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Geometric deep learning on molecular representations
Nature Machine Intelligence, 2021Geometric deep learning (GDL) is based on neural network architectures that incorporate and process symmetry information. GDL bears promise for molecular modelling applications that rely on molecular representations with different symmetry properties and
Kenneth Atz, F. Grisoni, G. Schneider
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Geometric Mappings on Geometric Lattices
Canadian Journal of Mathematics, 1971It is a classical result of mathematics that there is an intimate connection between linear algebra and projective or affine geometry. Thus, many algebraic results can be given a geometric interpretation, and geometric theorems can quite often be proved more easily by algebraic methods.
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Quaestiones Mathematicae, 2003
Abstract unavailable at this time... Mathematics Subject Classification (2000): 03E72, 52A01, 54E35, 53C70, 14M15 Quaestiones Mathematicae 25 (2002), 147 ...
Lubczonok, G, Remsing, C.C
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Abstract unavailable at this time... Mathematics Subject Classification (2000): 03E72, 52A01, 54E35, 53C70, 14M15 Quaestiones Mathematicae 25 (2002), 147 ...
Lubczonok, G, Remsing, C.C
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Fully Convolutional Geometric Features
IEEE International Conference on Computer Vision, 2019Extracting geometric features from 3D scans or point clouds is the first step in applications such as registration, reconstruction, and tracking. State-of-the-art methods require computing low-level features as input or extracting patch-based features ...
C. Choy, Jaesik Park, V. Koltun
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Southeast Asian Bulletin of Mathematics, 2000
The author calls ``geometric dynamics'' the fact that on a semi-Riemannian manifold \((M,g)\) the orbits of a vector field \(X\) are the solutions of a second-order differential equation \(\ddot{x} = \text{grad } f + F(\dot{x})\), where \(f = \frac{1}{2} g(X,X)\) and \(g(F(v),w) = d(g(X))(p)(v,w)\) for tangent vectors \(v,w\) at \(p \in M\).
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The author calls ``geometric dynamics'' the fact that on a semi-Riemannian manifold \((M,g)\) the orbits of a vector field \(X\) are the solutions of a second-order differential equation \(\ddot{x} = \text{grad } f + F(\dot{x})\), where \(f = \frac{1}{2} g(X,X)\) and \(g(F(v),w) = d(g(X))(p)(v,w)\) for tangent vectors \(v,w\) at \(p \in M\).
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Computational Mechanics, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
BOTTASSO, CARLO LUIGI +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
BOTTASSO, CARLO LUIGI +2 more
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2020
In this short note we shall briefly describe a few flavours of contemporary geometric origami, from kusudama to tessellations and beyond. It will be an impressionistic and not technical presentation, just to give an idea of what can be done with geometric origami.
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In this short note we shall briefly describe a few flavours of contemporary geometric origami, from kusudama to tessellations and beyond. It will be an impressionistic and not technical presentation, just to give an idea of what can be done with geometric origami.
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Geometric Reasoning With Geometric Algebra
2001This chapter starts with an introduction to Clifford algebra for Euclidean geometry and shows how geometric theorems can be proved automatically in the Clifford algebra formalism. A short review of available approaches is given and examples are provided.
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Proceedings of seventh annual ACM symposium on Theory of computing - STOC '75, 1975
The complexity of a number of fundamental problems in computational geometry is examined and a number of new fast algorithms are presented and analyzed. General methods for obtaining results in geometric complexity are given and upper and lower bounds are obtained for problems involving sets of points, lines, and polygons in the plane.
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The complexity of a number of fundamental problems in computational geometry is examined and a number of new fast algorithms are presented and analyzed. General methods for obtaining results in geometric complexity are given and upper and lower bounds are obtained for problems involving sets of points, lines, and polygons in the plane.
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Annual Review of Anthropology, 2007
Morphometrics, the field of biological shape analysis, has undergone major change in recent years. Most of this change has been due to the development and adoption of methods to analyze the Cartesian coordinates of anatomical landmarks. These geometric morphometric (GM) methods focus on the retention of geometric information throughout a study and ...
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Morphometrics, the field of biological shape analysis, has undergone major change in recent years. Most of this change has been due to the development and adoption of methods to analyze the Cartesian coordinates of anatomical landmarks. These geometric morphometric (GM) methods focus on the retention of geometric information throughout a study and ...
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