Constructs a support-preserving homotopy for differential forms with boundary decay estimates.
problem Non-uniqueness of chain homotopies in de Rham complexes with boundary decay properties.
method Constructs a specific chain homotopy with desirable support propagation and boundary decay estimates.
result Obtains a support-preserving right inverse of the divergence operator with optimal decay estimates.
We prove that a compactly supported homeomorphism of a smooth manifold of dimension greater or equal to 5 can be approximated uniformly by compactly supported diffeomorphisms if and only if it is isotopic to a diffeomorphism. If the given homeomorphism is in addition volume preserving, then it can be approximated unifo…
This paper speeds up SVC clustering by compressing data while preserving key properties.
problem Efficiently clustering large-scale real-world data sets.
method Spectrum-preserving data compression for fast support vector clustering.
result Achieved 100X and 115X speedups on real-world data sets while maintaining clustering quality.
We prove that a monomorphic functor F:Comp→Comp with finite supports is epimorphic, continuous, and its maximal ∅-modification F∘ preserves intersections. This implies that a monomorphic functor F:Comp→Comp of finite degree degF≤n preserves (finite-dimensional) compact ANR's if the spac…
This paper describes a characterization of tightness of closed contact 3-manifolds in terms of supporting open book decompositions. The main result is that tightness of a closed contact 3-manifold is preserved under Legendrian surgery.
Framework for AI customer support that protects privacy and reduces costs.
problem Privacy risks and compliance challenges in AI customer support.
method Zero-shot learning with large language models, real-time data anonymization, retrieval-augmented generation, robust post-processing.
result Reduces privacy risks and compliance costs while maintaining accuracy.
Privacy-preserving eye tracking framework using synthetic images.
problem Preserving sensitive personal information in eye tracking technology.
method Randomized encoding for privacy, Support Vector Regression model training on synthetic eye images.
result Framework achieves real-time gaze estimation with same accuracy as non-private version.
Let X be a data matrix of rank ρ, whose rows represent n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X. We pr…
Regular neighborhoods of singular submanifolds are isotopic to bundle morphisms.
problem Isotoping regular neighborhoods of singular submanifolds to bundle morphisms.
method Leaf preserving isotopy and homogeneity assumptions on foliations.
result Every leaf preserving diffeomorphism of a regular neighborhood is isotopic to a bundle morphism.
Additive noise protects privacy in releasing datasets for SVM classification.
problem Maintaining privacy in releasing datasets for SVM classification.
method Additive noise applied to obfuscate the dataset, optimizing privacy and utility measures.
result Optimal noise distribution ensures close classifier performance between original and obfuscated datasets, achieving local differential privacy.
Study proves a new formula for capillary hypersurfaces and shows a flow converging to a special shape.
problem Understanding the behavior of capillary hypersurfaces in hyperbolic space.
method Developed a volume-preserving flow starting from a star-shaped initial hypersurface and proved its long-time existence and convergence.
result The flow converges to a θ-totally umbilical cap, which is an energy minimizer for a given enclosed volume. In this paper we introduce a general notion of weak extension property for embeddings induced by a group actions. As an example, for the group H(M, m) of measure-preserving homeomorphisms of a noncompact manifold M, we deduce weak type extension theorems, and as an application we exhibit the local contractibility of th…
We consider the homogeneous and the non-homogeneous convex relaxations for combinatorial penalty functions defined on support sets. Our study identifies key differences in the tightness of the resulting relaxations through the notion of the lower combinatorial envelope of a set-function along with new necessary conditi…
A simple method for estimating PMF on large supports, preserving structure and suppressing noise.
problem Nonparametric estimation of multi-modal, heavy-tailed PMF on large discrete support.
method Data-dependent low-pass filtering on a line graph Laplacian.
result Smooth, multi-modal estimate of PMF that preserves coarse structure and suppresses noise.
The paper explores the transitivity of orbifold diffeomorphisms.
problem Understanding the transitivity of orbifold diffeomorphisms.
method Investigates the group of compactly supported diffeomorphisms of orbifolds.
result The group of orbifold diffeomorphisms is n-transitive. Transformers preserve support and can approximate any continuous map.
problem Understanding the mathematical properties of transformers.
method Characterizing maps between measures that can be represented as transformers and proving their properties.
result Transformers preserve support and have uniformly continuous Fréchet derivatives.
Proposes a new kernel technique for tensor data in SVM.
problem Handling tensorial data in machine learning.
method Kernelized support tensor train machine for image classification.
result Tensorizes the standard SVM on its input structure and kernel mapping scheme.
