KCS improves parametric maps from PET images by reducing noise and variance.
problem Improving the quality of parametric maps from PET images due to noise.
method Kinetic Compressive Sensing (KCS) method based on a hierarchical Bayesian model and novel reconstruction algorithm.
result KCS produces spatially coherent images and parametric maps with lower noise and better contrast.
Paper solves recovery of parametrizations from Legendre data.
problem Recovering parametrizations from Legendre data.
method Systematic and widely-applicable method to recover parametrizations from Gauss mapping and height function.
result Showed how to recover parametrization from dense subset of real-analytic parametrizations.
Estimates conditional Brenier maps using entropic optimal transport.
problem Non-parametric estimation of conditional Brenier maps.
method Entropic optimal transport for scalable non-parametric estimation.
result Entropic optimal transport maps asymptotically converge to conditional Brenier maps.
New algebraic parametrizations for harmonic maps to orthogonal group.
problem Finding solutions to harmonic maps from surfaces to O(n).
method Algebraic parametrizations and free holomorphic data.
result Reveals correspondence with null curves and minimal surfaces.
Improved spatial distribution learning with Bayesian transport maps and parametric shrinkage.
problem Learning non-Gaussian spatial distributions with limited training data.
method Proposed ShrinkTM approach using Bayesian transport maps with parametric shrinkage.
result ShrinkTM outperforms existing BTM, especially with few training samples.
Extends Teichmüller space parametrization using poles of higher order.
problem Parametrize Teichmüller space of crowned hyperbolic surfaces.
method Use meromorphic quadratic differentials with higher order poles to parametrize.
result Existence of harmonic map from punctured Riemann surface to crowned hyperbolic surface.
Parametric UMAP learns a mapping from data to embeddings.
problem Representing and learning from structured data.
method Parametric optimization over neural network weights for UMAP.
result Parametric UMAP performs comparably to non-parametric UMAP with faster online embeddings.
Upper bounds on neural network complexity for PDE solutions.
problem Approximating solutions of parametric PDEs without knowing their exact form.
method Using low-dimensionality of solution manifolds and a small reduced basis.
result Neural networks can approximate PDE solutions with sizes dependent only on the reduced basis.
Study contact structures on lens spaces, classifying rational knots.
problem Classify rational knots in lens spaces.
method One-parametric convex surface theory to classify Legendrian and transverse rational unknots.
result Determine the contact mapping class group of lens spaces.
Proposes a parametric t-SNE without perplexity tuning.
problem Non-parametric t-SNE's perplexity parameter limits DR quality.
method Multi-scale parametric t-SNE with deep neural network.
result Produces reliable embeddings with competitive neighborhood preservation.
Enhances Pontryagin-Thom theorem for manifold maps.
problem Identifying map spaces with moduli spaces of submanifolds.
method Space-level enhancement of Pontryagin-Thom theorem.
result Maps from manifolds to Thom spaces identified with moduli spaces of submanifolds.
Optimal regularity for 2D stationary varifolds proved.
problem Regularity of parametrized integer stationary varifolds in 2D.
method Established optimal regularity result for stationary varifolds.
result Smooth minimal branched immersion and constant multiplicity function.
New parametrization of 3-spheres using Johnson subgroups.
problem Constructing integral homology 3-spheres.
method Intrinsic description of equivalence relation on fourth Johnson subgroup.
result Intrinsic description of equivalence relation using fourth Johnson handlebody subgroups.
Paper introduces FNM framework for learning finite-dimensional parametrized models.
problem Efficiently learning finite-dimensional parametrized models from limited data.
method Fourier Neural Mappings (FNMs) framework for operator learning.
result End-to-end learning of PtO maps can be less data-efficient than learning the solution operator first.
In this paper, we explore holomorphic Segre preserving maps. First, we investigate holomorphic Segre preserving maps sending the complexification M of a generic real analytic submanifold $M \subseteq \C^N$ of finite type at some point p into the complexification M′ of a generic real analytic s…
The paper examines isometric timelike surfaces in 4D Minkowski space.
problem Analyzing geometric properties of isometric timelike surfaces.
method Study of Bour's theorem for four kinds of timelike helicoidal surfaces, analysis of geometric properties, presentation of parametrizations.
result Introduction of isometric pairs of timelike surfaces with same Gauss map.
