Three-dimensional (3D) topological Dirac semimetals (TDSs) are a recently proposed state of quantum matter that have attracted increasing attention in physics and materials science. A 3D TDS is not only a bulk analogue of graphene; it also exhibits non-trivial topology in its electronic structure that shares similarities with topological insulators. Moreover, a TDS can potentially be driven into other exotic phases (such as Weyl semimetals, axion insulators and topological superconductors), making it a unique parent compound for the study of these states and the phase transitions between them. Here, by performing angle-resolved photoemission spectroscopy, we directly observe a pair of 3D Dirac fermions in Cd3As2, proving that it is a model 3D TDS. Compared with other 3D TDSs, for example, β-cristobalite BiO2 (ref. 3) and Na3Bi (refs 4, 5), Cd3As2 is stable and has much higher Fermi velocities. Furthermore, by in situ doping we have been able to tune its Fermi energy, making it a flexible platform for exploring exotic physical phenomena.

. This discovery not only confirms TaAs as a 3D TWS, but also provides an ideal platform for realizing exotic physical phenomena (for example, negative magnetoresistance, chiral magnetic e ects and the quantum anomalous Hall e ect) which may also lead to novel future applications.

Deep learning has huge potential for accurate disease prediction with neuroimaging data, but the prediction performance is often limited by training-dataset size and computing memory requirements. To address this, we propose a deep convolutional neural network model, Simple Fully Convolutional Network (SFCN), for accurate prediction of brain age using T1-weighted structural MRI data. Compared with other popular deep network architectures, SFCN has fewer parameters, so is more compatible with small dataset size and 3D volume data. The network architecture was combined with several techniques for boosting performance, including data augmentation, pre-training, model regularization, model ensemble and prediction bias correction. We compared our overall SFCN approach with several widely-used machine learning models. It achieved state-of-the-art performance in UK Biobank data (N = 14,503), with mean absolute error (MAE) = 2.14y in brain age prediction and 99.5% in sex classification. SFCN also won (both parts of) the 2019 Predictive Analysis Challenge for brain age prediction, involving 79 competing teams (N = 2,638, MAE = 2.90y). We describe here the details of our approach, and its optimisation and validation. Our approach can easily be generalised to other tasks using different image modalities, and is released on GitHub.