Formula for interleaving distance of rectangle persistence modules.
problem Calculating distances between rectangle persistence modules.
method Formulas based on rectangle geometry, extended to decomposable modules.
result Closed formulas for interleaving and bottleneck distances.
Calculates Gordian distances using algebraic methods.
problem Determining when Alexander polynomials can't be realized by matrices with Gordian distance one.
method Using Blanchfield pairings and quadratic equations with integer solutions.
result Shows that certain Alexander polynomials cannot be realized by matrices with Gordian distance one.
Paper calculates Gromov-Hausdorff distance between simplexes and 2-distance spaces.
problem Calculating Gromov-Hausdorff distance between simplexes and 2-distance spaces.
method Formulas derived for clique covering number and chromatic number of graphs.
result Complete solution to generalized Borsuk problem for 2-distance spaces.
Negative-curvature surfaces uniquely define boundary distances.
problem Defining distances on curved surfaces at infinity.
method Renormalized distance calculation for negatively-curved surfaces.
result Negative-curvature surfaces are boundary distance rigid.
Paper introduces a new Brenier approach for more accurate Wasserstein distance calculation.
problem Estimating discrepancy between two data distributions, especially with quasi-discrete and discrete measures.
method Introduces a new Brenier approach to calculate a more accurate Wasserstein distance between two discrete distributions.
result Successfully avoids the limitations of the Sinkhorn distance, such as approximation and divide by zero issues.
Paper calculates robust XVA for derivatives under distributional uncertainty using Wasserstein distance.
problem Distributional uncertainty in over-the-counter derivatives pricing.
method Wasserstein distance as ambiguity measure, dual formulations derived using Lagrangian duality.
result Characterization and quantification of wrong-way counterparty credit and funding risks.
The paper introduces a statistical distance matrix for better feature representation and clustering.
problem Lack of detailed distance representation between feature elements.
method Extended traditional statistical distance to a matrix form (statistical distance matrix) and applied hierarchical clustering.
result The statistical distance matrix with clustering (Information Mandala) provides clearer and geometrically arranged feature representations.
New method estimates geodesic distances using spherelets.
problem Accurately estimating geodesic distances on unknown manifolds.
method Uses spherelets to locally approximate unknown subspaces and estimate geodesic distances.
result Lower error for many manifolds, validated through simulations and real data.
The paper examines how distortion principles affect insurance pricing under risk aversion and ambiguity.
problem Understanding how risk aversion and ambiguity impact insurance pricing.
method Investigates sensitivity of distortion functionals to risk aversion and ambiguity, using Wasserstein distance.
result Identifies worst-case distributions and methods to identify distortion densities.
Paper calculates robust FVA for OTC derivatives under distributional uncertainty.
problem Distributional uncertainty in over the counter derivatives valuation.
method Wasserstein distance as ambiguity measure, dual formulation of robust FVA optimization.
result Additional FVA charge due to distributional uncertainty measured under various configurations.
Upper bound for Hausdorff distance between hyperbolic space and its medianization.
problem Calculating the Hausdorff distance between hyperbolic space and its medianization.
method Using de Sitter space to model finite-dimensional hyperbolic space and its medianization, calculating the Hausdorff distance.
result An upper bound for the Hausdorff distance between hyperbolic space and its medianization is calculated.
We calculate Euclidean distance degrees for common manifold optimization types.
problem Optimizing on manifold structures.
method Closed-form expressions for stationary points of Euclidean distance function.
result Closed-form expressions for all stationary points on manifold optimization.
We calculate the volumes of the hyperbolic twist knot cone-manifolds using the Schläfli formula. Even though general ideas for calculating the volumes of cone-manifolds are around, since there is no concrete calculation written, we present here the concrete calculations. We express the length of the singular locus in t…
Here we study geodesics connecting two given points on odd-dimensional spheres respecting the Hopf fibration. This geodesic boundary value problem is completely solved in the case of 3-dimensional sphere and some partial results are obtained in the general case. The Carnot-Carathéodory distance is calculated. We also p…
A new algorithm reduces time complexity for binary time series classification.
problem High time complexity of ensemble shapelet transform limits its application.
method Introduces short isometric shapelet transform with two strategies: fixed shapelet length and single linear classifier.
result Demonstrates superior performance and reduced time complexity.
