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Crosslisted article(s) found for math.DG. https://arxiv.org/list/math.DG/new
[1/1]:
- Smooth Fractal Trees: Analytic Generators and Discrete Equivalence
Henk Mulder

The Paris agreement is 10 years old today.
How time flies. But how little really has changed.
"Paris Climate Change Agreement, 2015: the good, the bad and the ugly."
https://steadystatemanchester.net/2015/12/1…

Paris Climate Change Agreement, 2015: the good, the bad and the ugly.
A lot has been written on the Paris international agreement on climate change.  To try and cut through all those words to the essential issues, here is our very brief analytic summary. The good Asp…

10 years on, believe it or not, the Paris Agreement was signed.
This was our very short reaction at the time. Prescient, you might think.
The Good, The Bad, The Ugly.
Paris Climate Change Agreement, 2015: the good, the bad and the ugly. | Steady State Manchester https://…

Paris Climate Change Agreement, 2015: the good, the bad and the ugly.
A lot has been written on the Paris international agreement on climate change.  To try and cut through all those words to the essential issues, here is our very brief analytic summary. The good Asp…

Deformation quantisation of exact shifted symplectic structures, with an application to vanishing cycles
J. P. Pridham
https://arxiv.org/abs/2511.07602 https://arxiv.org/pdf/2511.07602 https://arxiv.org/html/2511.07602
arXiv:2511.07602v1 Announce Type: new
Abstract: We extend the author's and CPTVV's correspondence between shifted symplectic and Poisson structures to establish a correspondence between exact shifted symplectic structures and non-degenerate shifted Poisson structures with formal derivation, a concept generalising constructions by De Wilde and Lecomte. Our formulation is sufficiently general to encompass derived algebraic, analytic and $\mathcal{C}^{\infty}$ stacks, as well as Lagrangians and non-commutative generalisations. We also show that non-degenerate shifted Poisson structures with formal derivation carry unique self-dual deformation quantisations in any setting where the latter can be formulated.
One application is that for (not necessarily exact) $0$-shifted symplectic structures in analytic and $\mathcal{C}^{\infty}$ settings, it follows that the author's earlier parametrisations of quantisations are in fact independent of any choice of associator, and generalise Fedosov's parametrisation of quantisations for classical manifolds.
Our main application is to complex $(-1)$-shifted symplectic structures, showing that our unique quantisation of the canonical exact structure, a sheaf of twisted $BD_0$-algebras with derivation, gives rise to BBDJS's perverse sheaf of vanishing cycles, equipped with its monodromy operator.
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Measuring dissimilarity between convex cones by means of max-min angles
Welington de Oliveira, Valentina Sessa, David Sossa
https://arxiv.org/abs/2511.10483 https://arxiv.org/pdf/2511.10483 https://arxiv.org/html/2511.10483
arXiv:2511.10483v1 Announce Type: new
Abstract: This work introduces a novel dissimilarity measure between two convex cones, based on the max-min angle between them. We demonstrate that this measure is closely related to the Pompeiu-Hausdorff distance, a well-established metric for comparing compact sets. Furthermore, we examine cone configurations where the measure admits simplified or analytic forms. For the specific case of polyhedral cones, a nonconvex cutting-plane method is deployed to compute, at least approximately, the measure between them. Our approach builds on a tailored version of Kelley's cutting-plane algorithm, which involves solving a challenging master program per iteration. When this master program is solved locally, our method yields an angle that satisfies certain necessary optimality conditions of the underlying nonconvex optimization problem yielding the dissimilarity measure between the cones. As an application of the proposed mathematical and algorithmic framework, we address the image-set classification task under limited data conditions, a task that falls within the scope of the \emph{Few-Shot Learning} paradigm. In this context, image sets belonging to the same class are modeled as polyhedral cones, and our dissimilarity measure proves useful for understanding whether two image sets belong to the same class.
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