Modeling data as being sampled from a union of independent subspaces has been widely applied to a number of real world applications. However, dimensionality reduction approaches that theoretically preserve this independence assumption have not been well studied. Our key contribution is to show that 2K projection vect…
SMM preserves matrix data structure for SVM classification.
problem Preserving spatial correlations in matrix data for SVM.
method SMM uses spectral elastic net combining nuclear and Frobenius norms.
result SMM improves SVM performance on matrix data.
The paper proves positivity preservation and self-adjointness for Schrödinger operators on incomplete Riemannian manifolds.
problem Positivity preservation and self-adjointness for Schrödinger-type operators on incomplete Riemannian manifolds.
method Control of potential behavior near the Cauchy boundary, essential self-adjointness proof, core of smooth compactly supported functions.
result Positivity preservation and essential self-adjointness of Schrödinger operators on Lp functions on incomplete Riemannian manifolds. We prove a rigidity of the lightcone in Minkowski space. It is essentially the unique space endowed with a degenerate Riemannian metric, of lightlike type, and supporting an isometric non-proper action of a semi-simple group.
New set class preserves Fourier series terms for planar ovals, leading to isoperimetric inequalities.
problem Investigate geometric properties of kth Order Preserving Sets and ovals. method Introduce and analyze kth Order Preserving Sets and Midpoint Sets; study geometric properties and isoperimetric inequalities. result Established an isoperimetric-type inequality relating perimeter and area of ovals and their associated sets.
Develops a new tensor classification method for high-dimensional data.
problem Efficient learning algorithms exploiting tensorial structure in high-dimensional multi-way arrays.
method Tensor Train Multi-way Multi-level Kernel (TT-MMK) combining Canonical Polyadic decomposition, Dual Structure-preserving Support Vector Machine, and Tensor Train approximation.
result The TT-MMK method provides higher prediction accuracy and is more reliable computationally compared to other techniques.
New framework explains normalizing flows' power and limitations.
problem Understanding the expressive power and limitations of normalizing flows.
method Theoretical framework for well-conditioned coupling-based normalizing flows and volume-preserving flows.
result RealNVP is distributionally universal, but volume-preserving flows are not.
Smooth isotopy on cube saves energy with extra dimensions.
problem Constructing an isotopy with infinite kinetic energy.
method Explicit construction of isotopy on [0,1]n. result Existence of isotopy with infinite kinetic energy.
IVFS simplifies feature selection for high-dimensional data preservation.
problem Maintaining structure and pairwise distances in high-dimensional data.
method IVFS framework based on persistent diagrams from computational topology.
result IVFS well preserves pairwise distances and topological patterns of full data.
We study higher rank Cartan actions on compact manifolds preserving an ergodic measure with full support. In particular, we classify actions by Rk with k≥3 whose one-parameter groups act transitively as well as nondegenerate totally nonsymplectic $\Zk$-actions for k≥3.
Convolution has been playing a prominent role in various applications in science and engineering for many years. It is the most important operation in convolutional neural networks. There has been a recent growth of interests of research in generalizing convolutions on curved domains such as manifolds and graphs. Howev…
Continuous-time interpolation of volatility surfaces preserving mixtures and arbitrage-free.
problem Interpolation of volatility surfaces
method Constructing a mixture-preserving, arbitrage-free interpolation
result Lifts Brigo-Mercurio to time-varying weights with additive cost
Localized Multidirectional Correction improves non-refusal target-response behavior in foundation models.
problem Controlled post-training refusal suppression in routed MoE and hybrid-MoE foundation models.
method Introduce Localized Multidirectional Correction (LoMC), a support-gated intervention framework.
result Substantially improves non-refusal target-response behavior while maintaining general capability under a compact intervention footprint.
Let D2 be the open unit disc in the Euclidean plane and let G:=Diff(D2;area) be the group of smooth compactly supported area-preserving diffeomorphisms of D2. We investigate the properties of G endowed with the autonomous metric. In particular, we construct a bi-Lipschitz homomorphism Zk→G of a…
Spectral graph sparsification preserves geometry of GNN embeddings.
problem Maintaining geometric properties of graph neural network embeddings during sparsification.
method Proving spectral sparsification preserves squared pairwise distances, class means, and covariance structure in embedding space.
result Spectral sparsification preserves the geometry of learned embeddings in GNNs.
