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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.

168,657 papers · 148 categories

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11233445 · Jun 202019922001200920172026
48 results for interval-valued cores

Paper introduces a new method for classifying interval-valued time series.

problem Classification of interval-valued time series.
method Extends point-valued time series imaging methods to interval-valued scenarios using DKD_K-distance and employs deep learning for classification.
result Proposed method achieves superior classification performance compared to existing methods.

Study optimality conditions for interval-valued optimization problems on Riemannian manifolds.

problem Optimizing interval-valued functions on Riemannian manifolds under a total order relation.
method Generalized Hukuhara directional differentiability to derive KKT-type optimality conditions.
result Derives optimality conditions for interval-valued optimization problems on Riemannian manifolds.

New methods for ordinal classification of interval-valued data and functional data.

problem Ordinal classification of interval-valued data and functional data.
method Six ordinal classifiers are proposed, including parametric, binary decomposition, logistic regression, distance-based, k-nearest-neighbor, kernel PCA, and random forest methods.
result Considering ordering and interval-valued information improves the accuracy of ordinal classification.

Study KKT conditions for multi-objective optimization on Hadamard manifolds.

problem Optimizing multi-objective interval-valued functions on Hadamard manifolds.
method Developed KKT conditions for Pareto optimal solutions under different ordering and convexity notions.
result Results are more general than on Euclidean spaces.

Paper discusses directional differentiability of interval-valued functions on Riemannian manifolds.

problem Equivalence of directional differentiability of interval-valued functions and their components.
method Analyzes directional differentiability of interval-valued functions on Riemannian manifolds.
result Directional differentiability of interval-valued functions is not equivalent to the directional differentiability of their components.

New model uses interval-valued CVaR for better risk assessment in finance.

problem Measuring tail risk in rapidly changing financial markets.
method Employing random intervals to describe asset returns and using ICVaR as a risk measure.
result Optimal portfolio selection models show better risk assessment in real data.

Extends Fisher's Discriminant Analysis for interval-valued data.

problem Classifying entities represented by intervals and histograms.
method Adapts Fisher's Discriminant Analysis using Moore's interval arithmetic and Mallows' distance.
result Discriminant directions for interval-valued data are numerically maximized.

Proposes a method for forecasting large-scale interval-valued time series.

problem Modeling and forecasting large-scale interval-valued time series.
method Feature extraction procedure involving auto-segmentation, clustering, and precision matrix estimation.
result The method enhances forecasting performance for large-scale interval-valued time series.

This paper provides estimation and inference methods for an identified set's boundary (i.e., support function) where the selection among a very large number of covariates is based on modern regularized tools. I characterize the boundary using a semiparametric moment equation. Combining Neyman-orthogonality and sample s…

2017-12-28abs ↗pdf ↗

Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine. The goal is to learn a real-valued prediction function, and the training output labels indicate an interval of possible values. Whereas most existing algorithms for this task are…

2017-10-11abs ↗pdf ↗

Proposes a new matrix factorization model for interval-valued matrices.

problem Matrix factorization for matrices with entries in a given interval.
method Bounded simplex-structured matrix factorization (BSSMF) with fast algorithm for missing data.
result BSSMF provides a unique decomposition under certain conditions.

This paper establishes an equivalence between transitive double Lie algebroids and core diagrams.

problem Understanding and characterizing transitive double Lie algebroids.
method Using core diagrams and equivalence of transitive core diagrams with transitive double Lie groupoids.
result Transitive double Lie algebroids are completely determined by their core diagrams.

In various application areas, networked data is collected by measuring interactions involving some specific set of core nodes. This results in a network dataset containing the core nodes along with a potentially much larger set of fringe nodes that all have at least one interaction with a core node. In many settings, t…

2019-05-14abs ↗pdf ↗

AL0\ell_0CORE tensor decomposition reduces computational cost for sparse count data.

problem Efficiently decompose sparse count data matrices.
method Probabilistic Tucker decomposition with 0\ell_0-norm constraint.
result AL0\ell_0CORE achieves similar results to full Tucker decomposition at a fraction of the cost.

We introduce the notion of the visual core of a hyperbolic 3-manifold N and explore its basic properties. The visual core can be thought of as a harmonic analysis analogue of the convex core. We investigate circumstances in which the visual core of a cover N' of N embeds under the covering map from N' to N. We apply th…

1999-03-12abs ↗pdf ↗

Paper introduces a core-periphery model for identifying informative network structures.

problem Noise and bias in non-informative periphery structures obscure the informative core in complex networks.
method Spectral algorithms for core identification as a preprocessing step for network analysis.
result The proposed method outperforms traditional core-periphery methods in various downstream tasks.

Estimates covariance matrices for matrix-variate data via core covariance geometry.

problem Estimating covariance matrices for matrix-variate data with partial isotropy.
method Fixed-rank core covariance geometry, partial-isotropy rank-r core shrinkage estimator.
result The geometry of the space of rank-r cores is a smooth manifold.

