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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.

169,291 papers · 148 categories

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48 results for mass parameters

The paper studies nonlinear mass concepts in 3-manifolds with nonnegative scalar curvature.

problem Nonlinear isocapacitary mass in 3-manifolds with nonnegative scalar curvature.
method Derives positive mass theorems and shows mass coincides with ADM mass under mild conditions.
result Nonlinear masses coincide with ADM mass and prove the Penrose inequality.

The study uses statistical methods to analyze nuclear mass models.

problem Understanding the information content of nuclear masses from models.
method Bayesian calibration, Bayesian model averaging, chi-square correlation analysis, principal component analysis.
result A dramatic parameter reduction can be achieved in both 4-parameter and 14-parameter models.

Paper defines Bartnik mass for hyperbolic extensions and proves staticity.

problem Defining and proving staticity of asymptotically hyperbolic minimal mass extensions.
method Definition of Bartnik mass, construction of metrics, one-parameter family analysis.
result Static potential for asymptotically hyperbolic admissible extensions achieving Bartnik mass.

Deep learning νν-net automates cardiac MRI segmentation for accurate mass and function parameters.

problem Challenging image segmentation of biventricular cardiac anatomy.
method Deep neural network training on 253 manually segmented cases, evaluated on 1000 cases.
result State-of-the-art performance in LV and RV EF, VM measurements.

Improved protein identification in mass spectrometry data.

problem Expanding peptide scoring capabilities in tandem mass spectrometry.
method Deriving concave emission distributions for dynamic Bayesian networks.
result Efficiently learned scoring function outperforms state-of-the-art.

Study of large mass limits of G2 and Calabi-Yau monopoles on specific manifolds.

problem Understanding the behavior of monopoles in the large mass limit on G2 and Calabi-Yau manifolds.
method Developed a structure theory for the limit of SU(2)SU(2) G2G_2-monopoles and Calabi-Yau monopoles, extracting singular abelian G2-monopoles with Dirac singularities.
result Proved an energy identity for monopole bubbles in the large mass limit.

The study examines the index of MOTS in Kerr-Newman-de Sitter spacetime and its relation to mass and charge.

problem Investigating the index of MOTS in Kerr-Newman-de Sitter spacetime.
method Analyzing the spatial cross section of the cosmological horizon in the Kerr-Newman-de Sitter spacetime, proving index bounds and establishing area-charge estimates.
result Established bounds on the index of MOTS and a connection between MOTS with index one and General Relativity.

Deep Learning improves cosmological parameter estimation from weak lensing mass maps.

problem Distinguishing between five cosmological models along the σ8 - Ωm degeneracy.
method Design and implementation of a Deep Convolutional Neural Network (DCNN) trained on weak lensing mass maps.
result DCNN outperforms traditional non-Gaussian statistics (skewness and kurtosis) in high noise conditions.

Proves non-degeneracy of Riemannian Schwarzschild-anti de Sitter metrics.

problem Non-degeneracy of Riemannian Schwarzschild-anti de Sitter metrics.
method Analyzes linearised Einstein operator in TTTT-gauge for Kottler metrics.
result Non-degeneracy of TTTT-gauge-fixed linearised Einstein operator for most Riemannian Kottler metrics.

New method bypasses global fit for LISA's Galactic binaries, extracting population parameters directly.

problem Disentangling LISA's Galactic binary sources from backgrounds in a computationally intensive process.
method Simulation-based approach using normalizing flow to infer population parameters.
result Direct inference of population parameters from LISA's frequency strain series.

Study explores warped geometries of tensor manifolds, finding non-geodesic connections for some parameters.

problem Investigate non-geodesic connections in warped Segre-Veronese manifolds.
method Investigate a one-parameter family of warped geometries, presenting closed expressions for maps and distance.
result Segre-Veronese manifolds are not geodesically connected in Euclidean geometry but can be for some warping parameters.