The group of volume preserving diffeomorphisms, the group of symplectomorphisms and the group of contactomorphisms constitute the classical groups of diffeomorphisms. The first homology groups of the compactly supported identity components of the first two groups have been computed by Thurston and Banyaga, respectively…
Bayesian model averaging under predictor redundancy
problem Reporting Bayesian model averaging posterior without changing the Bayesian target
method Using hard or soft regions of support space
result Region reports often give shorter and clearer summaries while preserving the main posterior information
A triangulated piecewise-linear minimal surface in Euclidean 3-space defined using a variational characterization is critical for area amongst all continuous piecewise-linear variations with compact support that preserve the simplicial structure. We explicitly construct examples of such surfaces that are embedded and a…
Novel strategy for federated learning with privacy-preserving predictors and nonvacuous generalization bounds.
problem Privacy-preserving federated learning with nonvacuous generalization bounds.
method Randomized predictors, PAC-Bayesian generalization bound, synchronous and heterogeneous/homogenous cases.
result Achieves comparable predictive performance to batch approach while preserving privacy.
LOFT separates subspace rotation and transformation for orthogonal fine-tuning.
problem Conflating subspace rotation and transformation in orthogonal fine-tuning.
method LOFT explicitly separates subspace rotation and transformation, using task-aware support selection.
result LOFT recovers principal-subspace orthogonal adaptation and improves efficiency-performance trade-off.
Suppose that Ω is the open region in Rn above a Lipschitz graph and let d denote the exterior derivative on Rn. We construct a convolution operator T which preserves support in $\bar{Ω$}, is smoothing of order 1 on the homogeneous function spaces, and is a potential map in the sense that …
This study improves graph coarsening methods by preserving graph spectrum and distances.
problem Solving large-scale graph problems by working on a smaller graph.
method Developed a geometric approach using Gromov--Wasserstein distance to minimize the difference between graph distances and their coarsened versions.
result Minimizing the difference between graph distances and their coarsened versions can be achieved using the weighted kernel K-means method. An algorithm simplifies optimization with nonnegative and orthogonal constraints.
problem Optimization problems with nonnegative and orthogonal constraints.
method Support-set algorithm exploiting structural sparsity.
result Global convergence to first-order stationary point with iteration complexity O(ε−2). Proposes a privacy-preserving sign selection method for distributed systems.
problem Sign selection in distributed differentially private settings.
method Iterative peeling of stability function combined with exponential mechanism.
result Recovery of support and signs with optimal signal-to-noise ratio.
A privacy-preserving algorithm for high-dimensional bandits.
problem High-dimensional stochastic contextual linear bandits with sparse parameters under privacy constraints.
method PrivateLASSO algorithm based on sparse hard-thresholding and episodic thresholding.
result Minimax private lower bounds and utility guarantees for PrivateLASSO.
A dessin is a 2-cell embedding of a connected bipartite graph into an orientable closed surface. An automorphism of a dessin is a permutation of the edges of the underlying graph which preserves the colouring of the vertices and extends to an orientation-preserving self-homeomorphism of the supporting surface. A dessin…
Generative model uses random weighted support points for interpretable data sampling.
problem Creating diverse and interpretable sample sets from large datasets efficiently.
method Random weighted support points from Dirichlet process and Bayesian bootstrap.
result High-quality and diverse outputs at lower computational cost.
In this paper we consider a free boundary problem in the 3-dimensional Lorentz-Minkowski space ł3 which deals spacelike surfaces whose mean curvature is a linear function of the time coordinate and the boundary moves in a given support plane. We study spacelike surfaces that project one-to-one into a strip of the su…
Bi-Lipschitz flows approximate a wide range of distributions.
problem Characterizing the expressivity of bi-Lipschitz normalizing flows.
method Linking score regularity to transport map bi-Lipschitzness via probability flow ODE.
result Gaussian pullbacks induced by bi-Lipschitz variance-preserving transport maps are L1-dense among all probability densities. This paper investigates gradient recovery schemes for data defined on discretized manifolds. The proposed method, parametric polynomial preserving recovery (PPPR), does not require the tangent spaces of the exact manifolds, and they have been assumed for some significant gradient recovery methods in the literature. Ano…
A new method classifies color images using quaternion algebra.
problem Classifying color images with preserved intrinsic relationships.
method LSQMM model with quaternion nuclear norm regularization and ADMM algorithm.
result LSQMM outperforms state-of-the-art methods in classification accuracy and efficiency.