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I define the Standard Supersymmetric Model (SSM) as the minimal supersymmetric extension ofthe Standard Model with gauge coupling unification and universal soft supersymmetry breaking at the unification scale. This well-defined model has a…

High Energy Physics - Phenomenology · Physics 2007-05-23 S. Kelley

Normal multi-scale transform [4] is a nonlinear multi-scale transform for representing geometric objects that has been recently investigated [1, 7, 10]. The restrictive role of the exact order of polynomial reproduction $P_e$ of the…

Numerical Analysis · Mathematics 2013-11-19 Stanislav Harizanov

As modeling and visualization applications proliferate, there arises a need to simplify large polygonal models at interactive rates. Unfortunately existing polygon mesh simplification algorithms are not well suited for this task because…

Graphics · Computer Science 2025-07-22 Dmitry Brodsky , Benjamin Watson

This survey gives an overview of three central algebraic themes related to the study of splines: duality, group actions, and homology. Splines are piecewise polynomial functions of a prescribed order of smoothness on some subdivided domain…

Numerical Analysis · Mathematics 2023-12-18 Martina Lanini , Hal Schenck , Julianna Tymoczko

Locally refined spline surfaces (LRB) is a representation well suited for scattered data approximation. When a data set has local details in some areas and is largely smooth in other, LR B-splines allow the spatial distribution of degrees…

Numerical Analysis · Mathematics 2020-12-16 Vibeke Skytt , Tor Dokken

The problem of extrapolation and interpolation of asymptotic series is considered. Several new variants of improving the accuracy of the self-similar approximants are suggested. The methods are illustrated by examples typical of chemical…

Mathematical Physics · Physics 2010-04-08 V. I. Yukalov , E. P. Yukalova , S. Gluzman

Monotonicity constraints are powerful regularizers in statistical modelling. They can support fairness in computer-aided decision making and increase plausibility in data-driven scientific models. The seminal min-max (MM) neural network…

Machine Learning · Computer Science 2024-05-28 Christian Igel

Deep learning models are gaining popularity and potency in predicting polymer properties. These models can be built using pre-existing data and are useful for the rapid prediction of polymer properties. However, the performance of a deep…

Materials Science · Physics 2022-10-14 Himanshu , Tarak K Patra

The study of combinatorial optimization problems with a submodular objective has attracted much attention in recent years. Such problems are important in both theory and practice because their objective functions are very general. Obtaining…

Data Structures and Algorithms · Computer Science 2016-11-11 Niv Buchbinder , Moran Feldman

Supersymmetric nonlinear sigma models have target spaces that carry interesting geometry. The geometry is richer the more supersymmetries the model has. The study of models with two dimensional world sheets is particularly rewarding since…

High Energy Physics - Theory · Physics 2022-03-08 Ulf Lindström

Based on the computation of a superset of the implicit support, implicitization of a parametrically given hyper-surface is reduced to computing the nullspace of a numeric matrix. Our approach exploits the sparseness of the given parametric…

Algebraic Geometry · Mathematics 2014-11-12 Ioannis Emiris , Tatjana Kalinka , Christos Konaxis

In this paper, we formally investigate two mathematical aspects of Hermite splines which translate to features that are relevant to their practical applications. We first demonstrate that Hermite splines are maximally localized in the sense…

Numerical Analysis · Mathematics 2019-02-11 Julien Fageot , Shayan Aziznejad , Michael Unser , Virginie Uhlmann

Semialgebraic splines are functions that are piecewise polynomial with respect to a cell decomposition into sets defined by polynomial inequalities. We study bivariate semialgebraic splines, formulating spaces of semialgebraic splines in…

Commutative Algebra · Mathematics 2016-04-21 Michael DiPasquale , Frank Sottile , Lanyin Sun

For solving linear ill-posed problems regularization methods are required when the right hand side is with some noise. In the present paper regularized solutions are obtained by implicit iteration methods in Hilbert scales. % By exploiting…

Numerical Analysis · Mathematics 2015-05-20 Qinian Jin , Ulrich Tautenhahn

We present a PDE-based approach for the multidimensional extrapolation of smooth scalar quantities across interfaces with kinks and regions of high curvature. Unlike the commonly used method of [2] in which normal derivatives are…

Numerical Analysis · Mathematics 2023-09-26 Daniil Bochkov , Frederic Gibou

The local refinement of PHT-splines (polynomial splines over hierarchical T-meshes) is achieved by a simple cross insertion, which may introduce superfluous control points or coefficients. By allowing split-in-half in mesh refinement,…

Numerical Analysis · Mathematics 2019-04-18 Qian Ni , Xuhui Wang , Jiansong Deng

In the present paper, using S.L. Sobolev's method, interpolation spline that minimizes the expression $\int_0^1(\varphi^{(m)}(x)+\omega^2\varphi^{(m-2)}(x))^2dx$ in the $K_2(P_m)$ space are constructed. Explicit formulas for the…

Numerical Analysis · Mathematics 2014-10-21 Abdullo R. Hayotov

A standard construction in approximation theory is mesh refinement. For a simplicial or polyhedral mesh D in R^k, we study the subdivision D' obtained by subdividing a maximal cell of D. We give sufficient conditions for the module of…

Numerical Analysis · Mathematics 2016-10-18 Hal Schenck , Tatyana Sorokina

In this paper, we develop approximation error estimates as well as corresponding inverse inequalities for B-splines of maximum smoothness, where both the function to be approximated and the approximation error are measured in standard…

Numerical Analysis · Mathematics 2017-05-16 Stefan Takacs , Thomas Takacs

In many classification problems it is desirable to output well-calibrated probabilities on the different classes. We propose a robust, non-parametric method of calibrating probabilities called SplineCalib that utilizes smoothing splines to…

Machine Learning · Statistics 2018-09-21 Brian Lucena
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