Tuning the critical magnetic field of the triplon Bose-Einstein condensation in Ba3−x_{3-x}Srx_xCr2_2O8_8

The structure and magnetic interactions of the triplon Bose-Einstein condensation candidates Ba$_3$Cr$_2$O$_8$ and Sr$_3$Cr$_2$O$_8$ have been studied thoroughly in the literature, but little is known about a possible triplon condensation in the corresponding solid solution Ba$_{3-x}$Sr$_x$Cr$_2$O$_8$. We have prepared various members of this solid solution and systematically examined their magnetic properties in high magnetic fields up to 60 T and at low temperatures down to 340 mK, by means of pulsed field and cantilever magnetometry. From these experiments for $x\in\{3,2.9,2.8,2.7,2.6,2.5\}$, we find that the critical fields of Ba$_{3-x}$Sr$_x$Cr$_2$O$_8$ decrease monotonically as a function of the Sr content $x$. This change is in good agreement with the earlier reported variation of the magnetic interactions in these compounds

Tailor-made composite functions as tools in model choice: the case of sigmoidal vs bi-linear growth profiles

BACKGROUND: Roots are the classical model system to study the organization and dynamics of organ growth zones. Profiles of the velocity of root elements relative to the apex have generally been considered to be sigmoidal. However, recent high-resolution measurements have yielded bi-linear profiles, suggesting that sigmoidal profiles may be artifacts caused by insufficient spatio-temporal resolution. The decision whether an empirical velocity profile follows a sigmoidal or bi-linear distribution has consequences for the interpretation of the underlying biological processes. However, distinguishing between sigmoidal and bi-linear curves is notoriously problematic. A mathematical function that can describe both types of curve equally well would allow them to be distinguished by automated curve-fitting. RESULTS: On the basis of the mathematical requirements defined, we created a composite function and tested it by fitting it to sigmoidal and bi-linear models with different noise levels (Monte-Carlo datasets) and to three experimental datasets from roots of Gypsophila elegans, Aurinia saxatilis, and Arabidopsis thaliana. Fits of the function proved robust with respect to noise and yielded statistically sound results if care was taken to identify reasonable initial coefficient values to start the automated fitting procedure. Descriptions of experimental datasets were significantly better than those provided by the Richards function, the most flexible of the classical growth equations, even in cases in which the data followed a smooth sigmoidal distribution. CONCLUSION: Fits of the composite function introduced here provide an independent criterion for distinguishing sigmoidal and bi-linear growth profiles, but without forcing a dichotomous decision, as intermediate solutions are possible. Our function thus facilitates an unbiased, multiple-working hypothesis approach. While our discussion focusses on kinematic growth analysis, this and similar tailor-made functions will be useful tools wherever models of steadily or abruptly changing dependencies between empirical parameters are to be compared

Void-and-Cluster Sampling of Large Scattered Data and Trajectories

We propose a data reduction technique for scattered data based on statistical sampling. Our void-and-cluster sampling technique finds a representative subset that is optimally distributed in the spatial domain with respect to the blue noise property. In addition, it can adapt to a given density function, which we use to sample regions of high complexity in the multivariate value domain more densely. Moreover, our sampling technique implicitly defines an ordering on the samples that enables progressive data loading and a continuous level-of-detail representation. We extend our technique to sample time-dependent trajectories, for example pathlines in a time interval, using an efficient and iterative approach. Furthermore, we introduce a local and continuous error measure to quantify how well a set of samples represents the original dataset. We apply this error measure during sampling to guide the number of samples that are taken. Finally, we use this error measure and other quantities to evaluate the quality, performance, and scalability of our algorithm.Comment: To appear in IEEE Transactions on Visualization and Computer Graphics as a special issue from the proceedings of VIS 201

The Importance of Distrust in AI

In recent years the use of Artificial Intelligence (AI) has become increasingly prevalent in a growing number of fields. As AI systems are being adopted in more high-stakes areas such as medicine and finance, ensuring that they are trustworthy is of increasing importance. A concern that is prominently addressed by the development and application of explainability methods, which are purported to increase trust from its users and wider society. While an increase in trust may be desirable, an analysis of literature from different research fields shows that an exclusive focus on increasing trust may not be warranted. Something which is well exemplified by the recent development in AI chatbots, which while highly coherent tend to make up facts. In this contribution, we investigate the concepts of trust, trustworthiness, and user reliance. In order to foster appropriate reliance on AI we need to prevent both disuse of these systems as well as overtrust. From our analysis of research on interpersonal trust, trust in automation, and trust in (X)AI, we identify the potential merit of the distinction between trust and distrust (in AI). We propose that alongside trust a healthy amount of distrust is of additional value for mitigating disuse and overtrust. We argue that by considering and evaluating both trust and distrust, we can ensure that users can rely appropriately on trustworthy AI, which can both be useful as well as fallible.Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The version of records of this contribution is published in Explainable Artificial Intelligence First World Conference, xAI 2023, Lisbon, Portugal, July 26-28, 2023, Proceedings, Part III (CCIS, volume 1903) and is available at https://doi.org/10.1007/978-3-031-44070-

Computer-aided Optical Plasma Postprocessing Applied on Model Spark Gaps

Spark gaps are used as surge protective devices (SPD class 1) for low voltage grids protection against surge currents and overvoltages. For practical research of the narrow gap plasma of spark gaps, high-speed camera recordings are used in modified transparent test models. In this test setup, current densities of 1010 A/m2 are generated. In order to optimize and automate the evaluation process of camera recordings, an image analysis tool is developed further in this contribution. After basic image improvement and segmentation, this research optimizes a detection algorithm for plasma location and distribution. As a result, the known plasma distribution gives access to significantly more information about the plasma behaviour and the spatial distribution of radiation

Innovationsverhalten der deutschen Wirtschaft : Hintergrundbericht zur Innovationserhebung 2001

Die hohe Beteiligung an Innovationsaktivitäten und der weiterhin hohe wirtschaftliche Erfolg, den Innovatoren aus der Einführung neuer Produkte und Prozesse - gerade auch auf internationalen Märkten - erzielen können, zeigt, dass das deutsche Innovationssystem insgesamt wettbewerbsfähig ist. Dies ist jedoch kein Selbstläufer. Dieser Bericht liefert einige wichtige Hinweise auf Bereiche des Innovationssystems, in denen weitere Anstrengungen notwendig sind, um auch künftig die Leistungsfähigkeit der deutschen Wirtschaft zu erhalten