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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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3672108144 · Jun 202019922001200920182026
48 results for Voronoi constraints

In this thesis we study sets of points in the plane and their Voronoi diagrams, in particular when the points coincide. We bring together two ways of studying point sets that have received a lot of attention in recent years: Voronoi diagrams and compactifications of configuration spaces. We study moving and colliding p…

2002-10-22abs ↗pdf ↗

A hex sphere is a singular Euclidean sphere with four cones points whose cone angles are (integer) multiples of 2*pi/3 but less than 2*pi. Given a hex sphere M, we consider its Voronoi decomposition centered at the two cone points with greatest cone angles. In this paper we use elementary Euclidean geometry to describe…

2010-10-29abs ↗pdf ↗

Proof of existence and uniqueness of weighted Voronoi-Delaunay on polyhedral surfaces.

problem Existence and uniqueness of weighted Voronoi-Delaunay on polyhedral surfaces.
method Construct an isotopic map instead of edge-flipping algorithm, generalizing Dyer et al's method.
result Strict proof of existence and uniqueness of weighted Voronoi-Delaunay on polyhedral surfaces.

The study uses persistent homology to determine when Voronoi interpolation should stop.

problem Interpolating complex topological data sets accurately.
method Persistent homology is applied to the Voronoi tessellation to detect changes in the data's topology.
result The method effectively identifies when the interpolation has captured the data's topology changes.

Constructs an explicit cycle in arithmetic group cohomology.

problem Cohomology of SLn(Z)_n(\mathbb{Z}) at virtual cohomological dimension.
method Geometric rigidity of Voronoi tessellations and abstract framework for polyhedral tessellations.
result Explicit canonical cycle in top-dimensional homology of Voronoi complex.

Deviance Voronoi residuals improve earthquake insurance risk assessment.

problem Assessing earthquake insurance risk using spatio-temporal point process models.
method Extended Voronoi residuals and created simulation-based approach.
result Proposed formula for country-wide minimum capital test.

Improved taxi demand-supply forecasts using graph-based LSTM.

problem Accurate taxi demand-supply forecasting with complex spatial and temporal patterns.
method Investigated impact of spatial partitioning techniques (Voronoi vs. Geohash) on LSTM network performance.
result GraphLSTM offers competitive performance against ConvLSTM, at lower complexity, across real-world data sets.

Proposes a new adversarial model to avoid accuracy vs. adversarial accuracy tradeoff.

problem Inherent tradeoff between accuracy and adversarial accuracy in existing adversarial robustness definitions.
method Introduces Voronoi-epsilon adversary that balances perturbation constraints.
result Voronoi-epsilon adversary avoids accuracy vs. adversarial accuracy tradeoff even with large εε.

Algorithm finds adversarial examples for k-NN classifiers using Voronoi diagrams.

problem Ensuring robustness of k-NN classifiers against adversarial attacks.
method Geometric approach expanding outwards from input points to find minimum-norm adversarial examples.
result Our method outperforms existing approaches on various datasets.

The paper explores clustering methods using Bregman divergences.

problem Developing efficient clustering algorithms for complex data.
method Investigates fixed rate quantization and Voronoi diagrams in Riemannian metric spaces induced by separable Bregman divergences.
result Experimental results show improved performance of clustering algorithms using these metrics.

A Riemannian symmetric space is a Riemannian manifold in which it is possible to reflect all geodesics through a point by an isometry of the space. On such spaces, we introduce the notion of a distributional lattice, generalizing the notion of lattice. Distributional lattices exist in any Riemannian symmetric space: th…

2017-07-02abs ↗pdf ↗

Given a set S of n points in general position, we consider all k-th order Voronoi diagrams on S, for k=1,...,n, simultaneously. We deduce symmetry relations for the number of faces, number of vertices and number of circles of certain orders. These symmetry relations are independent of the position of the sites in S. As…

1999-05-04abs ↗pdf ↗

Bayesian model captures mean and variance of response variables.

problem Complex, predictor-dependent relationships and heteroscedastic patterns in data.
method Sum-of-tessellations for mean, product-of-tessellations for variance.
result Model captures nuanced variance structures and provides reliable predictive uncertainty.

Quantized Variational Inference improves ELBO optimization with fast convergence.

problem Maximizing Evidence Lower Bound (ELBO) for variational inference.
method Optimal Voronoi Tesselation for variance-free gradients, Richardson extrapolation for asymptotic improvement.
result Quantized Variational Inference leads to fast convergence with comparable computational cost.

The paper proves actions of lattices in higher rank groups have cost one.

problem Fixed price question for higher rank semisimple Lie groups.
method Low intensity Poisson point processes and geometry of Voronoi tessellations.
result Proves all probability measure preserving actions of lattices in higher rank groups have cost one.

New cell structure on O(3)/O(1)3O(3)/O(1)^3 derived from injectivity radius computation.

problem Constructing equivariant cell structures on flag manifolds.
method Injectivity radius computation and Dirichlet-Voronoi domains.
result New S3\mathfrak{S}_3-equivariant cell structure on O(3)/O(1)3O(3)/O(1)^3.

Proposes Dirichlet Simplex Nest for probabilistic modeling of various data types.

problem Modeling and inference for diverse data types.
method Probabilistic models based on Dirichlet distribution and Voronoi tessellation, with fast and accurate inference algorithms exploiting convex geometry and simplicial structure.
result Inference algorithms achieve consistency and strong error bounds across various settings and data distributions.

