Results 81 to 90 of about 4,580 (209)
Medial Axis Aware Learning of Signed Distance Functions
Abstract We propose a novel variational method to compute a highly accurate global signed distance function (SDF) to a given point cloud. To this end, the jump set of the gradient of the SDF, which coincides with the medial axis of the surface, is explicitly taken into account through a higher‐order variational formulation that enforces linear growth ...
Samuel Weidemaier +2 more
wiley +1 more source
Tangent Blow‐Ups for Processing Non‐Manifold Geometry
Abstract Many geometry processing pipelines implicitly assume their input data is a manifold, or is sampled from one, with a unique tangent plane at every point. Geometric data, however, routinely contains sharp features like edges, corners, self‐intersections, branching junctions, and other singularities, rendering standard methods ill‐defined at ...
Alice Petrov +3 more
wiley +1 more source
Observer Design for a Class of Lipschitz Nonlinear Systems with Output Uncertainty
:By means of linear matrix inequality (LMI) based Lyapunov method,a nonlinear observer was proposed for a class of Lipschitz nonlinear system with output uncertainty.The observer gain matrix was selected according to LMI with the output uncertainty ...
doaj
Strictly Conservative Neural Distance Fields
Abstract We propose a first method to generate neural unsigned or signed distance fields (SDFs) that are guaranteed to be conservative with respect to a given 3D shape. This means the true distance is never overestimated and the zero‐level set is a bounding volume for the shape. The method makes use of neural network architectures that ensure Lipschitz
I. Ludwig, M. Campen
wiley +1 more source
A Splitting Architecture for Exact Reduced Coulomb Friction
Abstract Existing approaches to frictional contact dynamics typically either modify the Coulomb law to improve numerical robustness or solve the exact law in a fully coupled monolithic form. However, in its reduced form, exact Coulomb friction can be written as a cone complementarity problem with an augmented velocity, which reveals a natural split ...
Hongcheng Song +3 more
wiley +1 more source
A Non‐Parametric Framework for Correlation Functions on Product Metric Spaces
Summary We propose a non‐parametric framework for analysing data defined over products of metric spaces, a versatile class encountered in various fields. This framework accommodates non‐stationarity and seasonality and is applicable to both local and global domains, such as the Earth's surface, as well as domains evolving over linear time or time ...
Pier Giovanni Bissiri +3 more
wiley +1 more source
A note on generalised information criteria for structured sparse models
Summary We propose a generalised information criteria ( gic) that accounts for sparsity pattern in the model. We obtain both asymptotic and nonasymptotic results for model selection. Moreover, we show that the gic is useful for selecting the regularisation parameter in regularised m$$ m $$ estimation in high‐dimensional scenarios.
Eduardo Fonseca Mendes +1 more
wiley +1 more source
Fairness‐aware insurance pricing: A multi‐objective optimization approach
Abstract Machine‐learning models can provide accurate predictions in insurance pricing, but can also increase disparities between protected groups. Existing fairness‐aware pricing approaches typically target one fairness notion at a time, making it difficult to compare trade‐offs between predictive accuracy, group fairness, individual fairness, and ...
Tim J. Boonen, Xinyue Fan, Zixiao Quan
wiley +1 more source
ABSTRACT We propose a new formulation of the Vašičekmodel within the framework of functional data analysis. We treat observations (continuous‐time rates) within a suitably defined trading day as a single statistical object. We then consider a sequence of such objects, indexed by day.
Piotr Kokoszka +4 more
wiley +1 more source
Density‐Valued ARMA Models by Spline Mixtures
ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley +1 more source

