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Sven Holtrop

Publications and source records attributed to Sven Holtrop.

2 recordsLinked to original sources

Higher Holonomy for Higher Lie Algebroid Connections on Graded Vector Bundles

We prove the existence of a Riemann--Hilbert $A_\infty$-functor from the curved dg-category of $\mathscr{A}$-connections on graded vector bundles, where $\mathscr{A}$ is a higher Lie algebroid, to the curved dg-category of $Π_\infty(\mathscr{A})$-connections on graded vector bundles, where $Π_\infty(\mathscr{A})$ is the homotopy groupoid of $\mathscr{A}$. When restricted to the sub dg-categories of flat objects, it induces an $A_\infty$-functor from the dg-category of representations up to homotopy of $\mathscr{A}$ to the dg-category of representations up to homotopy of $Π_\infty(\mathscr{A})$, which recovers the one from arXiv:1011.4693 and arXiv:0908.2843 when $\mathscr{A}$ is a Lie algebroid. The key ingredient for defining such functors is a non-abelian Stokes's theorem for endomorphism-valued differential forms. Furthermore, for a graded vector bundle $E$, we introduce the notion of its higher general linear groupoid in such a way that $G$-connections on $E$, for a higher Lie groupoid $G$, become smooth functors from $G$ to the higher general linear groupoid of $E$. We combine this with the Riemann--Hilbert functor to define higher parallel transport functors for $\mathscr{A}$-connections. Finally, we use these functors to define the higher holonomy groups of $\mathscr{A}$-connections and endow them with the structure of augmented simplicial Lie groups.

math.DG↗

Interpolating Location Data with Brownian Motion

In 2018 the ''Onderweg in Nederland'' (translation: the Dutch Travel Survey) project by Statistics Netherlands was commenced, where participants were asked to track their location using an app, and check if the data is correct. An issue that occurs is so-called 'gaps' in the location data, where a whole sequence of data points is missing. The easiest way to fill such a gap is with a straight line, but this leads to systematic errors such as an underestimation of the distance travelled. A more realistic way to fill this gap is with a stochastic process. We use a Brownian bridge to model the movement of a traveller, as these have been used before successfully in ecological research. We find an explicit expression for the distance travelled in terms of some parameters that can be obtained from the data. To test whether this method gives an accurate estimation of the distance travelled, we simulate travel data using multiple different processes.

stat.AP↗