Titlebook: Geometric Science of Information; 6th International Co Frank Nielsen,Frédéric Barbaresco Conference proceedings 2023 The Editor(s) (if appl

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https://doi.org/10.1007/978-981-13-7006-9tive of the way these shapes are parameterized. However for analysis of these shape spaces, it is sometimes useful to have a parameterization at hand, in particular if one is interested in deforming shapes. The purpose of the paper is to examine three different methods that one can follow to endow s

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Are Games a Viable Home Numeracy Practice?, algorithms in shape optimization are often based on techniques from differential geometry. Challenges arise when an application demands a non-smooth shape, which is commonly-encountered as an optimal shape for fluid-mechanical problems. In order to avoid the restriction to infinitely-smooth shapes

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Re-theorising Play as Pedagogical,tance, the arc-length parameterization is canonical, but we consider other natural parameterizations like the parameterization proportional to the curvature of the curve. Both aforementionned parameterizations are very natural and correspond to a natural physical movement: the arc-length parameteriz

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Final Reflections: Emerging Policy Issues,ifolds of nonlinear flags, i.e. nested submanifolds of a fixed type. The gauge invariant elastic metric on the shape space of surfaces involves the mean curvature and the normal deformation, i.e. the sum and the difference of the principal curvatures .. The proposed gauge invariant elastic metrics o

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Elaine Walker PhD,Karen M. Hochman MD3D curves in the visual scene as a sub-Riemannian structure. Horizontal curves in this setting express good continuation principles in 3D. Starting from the equation of neural activity we apply harmonic analysis techniques in the sub-Riemannian structure to solve the correspondence problem and find

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A European Debate about Colbert,ving HJB-PDEs on lifted homogeneous spaces such as the homogeneous space of 2D positions and orientations isomorphic to .. PDE-G-CNNs generalize G-CNNs and are provably equivariant to actions of the roto-translation group .(2). Moreover, PDE-G-CNNs automate geometric image processing via orientation

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Shilpa Kusampudi,Nagarjun Kondurusentations. The equivariant encoder/decoder ensures that these latents are geometrically meaningful and grounded in the input space. Mapping these geometry-.grounded latents to hyperspheres allows us to interpret them as points in a Kendall shape space. This paper extends the recent . paradigm by Va

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