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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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3467101134 · May 202619922001200920182026
48 results for roots density

Study on Jones polynomials and their roots in the unit circle and complex plane.

problem Understanding the roots of Jones polynomials for knots and links.
method Analyzing solutions of the equation JK(t)=1J_K(t)=1 for double-twist knots and links.
result The set of solutions to JKn(t)=1J_{K_n}(t)=1 is dense in the unit circle and complex plane.

Introduces new geometric framework for probability densities on manifolds.

problem Developing a new geometric framework for probability densities on manifolds.
method Introduces p\ell^p-information geometry and defines 2\ell^2-probability simplex with qq-root transform.
result Explicit solution of gradient flow and geodesic completeness of ee-connection.

We prove that the density of a topologically nontrivial, area-minimizing hypercone with an isolated singularity must be greater than the square root of 2. The Simons' cones show that this is the best possible constant. If one of the components of the complement of the cone has nontrivial kth homotopy group, we prove a …

2010-10-25abs ↗pdf ↗

The Volterra square-root process shows non-uniqueness of limiting distributions and regularity of its law.

problem Non-uniqueness of limiting distributions in the Volterra square-root process.
method Establishing existence of limiting distributions using integrability of the Volterra convolution kernel and exponential-affine transformation.
result The limiting distributions of the Volterra square-root process depend on the initial state and belong to weighted Besov spaces.

Improved MLMC method for robust and efficient probability and density estimation.

problem Stability and poor complexity of MLMC for low-regularity functionals.
method Numerical smoothing combined with MLMC for deterministic quadrature methods.
result Significant improvement in strong convergence and robustness of MLMC method.

Introduces a new geometric framework for probability distributions.

problem Developing a geometric framework for probability distributions.
method Introduces p\ell^p-information geometry and defines the 2\ell^2-probability simplex via the qq-root transform.
result Defines a noncanonical differentiable structure and qq-root map as an isometry.

We investigate the position of the Buchen-Kelly density in a family of entropy maximising densities which all match European call option prices for a given maturity observed in the market. Using the Legendre transform which links the entropy function and the cumulant generating function, we show that it is both the uni…

2011-02-01abs ↗pdf ↗

Efficient neural networks compute various differential operators cheaply.

problem Efficient computation of higher time complexity differential operators.
method Restricted neural network architectures with diagonal and hollow Jacobian matrices, allowing efficient extraction of dimension-wise derivatives.
result Demonstrated efficient computation of differential operators for various applications.

The MoN loss fails to accurately represent ground truth probability density functions in probabilistic trajectory prediction.

problem Improving the diversity of probabilistic trajectory predictions in autonomous driving and robot planning.
method Proof and validation of the MoN loss's inaccuracy and proposed solutions to correct it.
result The MoN loss approximates the square root of the ground truth probability density function, not the function itself.

OPAA estimates probability densities using functional analysis.

problem Estimating probability density functions efficiently and accurately.
method OPAA uses a parallelizable algorithm based on functional analysis to estimate probability distributions.
result OPAA provides an efficient method to estimate probability density functions and normalizing weights.

New properties established for SO(3) quantum representations, showing density and surjectivity.

problem Properties of SO(3) quantum representations of mapping class groups.
method Analyzing roots of unity and maximal ideals of Z[ζ_p] to establish properties.
result SO(3) quantum representations have dense image and are surjective modulo unramified maximal ideals.

We show that the topological modular functor from Witten-Chern-Simons theory is universal for quantum computation in the sense a quantum circuit computation can be efficiently approximated by an intertwining action of a braid on the functor's state space. A computational model based on Chern-Simons theory at a fifth ro…

2000-01-29abs ↗pdf ↗

Unified kernel density and empirical Bayes for unsupervised learning.

problem Addressing problems in unsupervised learning with geometric interpretation.
method Parametrizing the energy function with a neural network and combining kernel density estimation and empirical Bayes.
result Emergence of rich 'creative memories' as attractors in associative memory framework.

Bayesian neural networks are shown to be minimax and admissible under certain conditions.

problem Optimality of Bayesian neural networks in deep learning models.
method Analysis of decision rules induced by BNNs in the normal location model under quadratic loss.
result A hyperprior on the effective output variance yields a minimax and admissible decision rule.

We consider Feller mean-reverting square-root diffusion, which has been applied to model a wide variety of processes with linearly state-dependent diffusion, such as stochastic volatility and interest rates in finance, and neuronal and populations dynamics in natural sciences. We focus on the statistical mixing (or sup…

2009-10-08abs ↗pdf ↗

Kernel ridge regression imputation with consistent variance estimation for handling missing data.

problem Handling missing data in statistical analysis.
method Kernel ridge regression imputation combined with entropy method for variance estimation.
result Root-n consistency of the imputation estimator in a Sobolev space setting.