The paper analyzes how companies' investments before crises affect their performance after crises.

problem Understanding how companies' investments before financial crises impact their performance afterward.
method Cluster analysis using Voronoi tessellation with statistical outliers identified.
result Positive investments before crises are associated with better performance after crises.

Study spider mechanism configuration spaces using squared distance function.

problem Understand configuration spaces of spider mechanisms.
method Use Morse theory of squared distance function from body to fixed point.
result List and describe critical manifolds of squared distance function as products of polygon spaces.

A new framework detects anomalies in structured data.

problem Detecting anomalies in samples not conforming to low-dimensional manifolds.
method Preference Isolation Forest (PIF) framework combining adaptive isolation methods and preference embedding.
result Anomalies identified as isolated points in a high-dimensional preference space.

For a given lattice, we establish an equivalence involving a closed zone of the corresponding Voronoi polytope, a lamina hyperplane of the corresponding Delaunay partition and a quadratic form of rank 1 being an extreme ray of the corresponding L-type domain.

2000-04-01abs ↗pdf ↗

Study compares tessellation strategies for taxi demand-supply forecasting models.

problem Improving taxi demand-supply forecasting using neural networks.
method Compared Voronoi tessellation and Geohash tessellation for LSTM models.
result Variable-sized polygon tessellation yields superior performance in LSTM models.

A new method learns quantization boundaries in continuous space using tessellation.

problem Mapping between discrete and continuous distributions is difficult.
method Constructs normalizing flows on convex polytopes with exact likelihood evaluations.
result Improves likelihood evaluation and quantization learning across various data modalities.

Two methods using low-discrepancy points improve data compression for neural networks.

problem Efficiently compress large datasets for neural network training.
method Two methods based on low-discrepancy points: digital nets with averaging and clustering.
result Second method outperforms supercompress in compression error and neural network accuracy.

rMCL improves on MCL by preserving diversity in predictions for regression problems.

problem Multimodal density estimation in regression settings with multiple targets.
method rMCL uses a learned scoring scheme based on Voronoi tessellations to maintain diversity among predictions.
result rMCL outperforms existing MCL variants in sound source localization tasks.

Using an idea of Voronoi in the geometric theory of positive definite quadratic forms, we give a transparent proof of John's characterization of the unique ellipsoid of maximum volume contained in a convex body. The same idea applies to the 'hard part' of a generalization of John's theorem and shows the difficulties of…

2012-07-31abs ↗pdf ↗

The Delaunay tessellation of a locally finite subset of hyperbolic space is constructed using convex hulls in Euclidean space of one higher dimension. For finite and lattice-invariant sets it is proven to be a polyhedral decomposition, and versions (necessarily modified from the Euclidean setting) of the empty circumsp…

2013-08-22abs ↗pdf ↗

Study approximates probability measures using structured classes of functions.

problem Approximating probability measures in Wasserstein-pp distance.
method Structured classes of approximators for functions in Lp(Ω)L_p(Ω), transferring to measures in Wp(Ω)W_p(Ω).
result Linear rate approximation for measures with densities bounded away from zero.

Standard bubbles and partitions are stable in various model spaces.

problem Stability of standard bubbles and partitions in different model spaces.
method New conjugated Brascamp-Lieb inequality and conformally flattening boundary potential.
result Stability of standard bubbles and partitions in Rn\mathbb{R}^n, Sn\mathbb{S}^n, and Hn\mathbb{H}^n.

New algorithm for computing Veech groups from translation surfaces.

problem Computing Veech groups for translation surfaces.
method Infinite translation surface containing copies of all surfaces in a stratum; associated affine automorphisms of the infinite surface map marked segments to other pairs of segments.
result Explicit hyperbolic ball condition for Fuchsian groups to agree with their Dirichlet domain.

Topology-GS improves 3D GS for better structural and feature integrity.

problem Compromised pixel-level and feature-level integrity in 3D GS.
method Incorporates Local Persistent Voronoi Interpolation (LPVI) and PersLoss based on persistent homology.
result Topology-GS outperforms existing methods in PSNR, SSIM, and LPIPS metrics.

Analyzes convergence rates for Gaussian-gated MoE model.

problem Theoretical understanding of Gaussian-gated MoE model is incomplete.
method Maximum likelihood estimation with novel Voronoi loss functions.
result MLE has distinct behaviors under different settings of Gaussian gating function parameters.

New matching estimators correct bias in multivariate settings without smoothing parameters.

problem Bias in nearest-neighbor and matching estimators in multiple dimensions.
method Polynomial least squares fits on Voronoi tessellations.
result Novel estimators converge at n\sqrt{n} rate under mild smoothness assumptions.

We prove an optimal systolic inequality for CAT(0) metrics on a genus~2 surface. We use a Voronoi cell technique, introduced by C.~Bavard in the hyperbolic context. The equality is saturated by a flat singular metric in the conformal class defined by the smooth completion of the curve y^2=x^5-x. Thus, among all CAT(0) …

2005-01-02abs ↗pdf ↗

New algorithm predicts geolocation of fungi samples with high accuracy.

problem Identifying the origin of biological material at crime scenes.
method Ensemble of deep neural network classifiers trained on Voronoi partitions.
result More than half of geolocation errors under 100 kilometers for continental analysis and nearly 90% accuracy for global analysis.