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Citizen conceptions of democracy and support for artificial intelligence in government and politics
(2022)
How much do citizens support artificial intelligence (AI) in government and politics at different levels of decision-making authority and to what extent is this AI support associated with citizens’ conceptions of democracy? Using original survey data from Germany, the analysis shows that people are overall sceptical toward using AI in the political realm. The findings suggest that how much citizens endorse democracy as liberal democracy as opposed to several of its disfigurations matters for AI support, but only in high-level politics. While a stronger commitment to liberal democracy is linked to lower support for AI, the findings contradict the idea that a technocratic notion of democracy lies behind greater acceptance of political AI uses. Acceptance is higher only among those holding reductionist conceptions of democracy which embody the idea that whatever works to accommodate people's views and preferences is fine. Populists, in turn, appear to be against AI in political decision making.
CFD-DEM Simulation of Superquadric Cylindrical Particles in a Spouted Bed and a Rotor Granulator
(2023)
The fluidization behavior of cylindrical particles in a spouted bed was first investigated experimentally using a camera setup. The obtained average spouted bed height was used to evaluate the accuracy of different drag models in CFD-DEM simulations with the superquadric approach to model the particle shape. The drag model according to Sanjeevi et al. showed the best agreement. With this model, cylindrical particles were simulated in a rotor granulator and the particle dynamics were compared with the fluidization of volume equivalent spherical particles.
Drawing on theorising on digital technologies as external enablers of entrepreneurial activities and an interactionist perspective on corporate entrepreneurship, this article examines the relationship between digital technology support and employee intrapreneurial behaviour. We propose that management support for innovation as an organisational characteristic and intrapreneurial self-efficacy as an individual characteristic moderate this relationship. Findings from a metric conjoint experiment with 1360 decisions nested within 85 employees showed that support by social media, support by collaborative technologies, and support by intelligent decision support systems were significant predictors of employee intrapreneurial behaviour. However, the relative impact of support by these digital technologies varied with different levels of management support for innovation and intrapreneurial self-efficacy.
Edit distances between merge trees of scalar fields have many applications in scientific visualization, such as ensemble analysis, feature tracking or symmetry detection. In this paper, we propose branch mappings, a novel approach to the construction of edit mappings for merge trees. Classic edit mappings match nodes or edges of two trees onto each other, and therefore have to either rely on branch decompositions of both trees or have to use auxiliary node properties to determine a matching. In contrast, branch mappings employ branch properties instead of node similarity information, and are independent of predetermined branch decompositions. Especially for topological features, which are typically based on branch properties, this allows a more intuitive distance measure which is also less susceptible to instabilities from small-scale perturbations. For trees with 𝒪(n) nodes, we describe an 𝒪(n4) algorithm for computing optimal branch mappings, which is faster than the only other branch decomposition-independent method in the literature by more than a linear factor. Furthermore, we compare the results of our method on synthetic and real-world examples to demonstrate its practicality and utility.
The direct regioselective C−H-functionalization of simple, unfunctionalized pyridines is considered a long-standing challenge in heterocyclic chemistry. Herein, we report a novel one-pot protocol for the C4-selective sulfonylation of pyridines via triflic anhydride (Tf2O) activation, base-mediated addition of a sulfinic acid salt, and subsequent elimination/re-aromatization. Contrary to previous approaches employing tailored blocking groups, positional selectivity can be controlled by using N-methylpiperidine as simple, readily available external base. This method offers a highly modular and streamlined access to C4-sulfonylated pyridines.
Janus-Materialien sind anisotrope Nano- und Mikroarchitekturen, die zwei verschiedene Seiten mit unterschiedlichen oder entgegengesetzten physikochemischen Eigenschaften aufweisen. Parallel zur Entwicklung neuer Methoden zur Herstellung dieser Materialien wurden entscheidende Fortschritte in Bezug auf Anwendungen erzielt, z. B. in der Biologie, der Katalyse, der Pharmazie und neuerdings auch in der Batterietechnologie. Dieser Kurzaufsatz stellt die jüngsten und wichtigsten Erfolge bei der Anwendung aufgabenspezifisch funktionalisierter Janus-Nanomaterialien im Bereich der heterogenen Katalyse für unterschiedliche chemische Transformationen vor. Er umfasst Reduktionreaktionen, oxidative Entschwefelung und Farbstoffabbau, asymmetrische Katalyse, Biomassetransformationen, Kaskadenreaktionen, Oxidationsreaktionen, übergangsmetallkatalysierte Kreuzkupplungsreaktionen, elektro- und photokatalytische Reaktionen sowie Gasphasenreaktionen. Zum Abschluss folgt ein Ausblick auf mögliche zukünftige Anwendungen.
Algorithms increasingly govern people's lives, including through rapidly spreading applications in the public sector. This paper sheds light on acceptance of algorithms used by the public sector emphasizing that algorithms, as parts of socio-technical systems, are always embedded in a specific social context. We show that citizens' acceptance of an algorithm is strongly shaped by how they evaluate aspects of this context, namely the personal importance of the specific problems an algorithm is supposed to help address and their trust in the organizations deploying the algorithm. The objective performance of presented algorithms affects acceptance much less in comparison. These findings are based on an original dataset from a survey covering two real-world applications, predictive policing and skin cancer prediction, with a sample of 2661 respondents from a representative German online panel. The results have important implications for the conditions under which citizens will accept algorithms in the public sector.
The development of algorithmic differentiation (AD) tools focuses mostly on handling floating point types in the target language. Taping optimizations in these tools mostly focus on specific operations like matrix vector products. Aggregated types like std::complex are usually handled by specifying the AD type as a template argument. This approach provides exact results, but prevents the use of expression templates. If AD tools are extended and specialized such that aggregated types can be added to the expression framework, then this will result in reduced memory utilization and improve the timing for applications where aggregated types such as complex number or matrix vector operations are used. Such an integration requires a reformulation of the stored data per expression and a rework of the tape evaluation process. We will demonstrate the overheads on a synthetic benchmark and show the improvement when aggregated types are handled properly by the expression framework of the AD tool.
A stereoselective synthesis of isoindolo[2,1-a]quinolin-11(5H)-ones containing three contiguous stereogenic centers is described. This Lewis-acid mediated reaction of enamides with N-aryl-acylimines affords the desired fused heterocyclic isoindolinones in high yields and diastereoselectivities. Scope and limitations of this method are discussed. The stereochemical outcome of this transformation indicates a stepwise reaction pathway.
The measurement of self-diffusion coefficients using pulsed-field gradient (PFG) nuclear magnetic resonance (NMR) spectroscopy is a well-established method. Recently, benchtop NMR spectrometers with gradient coils have also been used, which greatly simplify these measurements. However, a disadvantage of benchtop NMR spectrometers is the lower resolution of the acquired NMR signals compared to high-field NMR spectrometers, which requires sophisticated analysis methods. In this work, we use a recently developed quantum mechanical (QM) model-based approach for the estimation of self-diffusion coefficients from complex benchtop NMR data. With the knowledge of the species present in the mixture, signatures for each species are created and adjusted to the measured NMR signal. With this model-based approach, the self-diffusion coefficients of all species in the mixtures were estimated with a discrepancy of less than 2 % compared to self-diffusion coefficients estimated from high-field NMR data sets of the same mixtures. These results suggest benchtop NMR is a reliable tool for quantitative analysis of self-diffusion coefficients, even in complex mixtures.