In this paper, we characterize locally dually flat generalized m-th root Finsler metrics. Then we find a condition under which a generalized m-th root metric is projectively related to a m-th root metric. Finally, we prove that if a generalized m-th root metric is conformal to a m-th root metric, then both of them redu…

2013-02-11abs ↗pdf ↗

New definition of patient-specific root causes of disease using counterfactuals.

problem Lack of rigorous mathematical formulation for automatic detection of root causes.
method Proposes a counterfactual definition matching clinical intuition and uses Shapley values for causal contribution scores.
result Adapts to disease prevalence, accounts for noisy labels, and admits fast computation.

A conservative drifting method improves generative modeling by using KDE gradients, proving convergence rates.

problem Improving generative modeling by addressing non-conservatism issues.
method Proposes a conservative drifting method using kernel density estimator gradients to address non-conservatism.
result Proves finite-particle convergence rates for the conservative method, providing explicit quadrature constants.

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.

Study differential properties of matrix square roots in specific cases.

problem Understanding matrix square roots in semi-simple, symmetric, and orthogonal cases.
method Analysis of differential and metric structures of real square roots of matrices under specific conditions.
result Differential properties of matrix square roots in semi-simple, symmetric, and orthogonal cases.

D. Margalit and S. Schleimer found examples of roots of the Dehn twist about a nonseparating curve in a closed orientable surface, that is, homeomorphisms whose nth power is isotopic to the Dehn twist. Our main theorem gives elementary number-theoretic conditions that describe the values of n for which an nth root exis…

2009-06-08abs ↗pdf ↗

Margalit and Schleimer observed that Dehn twists on orientable surfaces have nontrivial roots. We investigate the problem of roots of a Dehn twist t_c about a nonseparating circle c in the mapping class group M(N_g) of a nonorientable surface N_g of genus g. We explore the existence of roots and, following the work of …

2017-01-02abs ↗pdf ↗

New model identifies patient-specific disease root causes.

problem Identifying root causes of complex diseases varying between patients.
method Generalized Root Causal Inference (GRCI) algorithm for heteroscedastic noise model.
result GRCI accurately extracts patient-specific root causes.

A new method simulates square-root processes efficiently.

problem Simulating square-root processes accurately and efficiently.
method Simulate the integrated square-root process instead of the square-root process itself.
result High precision with low number of time steps, and exact limiting Inverse Gaussian distributions.

Enhanced ROOT-SGD optimizes stochastic optimization with diminishing stepsizes.

problem Improving statistical efficiency in stochastic optimization.
method Integrates a diminishing stepsize strategy into ROOT-SGD.
result Achieves optimal convergence rates with improved stability and precision.

The study of random positive 3-strand braids reveals patterns in the roots of their Alexander polynomials.

problem Investigating the roots of Alexander polynomials of random positive 3-strand braids.
method Experimental data analysis, conjectures refinement, and proof of results using tools like the signature function of links and Lyapunov exponent of the Burau representation.
result Generically, at least 69% of the roots of Alexander polynomials are on the unit circle, with a large root-free region near the origin.

GT is a new method for denoising and enhancing datasets using Gaussian density estimates.

problem Improving latent structures in datasets.
method GT is an iterative method that generates a new distance function by computing the 2\ell^2-Wasserstein distance between Gaussian density estimates.
result GT is stable under perturbations and asymptotically ellipsoidal neighborhoods in the continuous case.

In this paper, we prove that every m-th root metric with isotropic mean Berwald curvature reduces to a weakly Berwald metric. Then we show that an m-th root metric with isotropic mean Landsberg curvature is a weakly Landsberg metric. We find necessary and sufficient condition under which conformal ββ-change of an m-th…

2017-06-24abs ↗pdf ↗

Automorphisms of Lie algebras and their root systems are fully lifted.

problem Understanding automorphisms of real semisimple Lie algebras and their root systems.
method Proving every automorphism of the restricted root system can be lifted to a Lie algebra automorphism.
result Automorphisms of restricted root systems can be fully lifted to Lie algebras.

Margalit and Schleimer constructed nontrivial roots of the Dehn twist about a nonseparating curve. We prove that the conjugacy classes of roots of the Dehn twist about a nonseparating curve correspond to the conjugacy classes of periodic maps with certain conditions. Futhermore, we give data set which determine the con…

2009-11-26abs ↗pdf ↗

In this paper, we find a condition under which a Finsler space with Kropina change of mth-root metric is projectively related to a mth-root metric and also we find a condition under which this Kropina transformed mth-root metric is locally dually flat. Moreover we find the condition for its Projective flatness.

2017-12-23abs ↗pdf ↗