Large scale agglomerative clustering is hindered by computational burdens. We propose a novel scheme where exact inter-instance distance calculation is replaced by the Hamming distance between Kernelized Locality-Sensitive Hashing (KLSH) hashed values. This results in a method that drastically decreases computation tim…
Study calculates site-specific Gordian distances between graph embeddings.
problem Determining the minimal number of crossing changes between graph embeddings.
method Covering space theory for proofs.
result Site-specific Gordian distances between Milnor links and trivial links are determined.
The setting for this brief paper is R^3. Distance between two spheres is understood as distance delta between spherical centers. For instance, a Reuleaux tetrahedron T is the intersection of four unit balls satisfying delta=1 pairwise. Volume and surface area of T are already well-known; our humble contribution is to c…
The economy globalization measure problem is discussed. Four macroeconomic indices of twenty among the "richest" countries are examined. Four types of "distances" are calculated.Two types of networks are next constructed for each distance measure definition. It is shown that the globalization process can be best charac…
We review and implement an efficient method for calculating entropy-regularized Wasserstein loss.
problem Efficiently calculating entropy-regularized Wasserstein loss.
method Batched Sinkhorn iterations for PyTorch implementation.
result Improved computational efficiency in calculating Wasserstein loss.
Defines pants distance for knotted surfaces in 4-manifolds.
problem Complexity of knotted surfaces in 4-manifolds.
method Defining pants distance and proving standard form conditions.
result Determines conditions for standard form of knotted surfaces.
CADM proposes a cluster-specific distance metric for categorical data clustering.
problem Inadequate distance metrics for categorical data, especially varying within clusters.
method Cluster-customized adaptive distance metric for categorical data.
result Achieved competitive performance in categorical data clustering.
New method calculates Ricci curvature from distances between weighted volumes.
problem Calculating Ricci curvature for weighted Riemannian manifolds.
method Asymptotic retrieval of generalized Ricci tensor from scaled metric derivatives of Wasserstein 1-distances.
result Limiting coarse curvature of random graphs converges to generalized Ricci tensor.
The paper uses distance covariance to improve fairness in machine learning models.
problem Improving fairness in machine learning models.
method Using conditional and distance covariance statistics to assess independence and add a penalty for fairness.
result The method effectively reduces the fairness gap in machine learning models.
We calculate the Chern-Simons invariants of the twist knot orbifolds using the Schläfli formula for the generalized Chern-Simons function on the family of the twist knot cone-manifold structures. Following the general instruction of Hilden, Lozano, and Montesinos-Amilibia, we here present the concrete formulae and calc…
The study bounds distances in simplicial complexes and defines new invariants for 3-manifolds and handlebody-knots.
problem Estimating distances in simplicial complexes associated with low-dimensional manifolds.
method Obtained bounds on distances in simplicial complexes using topological conditions on vertices and curve complexes. Defined new invariants for 3-manifolds and handlebody-knots using splitting distances.
result Splitting distances in simplicial complexes are bounded from below under stabilizations, leading to converging invariants.
New neural method calculates EMD for particle physics data.
problem Metric for particle collider events based on Wasserstein metric.
method Neural network architecture estimating EMD using Kantorovich-Rubinstein duality.
result Differentiable way to calculate EMD for geometric fitting.
The paper constructs Gromov-Hausdorff metrics for Lorentzian spaces and calculates dimensions.
problem Understanding the geometry and dimensions of Lorentzian spaces.
method Construction of Gromov-Hausdorff metrics, calculation of dimensions, and analysis of Lorentzian spaces.
result Dushnik-Miller dimension of Minkowski spaces is countably infinite.
A new smooth edit distance for easier optimization in machine learning.
problem Hard optimization of edit distance for variable-length sequences.
method Soft edit distance (SED) as a differentiable approximation.
result SED can be optimized with gradient methods and used for clustering.
New Bregman chord divergences simplify distance selection in machine learning.
problem Selecting appropriate distances for machine learning tasks.
method Extend Bregman divergences with two scalar parameters.
result Simplified distance selection with asymptotic generalization of Bregman divergences.
A new metric compares true and learned causal graphs considering data and graph structure.
problem Comparing true and learned causal graphs accurately.
method Continuous Structural Intervention Distance (CSID) using conditional mean embeddings and maximum mean discrepancy.
result Validated the CSID with synthetic data, showing its effectiveness in comparing causal graphs.
