Key AI Competences by 2035: A Taxonomy for Firms

Our research examines the transformative changes that AI systems already bring about and are projected to cause in the future. These transformations are often referred to as ‘a fourth industrial revolution’ (Schwab, 2016; cf. Brynjolfsson & McAfee, 2014; Raisch & Krakowski, 2021). For the purposes of this foresight exercise, we assume that AI is likely to be a ‘general-purpose technology’ (Brynjolfsson et al., 2019; cf. Lipsey et al., 2005), similar to technologies such as the steam engine, electrification, and computing. The overall research questions that this project aims to address are: What are the effects of AI on companies by 2035? Does the advent of AI necessitate changes in the organisational design of companies? What are the corresponding key competences that companies need? In this paper, we propose a taxonomy that addresses the last question: what are the key competences for firms on an organisational level to be prepared for AI systems

Statistics of turbulence in the energy-containing range of Taylor-Couette compared to canonical wall-bounded flows

Considering structure functions of the streamwise velocity component in a framework akin to the extended self-similarity hypothesis (ESS), de Silva \textit{et al.} (\textit{J. Fluid Mech.}, vol. 823,2017, pp. 498-510) observed that remarkably the \textit{large-scale} (energy-containing range) statistics in canonical wall bounded flows exhibit universal behaviour. In the present study, we extend this universality, which was seen to encompass also flows at moderate Reynolds number, to Taylor-Couette flow. In doing so, we find that also the transversal structure function of the spanwise velocity component exhibits the same universal behaviour across all flow types considered. We further demonstrate that these observations are consistent with predictions developed based on an attached-eddy hypothesis. These considerations also yield a possible explanation for the efficacy of the ESS framework by showing that it relaxes the self-similarity assumption for the attached eddy contributions. By taking the effect of streamwise alignment into account, the attached eddy model predicts different behaviour for structure functions in the streamwise and in the spanwise directions and that this effect cancels in the ESS-framework --- both consistent with the data. Moreover, it is demonstrated here that also the additive constants, which were previously believed to be flow dependent, are indeed universal at least in turbulent boundary layers and pipe flow where high-Reynolds number data are currently available.Comment: accepted in J. Fluid Mec

VegaProf: Profiling Vega Visualizations

Vega is a popular domain-specific language (DSL) for visualization specification. At runtime, Vega's DSL is first transformed into a dataflow graph and then functions to render visualization primitives. While the Vega abstraction of implementation details simplifies visualization creation, it also makes Vega visualizations challenging to debug and profile without adequate tools. Our formative interviews with three practitioners at Sigma Computing showed that existing developer tools are not suited for visualization profiling as they are disconnected from the semantics of the Vega DSL specification and its resulting dataflow graph. We introduce VegaProf, the first performance profiler for Vega visualizations. VegaProf effectively instruments the Vega library by associating the declarative specification with its compilation and execution. Using interactive visualizations, VegaProf enables visualization engineers to interactively profile visualization performance at three abstraction levels: function, dataflow graph, and visualization specification. Our evaluation through two use cases and feedback from five visualization engineers at Sigma Computing shows that VegaProf makes visualization profiling tractable and actionable.Comment: Submitted to EuroVis'2

Quantum error suppression with subgroup stabilisation projectors

Quantum state purification is the functionality that, given multiple copies of an unknown state, outputs a state with increased purity. This is an essential building block for near- and middle-term quantum ecosystems before the availability of full fault tolerance, where one may want to suppress errors not only in expectation values but also in quantum states. We propose an effective state purification gadget with a moderate quantum overhead by projecting $M$ noisy quantum inputs to their symmetric subspace defined by a set of projectors forming a symmetric subgroup with order $M$. Our method, applied in every short evolution over $M$ redundant copies of noisy states, can suppress both coherent and stochastic errors by a factor of $1/M$. This reduces the circuit implementation cost $M$ times smaller than the state projection to the full symmetric subspace proposed more than two decades ago by Barenco et al. We also show that our gadget purifies the depolarised inputs with probability $p$ to asymptotically $O\left(p^{2}\right)$ with an optimal choice of $M$ when $p$ is small. Our method provides flexible choices of state purification depending on the hardware restrictions before fully fault-tolerant computing is available. Our method may also find its application in designing robust verification protocols for quantum outputs

kMap.py: A Python program for simulation and data analysis in photoemission tomography

For organic molecules adsorbed as well-oriented ultra-thin films on metallic surfaces, angle-resolved photoemission spectroscopy has evolved into a technique called photoemission tomography (PT). By approximating the final state of the photoemitted electron as a free electron, PT uses the angular dependence of the photocurrent, a so-called momentum map or k-map, and interprets it as the Fourier transform of the initial state's molecular orbital, thereby gains insights into the geometric and electronic structure of organic/metal interfaces. In this contribution, we present kMap.py which is a Python program that enables the user, via a PyQt-based graphical user interface, to simulate photoemission momentum maps of molecular orbitals and to perform a one-to-one comparison between simulation and experiment. Based on the plane wave approximation for the final state, simulated momentum maps are computed numerically from a fast Fourier transform of real space molecular orbital distributions, which are used as program input and taken from density functional calculations. The program allows the user to vary a number of simulation parameters such as the final state kinetic energy, the molecular orientation or the polarization state of the incident light field. Moreover, also experimental photoemission data can be loaded into the program enabling a direct visual comparison as well as an automatic optimization procedure to determine structural parameters of the molecules or weights of molecular orbitals contributions. With an increasing number of experimental groups employing photoemission tomography to study adsorbate layers, we expect kMap.py to serve as an ideal analysis software to further extend the applicability of PT