Consider a triple of "Bartnik data" (Σ,γ,H)(Σ, γ,H), where ΣΣ is a topological 2-sphere with Riemannian metric γγ and positive function HH. We view Bartnik data as a boundary condition for the problem of finding a compact Riemannian 3-manifold (Ω,g)(Ω,g) of nonnegative scalar curvature whose boundary is isometric to (Σ,γ)(Σ,γ)

2011-06-21abs ↗pdf ↗

In this article we prove a family of local (in time) weighted Strichartz estimates with derivative losses for the Klein-Gordon equation on asymptotically de Sitter spaces and provide a heuristic argument for the non-existence of a global dispersive estimate on these spaces. The weights in the estimates depend on the ma…

2010-11-21abs ↗pdf ↗

A censored transformed model for proportional outcomes with boundary mass and an application to loss given default modeling.

problem Modeling proportional outcomes with boundary mass in loss given default (LGD) modeling.
method Zero-one censored transformed normal (ZOC-TN) model.
result Captures a wider range of qualitative density shapes than benchmark models while being parsimonious, computationally efficient, and numerically stable.

Nesterov SGD doesn't accelerate over SGD in over-parameterized learning.

problem Theoretical and practical acceleration of SGD with momentum in over-parameterized learning.
method Introducing a compensation term to Nesterov SGD, resulting in MaSS algorithm.
result MaSS converges for same step sizes as SGD and achieves accelerated convergence rates over SGD.

Estimates the probability of discovering a new type in samples from a population.

problem Estimating the missing mass of unknown type proportions in samples.
method Bayesian nonparametric tools and Good-Turing estimator for regularly varying type proportions.
result The Good-Turing estimator is rate optimal under regularly varying type proportions.

New Riemannian radial distributions help estimate parameters on symmetric spaces.

problem Challenges in manifold data analysis due to lack of parametric distributions.
method Introduced Riemannian radial distributions on symmetric spaces, utilized symmetry, and developed M-estimators.
result MLE achieves root-n convergence rate up to logarithmic terms, demonstrating optimality.

The paper proves a geometric capacitary inequality for sub-static manifolds with harmonic potentials.

problem Proving a geometric capacitary inequality for sub-static manifolds with harmonic potentials.
method Introducing a one-parameter family of functions that are monotone along the level-set flow of the potential, up to the optimal threshold.
result Proves a geometric capacitary inequality where the capacity of the horizon plays the same role as the ADM mass in the celebrated Riemannian Penrose Inequality.

Study on Hawking and Bartnik masses for specific surfaces.

problem Analyzing the positivity and bounds of Hawking and Bartnik masses for constant mean curvature surfaces.
method Intrinsic conditions and estimates for the masses of constant mean curvature surfaces.
result Positivity and estimates of Hawking and Bartnik masses for surfaces with nonnegative scalar curvature.

We prove directly without using a density theorem that (i) the ADM mass defined in the usual way on an asymptotically flat manifold is equal to the mass defined intrinsically using Ricci tensor; (ii) the Hamiltonian formulation of center of mass and the center of mass defined intrinsically using Ricci tensor are the sa…

2014-08-18abs ↗pdf ↗

The paper examines mass aspects at future null infinity and limits of quasilocal mass.

problem Understanding mass aspects and limits of quasilocal mass at future null infinity.
method Review and extension of Bondi mass and mass loss formula in Bondi-Sachs coordinate system.
result New results about the limit of quasilocal mass of unit spheres at null infinity.

Unified definition of mass aspect function for weakly regular hyperbolic manifolds.

problem Ambiguity in mass definition for asymptotically hyperbolic manifolds.
method Introduced an ADM-style mass aspect function for broad asymptotics and low regularity.
result Unified mass aspect function exhibits favorable covariance properties.

On asymptotically flat and asymptotically hyperbolic manifolds, by evaluating the total mass via the Ricci tensor, we show that the limits of certain Brown-York type and Hawking type quasi-local mass integrals equal the total mass of the manifold in all dimensions.

2015-10-27abs ↗pdf ↗