Study of 2D Lorentzian anti-de Sitter plane using geometric control theory.
problem Understanding extremal trajectories and reachable set on anti-de Sitter plane.
method Geometric control theory and differential geometry.
result Construction of optimal synthesis and description of Lorentzian distance.
In data science, it is often required to estimate dependencies between different data sources. These dependencies are typically calculated using Pearson's correlation, distance correlation, and/or mutual information. However, none of these measures satisfy all the Granger's axioms for an "ideal measure". One such ideal…
The study examines Teichmüller distances in lattices and punctured tori.
problem Distribution of Teichmüller distances in moduli spaces.
method Uniform distribution of lattices, calculation of Teichmüller distances.
result Identifies distribution of distances in Teichmüller space.
WEGL embeds graphs in a vector space for faster machine learning.
problem Efficiently embedding graphs for machine learning tasks.
method Wasserstein distance for node embedding similarity, Monge maps for graph representation.
result State-of-the-art classification performance with superior computational efficiency.
Researchers measure distances between quantum states to speed up machine learning.
problem Calculating distances between quantum states for machine learning is complex.
method Three-step method using many-particle interference to measure Hilbert-Schmidt distance.
result The method reduces complexity in calculating Euclidean distances between quantum states.
New method trains prototypical few-shot models on single class.
problem Train few-shot models on single class with limited examples.
method Introduce 'null class' and use batch normalization; propose Gaussian layer for distance calculation.
result Achieved high accuracy on test sets (Omniglot: 98%, MiniImageNet: 80%).
The paper calculates expected distances on partially oriented flag manifolds.
problem Understanding distances on partially oriented flag manifolds.
method Computing expected distances on low-dimensional examples.
result Computed expected distances on partially oriented flag manifolds.
The original k-means clustering method works only if the exact vectors representing the data points are known. Therefore calculating the distances from the centroids needs vector operations, since the average of abstract data points is undefined. Existing algorithms can be extended for those cases when the sole input i…
A new metric HCP distance for comparing distributions.
problem Comparing high-dimensional probability distributions efficiently.
method Hilbert curve projection to low-dimensional coupling, followed by transport distance calculation.
result HCP distance is a proper metric for probability measures with bounded supports.
Paper proposes Gini distance statistics for estimating feature-label dependence.
problem Identifying statistical dependence between features and categorical labels.
method Generalized Gini distance in RKHS for feature-label dependence estimation.
result Gini distance statistics converge faster and have tighter error bounds than distance covariance.
Paper uses Gaussian mixture models and Wasserstein distance for schema matching.
problem Schema matching between different datasets.
method Gaussian mixture models and Wasserstein distance for comparison.
result Derives an approximation for Wasserstein distance between Gaussian mixture models.
In this paper the regularity of optimal transportation potentials defined on round spheres is investigated. Specifically, this research generalises the calculations done by Loeper, where he showed that the strong (A3) condition of Trudinger and Wang is satisfied on the round sphere, when the cost-function is the geodes…
SWRLDA improves LDA for multi-class classification with edge classes.
problem LDA's vulnerability to edge classes causing biased mean and large distances.
method Self-weighted robust LDA with l21-norm distance criterion.
result SWRLDA outperforms other methods on synthetic and real-world datasets.
The paper defines and calculates stabilization distances between surfaces in 4-manifolds.
problem Calculating the minimal number of 1-handle stabilizations for surfaces to become isotopic.
method Using homology of cyclic covers and metabelian twisted homology.
result For every nonnegative integer m, there exist pairs of surfaces with a specific stabilization distance.
We present a simple, yet effective, approach to Semi-Supervised Learning. Our approach is based on estimating density-based distances (DBD) using a shortest path calculation on a graph. These Graph-DBD estimates can then be used in any distance-based supervised learning method, such as Nearest Neighbor methods and SVMs…
MultiDendrograms is a Java-written application that computes agglomerative hierarchical clusterings of data. Starting from a distances (or weights) matrix, MultiDendrograms is able to calculate its dendrograms using the most common agglomerative hierarchical clustering methods. The application implements a variable-gro…
We calculate the bridge distance for m-bridge knots/links in the 3-sphere with sufficiently complicated 2m-plat projections. In particular we show that if the underlying braid of the plat has n−1 rows of twists and all its exponents have absolute value greater than or equal to three then the distance of the b…