A new method for optimizing neural networks with orthogonal constraints.
problem Optimizing neural networks with orthogonal constraints.
method Parametrization using the exponential map to transform constrained optimization into unconstrained.
result Faster, more accurate, and stable convergence in RNNs with orthogonal recurrent weights.
Let f:S1→R be a generic map. We may use f to define a new map f~:S1→R3 by f~(t)=(−f(t),f′(t),−f′′(t)), and if f is an embedding then the image of f~ will be a knot. Knots defined by such parametrizations are called holonomic knots. They were introduced in 1997 by Vassiliev, w…
Let X be an infinite hyperbolic surface endowed with an upper bounded geodesic pants decomposition. Alessandrini, Liu, Papadopoulos, Su and Sun \cite{ALPSS}, \cite{ALPS} parametrized the quasiconformal Teichmüller space Tqc(X) and the length spectrum Teichmüller space Tls(X) using the Fenchel-Nielsen coordi…
New map constructed from equivariant spectra for manifold study.
problem Understanding equivariant parametrized h-cobordism in non-manifold settings.
method Constructed a map from suspension G-spectrum to equivariant A-theory spectrum, compatible with tom Dieck splitting formulas.
result Fiber of constructed map is wedge of stable h-cobordism spectra.
We discuss the issue of branching in quasiregular mapping, and in particular the relation between branching and the problem of finding geometric parametrizations for topological manifolds. Other recent progress and open problems of a more function theoretic nature are also presented.
To study spacelike surfaces in the Lorentz-Minkowski space R14, we construct a pair of maps whose values are in the lightcone, called lr±-Gauss maps. We can use these maps to study umbilical spacelike surfaces and find parametrizations of spacelike surfaces of revolution of hyperbolic and …
Let M and N be closed n-dimensional manifolds, and equip N with a volume form σ. Let μbe an exact n-form on M. Arnold then asked the question: When can one find a map f:;N such that f*σ=μ. In 1973 Eliashberg and Gromov showed that this problem is, in a deep sense, trivial: It satisfies an h-principle, and whenever one …
The study describes the geometry of surfaces and their representations in SL(3,R).
problem Understanding the geometry of surface group representations into SL(3,R).
method Proving asymptotic formulas and harmonic map convergence for equivariant maps.
result The geometry of the image is weakly convex and a (one-third) translation surface.
New proof of index theorem for topological manifold bundles.
problem Index theorem for fiber bundles of compact topological manifolds.
method Use of a convenient framework for bivariant theories and recent results on the homotopy type of the topological cobordism category.
result Refinement of the assembly map for an extended A-theory characteristic.
Solitons are special polygon midpoints under affine transformations.
problem Characterizing polygons whose midpoints under affine transformations form a new polygon.
method Analyzing midpoints polygons and their relationship to affine transformations and differential equations.
result A large class of polygons are on an orbit of a one-parameter subgroup of the affine group, and these curves are solutions to a specific differential equation.
The study proves a strong parametric h-principle for minimal surfaces.
problem Proving a parametric h-principle for minimal surfaces.
method Using a parametric h-principle due to Forstneric and Larusson.
result The space of complete nonflat conformal minimal immersions has the same homotopy type as the space of continuous maps.
A major challenge in the training of recurrent neural networks is the so-called vanishing or exploding gradient problem. The use of a norm-preserving transition operator can address this issue, but parametrization is challenging. In this work we focus on unitary operators and describe a parametrization using the Lie al…
Bayesian model learns complex multivariate dependencies.
problem Learning dependency structures across multiple dimensions.
method Flexible Gaussian process priors and Dirichlet process for structure learning.
result Efficient variational inference for model parameters.
Differentiable cutting-plane layers solve parametric mixed-integer linear optimization problems.
problem Solving parametric mixed-integer linear optimization problems with changing data.
method Introducing cutting-plane layers (CPLs) for differentiable cutting-plane generation.
result The algorithm computes solutions with low integrality gaps and generalizes to unseen instances.
Develops neural network approximations for infinite-dimensional input-output maps.
problem Approximating input-output maps between infinite-dimensional spaces.
method Combines neural networks and model reduction techniques.
result Proves convergence of the proposed approximation methodology.