Probabilistic programming aids in automatically dating ice cores, reducing manual error and uncertainty.

problem Automatically dating ice cores with high accuracy and capturing uncertainty.
method Probabilistic models and probabilistic programming for automatic inference.
result Demonstrated the use of probabilistic programming for ice core dating, showcasing its benefits and limitations.

The term "CoRE kernel" stands for correlation-resemblance kernel. In many applications (e.g., vision), the data are often high-dimensional, sparse, and non-binary. We propose two types of (nonlinear) CoRE kernels for non-binary sparse data and demonstrate the effectiveness of the new kernels through a classification ex…

2014-04-24abs ↗pdf ↗

We obtain upper and lower bounds on the difference between the renormalized volume and the volume of the convex core of a convex cocompact hyperbolic 3-manifold which depend on the injectivity radius of the boundary of the universal cover of the convex core and the Euler characteristic of the boundary of the convex cor…

2015-02-17abs ↗pdf ↗

New algorithm detects cores in graphs with community structure, improving vertex selection for better clustering.

problem Understanding and detecting core-periphery structures in graphs with community structure.
method Introduces relative centrality to detect cores in graphs with community and core-periphery structures.
result Relative centrality solves bias issues in core detection, leading to better vertex selection and improved clustering performance.

Deep learning predicts nuclear equation of state from rotating core collapse GW signals.

problem Classifying the nuclear equation of state from rotating core collapse gravitational wave signals.
method Employed deep convolutional neural networks to classify visual and temporal patterns in GW signals.
result Up to 97% correct classifications of nuclear equation of state in the test set.

This study examines cores within superclusters, highlighting their transitional nature and dynamical state.

problem Understanding the morphology and dynamical properties of cores within superclusters.
method Projected and radial velocity distributions of galaxies, morphological analysis, entropy and mass estimates.
result Cores are transitional structures that evolve towards virialisation but remain gravitationally bound.

The study allows for connected sums in manifolds with positive intermediate Ricci curvature.

problem Performing connected sums in manifolds with positive intermediate Ricci curvature.
method Introducing and utilizing kk-core metrics to show the possibility of connected sums.
result Connected sums are possible under certain conditions involving kk-core metrics.

Study signatures of torus links and their cores using Neumann's equivariant signatures and Hirzebruch's formula.

problem Computing signatures of torus links and their cores.
method Use Neumann's equivariant signatures and rewrite Hirzebruch's formula for torus links (without cores) in terms of integral points in a parallelogram.
result Rewritten Hirzebruch's formula for torus links with cores using integral points in a parallelogram.

Core groups are link invariants defined by arc or region presentations.

problem Defining link invariants using different presentations of arcs and regions.
method Introducing core groups as link invariants defined by presentations involving arcs or regions, and extending these to virtual link diagrams.
result Properties of core groups and their extensions to virtual link diagrams are discussed.

Study online learning with set-valued feedback, showing differences between deterministic and randomized approaches.

problem Online learning with set-valued feedback, where labels are sets rather than single labels.
method Introduced new combinatorial dimensions (Set Littlestone and Measure Shattering) to characterize learnability.
result Characterized deterministic and randomized online learnability, and established bounds for various learning settings.

An imprecise SHAP method explains class probabilities with limited data.

problem Explaining class probabilities with limited training data.
method New approach for computing feature marginal contributions and general approach to interval-valued Shapley values.
result The imprecise SHAP method improves explanation of class probabilities.

We show that in any triangulation of a solid torus, there is a pre-core curve that lies in the 2-skeleton and that intersects the interior of each face in at most 10 straight arcs. By definition, a pre-core curve is a simple closed curve that becomes a core curve when a collar is attached to the boundary of the solid t…

2011-06-15abs ↗pdf ↗

The study shows portfolios based on core-periphery stock structure outperform traditional strategies.

problem Optimizing stock portfolios using mesoscale structures.
method Constructing portfolios based on the core-periphery profile of stocks from Pearson correlations.
result Portfolios based on the core-periphery profile of stocks outperform traditional strategies.

The main result is that an s-cobordism (topological or smooth) of 4-manifolds has a product structure outside a ``core'' sub s-cobordism. These cores are arranged to have quite a bit of structure, for example they are smooth and abstractly (forgetting boundary structure) diffeomorphic to a standard neighborhood of a 1-…

2004-03-31abs ↗pdf ↗

Totally geodesic submanifolds in convex cores are properly immersed and have finite volume.

problem Characterizing totally geodesic submanifolds in geometrically finite manifolds.
method Analysis of totally geodesic submanifolds in the convex core of geometrically finite rank-one locally symmetric manifolds.
result Every maximal totally geodesic submanifold of dimension at least two in the convex core is properly immersed and has finite volume, and only finitely many such submanifolds can occur.