Samuel Cooper propose a generative adversarial architecture, SliceGAN, which can use a single representative cross-sectional image to synthesize realistic 3D volumes. In an accompanying News & Views, Alejandro Franco discusses the technique and the potential to extend it to even further dimensional expansion.

    See Kench and Cooper, and Franco

  • No. 3 March 2021

    Deep learning for nanocrystal tomography

    The 3D elemental structure and composition of nanocrystals can be analysed by combining scanning transmission electron microscopy (STEM) and energy-dispersive X-ray spectroscopy (EDX). This is useful, for instance, for the study and design of semiconductor quantum dots for optoelectronic applications in display devices. However, EDX has low efficiency and leads to electron beam-induced damage to the nanocrystals. In this issue, Han et al. demonstrate an unsupervised deep learning method that can help to reconstruct elemental 3D maps under reduced beam exposure. With this approach, valuable information can be learned about the dependence of optical properties on the structure and elemental composition of quantum dots.

    See Han et al.

  • No. 2 February 2021

    Tackling the torrent of scientific literature with active learning

    Systematic reviews, which provide a comprehensive overview of the literature in a specific research area, are important tools for scholars, policymakers and clinicians, among others. However, producing them typically involves screening thousands (or tens of thousands) of papers, which is time-consuming and error-prone when done manually. This is frustrating especially when good overviews are urgently needed, for instance in the case of COVID-19 research. In recent years, it has become possible to speed up literature screening with machine learning approaches, and collaborative workflows have been developed where machine learning algorithms are optimized to find the most relevant records by human-in-the-loop approaches. In this issue van de Schoot et al. demonstrate an open source framework for machine learning-supported systematic reviewing of the literature called ASReview, integrating several machine learning classifiers.

    See van de Schoot et al.

  • No. 1 January 2021

    Learning visual appearance for flight control

    Flying insects show impressive skills in navigation and piloting, including landing and avoiding obstacles, which roboticists try to mimic in the design of lightweight flying robots. The visual cue of optical flow is known to play a major role in insect navigation and is increasingly studied for use by small flying robots as well. However, there are gaps in the current understanding of optical flow control, as it cannot disentangle distance from velocity, and is less informative in the forward flight direction. In this issue, De Croon et al. propose a solution that consists of a learning process in which the robot first uses optical flow and self-induced oscillations to perceive distances to objects in its environment. It then learns a mapping from visual appearance to these distances to complement optical flow, solving the above-mentioned problems. The approach, which is biologically plausible in terms of processing, sensing, and actuation requirements, is demonstrated on a flying robot.

    See De Croon et al.