Adapts POD basis for parametric ROMs using pGP.
problem Updating POD basis for accurate system behavior over parameter space.
method Formulates problem as supervised statistical learning, uses pGP to learn mapping between parameter space and Grassmann manifold.
result Proposes pGP for optimal estimation of POD basis parameters and quantifies uncertainty.
Let M be a complete metric ANR-space such that for any metric compactum K the function space C(K,M) contains a dense set of Bing (resp., Krasinkiewicz) maps. It is shown that M has the following property: If f:X→Y is a perfect surjection between metric spaces, then C(X,M) with the source limitati…
This paper proposes a new method for conditional sampling using optimal transport.
problem Sampling conditional distributions in Bayesian inference and density estimation.
method Iterative block-triangular transport maps solving an optimal transport problem with a weighted L2 cost function.
result The proposed method extends the data-driven approach for conditional sampling.
A new statistical model uses Orlicz-Sobolev spaces with Gaussian weight.
problem Statistical modeling of infinite-dimensional probability measures.
method Affine statistical bundle on Gaussian Orlicz-Sobolev space.
result Provides tools for solving infinite-dimensional evolution problems.
New method trains generative models by reversing generator maps.
problem Training deep neural network generators.
method Non-parametrically estimate flexible code distributions by reversing generator maps.
result More powerful generative models, better latent structure modeling, explicit generalization control.
Engel knots map to formal knots without restrictions.
problem Classifying Engel knots and their properties.
method Weak homotopy equivalence of Engel knots to formal knots.
result Engel knots map to formal knots without restrictions.
New method solves high-dimensional Bayesian inverse problems efficiently.
problem Efficiently solving high-dimensional Bayesian inverse problems with limited data.
method Physics-informed Neural Operators with RealNVP architecture for invertibility and differentiability.
result Accurate approximations of the full posterior without additional forward solves or sampling.
This note is concerned with accurate and computationally efficient approximations of moments of Gaussian random variables passed through sigmoid or softmax mappings. These approximations are semi-analytical (i.e. they involve the numerical adjustment of parametric forms) and highly accurate (they yield 5% error at most…
Motivated by the need for parametric families of rich and yet tractable distributions in financial mathematics, both in pricing and risk management settings, but also considering wider statistical applications, we investigate a novel technique for introducing skewness or kurtosis into a symmetric or other distribution.…
Study counts orbits of mapping class group in shearing coordinates.
problem Counting orbits of mapping class group in shearing coordinates.
method Uses shearing coordinates and asymptotics of Teichmüller space.
result Asymptotic behavior of mapping class group orbits in shearing coordinates.
We develope a new and general notion of parametric measure models and statistical models on an arbitrary sample space Ω which does not assume that all measures of the model have the same null sets. This is given by a diffferentiable map from the parameter manifold M into the set of finite measures or probability me…
Study on Gauss map of anisotropic minimal surfaces with Morse index estimates.
problem Estimating the Morse index of anisotropic minimal surfaces.
method Local analysis of Gauss map, conformal geometric techniques applied to the Gauss map.
result Upper and lower estimates for the Morse index of anisotropic minimal surfaces.
We present the FuSSO, a functional analogue to the LASSO, that efficiently finds a sparse set of functional input covariates to regress a real-valued response against. The FuSSO does so in a semi-parametric fashion, making no parametric assumptions about the nature of input functional covariates and assuming a linear f…
Bökstedt and Madsen defined an infinite loop map from the embedded d-dimensional cobordism category of Galatius, Madsen, Tillmann and Weiss to the algebraic K-theory of BO(d) in the sense of Waldhausen. The purpose of this paper is to establish two results in relation to this map. The first result is that it exte…
Characterizes pseudo-Anosov mapping classes on general marked surfaces.
problem Stability of mapping classes on marked surfaces.
method Cluster algebraic description and reduction procedure of mapping classes.
result Characterizes pseudo-Anosov mapping classes in terms of uniform sign stability.
We consider the biharmonicity condition for maps between Riemannian manifolds (see [BK]), and study the non-geodesic biharmonic curves in the Heisenberg group H_3. First we prove that all of them are helices, and then we obtain explicitly their parametric equations.
Symplectic manifold rays can be removed without changing the manifold's structure.
problem Removing parametrized rays from a symplectic manifold without altering its structure.
method Using a specific vector field condition to ensure symplectomorphism.
result Symplectic manifolds and their subsets remain symplectomorphic after removing parametrized rays.