arXiv research

Paper finds maximum curvature of Bézier-spline curves.

Sinh-acceleration speeds up B-spline option pricing.

Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups. Central in the success of G-CNNs is the lifting of feature maps to higher dimensional disentangled representations, in which data characteristics are effectively learned, geometri…

A new method evolves point clouds using B-splines for smooth surfaces.

This paper is devoted to the application of B-splines to volatility modeling, specifically the calibration of the leverage function in stochastic local volatility models and the parameterization of an arbitrage-free implied volatility surface calibrated to sparse option data. We use an extension of classical B-splines …

This paper presents an efficient algorithm for evolving point cloud data on smooth manifolds using B-Splines.

In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The mo…

Period maps surjective for certain gravitational instantons.

Isogeometric analysis is a recently developed computational approach that integrates finite element analysis directly into design described by non-uniform rational B-splines (NURBS). In this paper we show that price surfaces that occur in option pricing can be easily described by NURBS surfaces. For a class of stochast…

Bayesian nonparametric LABS model adapts to function smoothness in Besov spaces.

We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GHP distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or…

Many attempts have been made in recent decades to integrate machine learning (ML) and topological data analysis. A prominent problem in applying persistent homology to ML tasks is finding a vector representation of a persistence diagram (PD), which is a summary diagram for representing topological features. From the pe…

Kolmogorov-Arnold Networks achieve optimal convergence rates in nonparametric regression.

Smooth neural TPPs using B-splines for better efficiency and accuracy.

BEKAN uses RBFs and evolutionary methods to solve PDEs with boundary conditions.

Efficient numerical method for time-fractional Black-Scholes model.

We give a uniform and elementary treatment of many classical and new triply periodic minimal surfaces in Euclidean space, based on a Schwarz-Christoffel formula for periodic polygons in the plane. Our surfaces share the property that vertical symmetry planes cut them into simply connected pieces.

Dropout improves regularization in flexible models for rare features.

New method speeds up sparse Gaussian processes for large datasets.

We study the motion of discrete interfaces driven by ferromagnetic interactions in a two-dimensional low-contrast periodic environment, by coupling the minimizing movements approach by Almgren, Taylor and Wang and a discrete-to-continuum analysis. As in a recent paper by Braides and Scilla dealing with high-contrast pe…

Locally-verifiable conditions ensure exactness of spline discrete de Rham complex.

Motivated by applications in architecture and design, we present a novel method for increasing the developability of a B-spline surface. We use the property that the Gauss image of a developable surface is 1-dimensional and can be locally well approximated by circles. This is cast into an algorithm for thinning the Gau…

Develops flexible non-parametric ACFs using B-spline kernels.

We study the periods mapping from the moduli space of real hyperelliptic curves with marked point on an oriented oval to the euclidean space. The mapping arises in the analysis of Chebyshev construction used in the constrained optimization of the uniform norm of polynomials and rational functions. The decomposition of …

A new model approximates complex functions in parameter space.

Deep-SITAR uses autoencoders to predict growth patterns.

A new knot selection method for GAMs reduces model complexity.

Uniformizes varieties with log-canonical singularities using ball quotients.

Parametric spatial transformation models have been successfully applied to image registration tasks. In such models, the transformation of interest is parameterized by a fixed set of basis functions as for example B-splines. Each basis function is located on a fixed regular grid position among the image domain, because…

Let $f\colon M\to M$ be an expansive homeomorphism with dense topologically hyperbolic periodic points, $M$ a compact manifold. Then there is a local product structure in an open and dense subset of $M$. Moreover, if some topologically hyperbolic periodic point has codimension one, then this local product structure is …

Study connects surface twists to curve invariants.

Uniform bounds found for Sierpinski carpet hyperbolic components.

New method weaves paper strips for designing curved surfaces with elasticity.

Efficiently estimates covariance for sparse functional data.

We review some recent results in the generic rigidity theory of planar frameworks with forced symmetry, giving a uniform treatment to the topic. We also give new combinatorial characterizations of minimally rigid periodic frameworks with fixed-area fundamental domain and fixed-angle fundamental domain.

We show that, up to some natural normalizations, the moduli space of singly periodic complete embedded maximal surfaces in the Lorentz-Minkowski space $ł^3=(\r^3,dx_1^2+dx_2^2-dx_3^2),$ with fundamental piece having a finite number $(n+1)$ of singularities, is a real analytic manifold of dimension $3n+4.$ The underlyin…

Proposes a new model for high-dimensional data analysis with unknown link function.

Orbifold uniformization of complex algebraic variety via polystable parabolic Higgs bundle

We prove the Livšic Theorem for arbitrary $GL(m,\mathbb R)$ cocycles. We consider a hyperbolic dynamical system $f : X \to X$ and a Hölder continuous function $A: X \to GL(m,\mathbb R)$. We show that if $A$ has trivial periodic data, i.e. $A(f^{n-1} p) ... A(fp) A(p) = Id$ for each periodic point $p=f^n p$, then there …

Approximating complex curves with simple parametric curves is widely used in CAGD, CG, and CNC. This paper presents an algorithm to compute a certified approximation to a given parametric space curve with cubic B-spline curves. By certified, we mean that the approximation can approximate the given curve to any given pr…

DecompKAN improves time series forecasting accuracy and transparency.

Two projective structures on Riemann surfaces are described and shown not to be identical.

Revisits stochastic collocation with exponential splines for option pricing.

In this paper, we address the issue of quaternionic Toledo invariant to study the character variety of two dimensional complex hyperbolic uniform lattices into $SU(n,2)$. We construct four distinct representations to prove that the character variety contains at least four distinct components. We also address the existe…

Bayesian semi-supervised learning for multi-class classification.

Study ergodic properties of geodesic flows on specific manifolds without conjugate points.

KANHedge improves hedging of high-dimensional options using learnable B-spline activation functions.

RST improves environmental time series classification accuracy using randomized B-spline trees.