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The topology of the Fermi surface significantly influences the transport properties of a material. Firstly measured through quantum oscillation experiments, the Fermi surfaces of crystals are now commonly characterized using angle-resolved photoemission spectroscopy (ARPES), given the larger information volume it provides. In the case of Weyl semimetals, ARPES has proven remarkably successful in verifying the existence of the Weyl points and the Fermi arcs, which define a Weyl Fermi surface. However, ARPES is limited in resolution, leading to significant uncertainty when measuring relevant features such as the distance between the Weyl points. While quantum oscillation measurements offer higher resolution, they do not reveal insights into the cross-sectional shape of a Fermi surface. Moreover, both techniques lack critical information about transport, like the carriers mean free path. Here, we report measurements unveiling the distinctive peanut-shaped cross-section of the Fermi surface of Weyl fermions and accurately determine the separation between Weyl points in the Weyl semimetal NbP. To surpass the resolution of ARPES, we combine quantum oscillation measurements with transverse electron focusing (TEF) experiments, conducted on microstructured single-crystals. The TEF spectrum relates to the Fermi surface shape, while the frequency of the quantum oscillations to its area. Together, these techniques offer complementary information, enabling the reconstruction of the distinctive Weyl Fermi surface geometry. Concurrently, we extract the electrical transport properties of the bulk Weyl fermions. Our work showcases the integration of quantum oscillations and transverse electron focusing in a singular experiment, allowing for the measurements of complex Fermi surface geometries in high-mobility quantum materials.

In recent years an increasing number of researchers and practitioners have been suggesting algorithms for large-scale neural network architecture search: genetic algorithms, reinforcement learning, learning curve extrapolation, and accuracy predictors. None of them, however, demonstrated high-performance without training new experiments in the presence of unseen datasets. We propose a new deep neural network accuracy predictor, that estimates in fractions of a second classification performance for unseen input datasets, without training. In contrast to previously proposed approaches, our prediction is not only calibrated on the topological network information, but also on the characterization of the dataset-difficulty which allows us to re-tune the prediction without any training. Our predictor achieves a performance which exceeds 100 networks per second on a single GPU, thus creating the opportunity to perform large-scale architecture search within a few minutes. We present results of two searches performed in 400 seconds on a single GPU. Our best discovered networks reach 93.67% accuracy for CIFAR-10 and 81.01% for CIFAR-100, verified by training. These networks are performance competitive with other automatically discovered state-of-the-art networks however we only needed a small fraction of the time to solution and computational resources.

We present a modular framework, the Workload Characterisation Framework (WCF), that is developed to reproducibly obtain, store and compare key characteristics of radio astronomy processing software. As a demonstration, we discuss the experiences using the framework to characterise a LOFAR calibration and imaging pipeline., Comment: 4 pages, 4 figures; to be published in ADASS XXVI (held October 16-20, 2016) proceedings. See http://www.adass2016.inaf.it/images/posters/grange.pdf for the poster