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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.

169,341 papers · 148 categories

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50100150200 · Jun 202019922001200920182026
48 results for variational mass

New mass inequalities and proofs for causal variational principles.

problem Proving new mass inequalities for causal variational principles.
method Proved a new inequality for minimizers of causal variational principles and applied it to prove the positive mass theorem.
result Introduced a positive quasilocal mass and proved new mass inequalities.

Study convergence rates of variational posterior distributions for inference.

problem Characterize convergence rates of variational posterior distributions for nonparametric and high-dimensional inference.
method Formulate general conditions on prior, likelihood, and variational class to characterize convergence rates. Propose novel prior mass conditions for specific prior distributions.
result The convergence rate of variational posterior distributions is the sum of the convergence rate of the true posterior and the variational approximation error.

The paper studies how test particles' mass and charge vary in Kaluza-Klein models.

problem Understanding how test particles' mass and charge change in Kaluza-Klein models.
method Analyzes geodesic motion in a 5D Kaluza-Klein spacetime with background metrics encoding 4D gauge fields and Higgs-like scalars.
result The mass and charge of test particles become variable when traversing regions with massive gauge fields or non-constant Higgs scalars.

New method recovers relative rates in spatial compositional data from IMS.

problem Challenges in analyzing spatial data from IMS due to competitive sampling.
method Hierarchical Variational Graph Fused Lasso using heavy-tailed graphical lasso prior and automatic differentiation variational inference.
result Our method outperforms state-of-the-practice point estimate methodologies in IMS and has superior posterior coverage.

We discuss some geometric problems related to the definitions of quasilocal mass proposed by Brown-York \cite{BYmass1} \cite{BYmass2} and Liu-Yau \cite{LY1} \cite{LY2}. Our discussion consists of three parts. In the first part, we propose a new variational problem on compact manifolds with boundary, which is motivated …

2009-06-30abs ↗pdf ↗

We define an ADM-like mass, called p-mass, for an asymptotically flat pseudohermitian manifold. The p-mass for the blow-up of a compact pseudohermitian manifold (with no boundary) is identified with the first nontrivial coefficient in the expansion of the Green function for the CR Laplacian. We deduce an integral formu…

2013-12-30abs ↗pdf ↗

New inequalities linking manifold capacities and quasi-local masses derived.

problem Understanding the relationship between manifold capacities and quasi-local masses.
method By recasting the problem into mean-convex fill-ins with nonnegative scalar curvature and considering fill-ins with singular metrics.
result Derivation of new variational characterizations of Riemannian Schwarzschild manifolds and comparison results for surfaces in them.

We identify a condition on spacelike 2-surfaces in a spacetime that is relevant to understanding the concept of mass in general relativity. We prove a formula for the variation of the spacetime Hawking mass under a uniformly area expanding flow and show that it is nonnegative for these so-called "time flat surfaces." S…

2013-10-31abs ↗pdf ↗

New method calculates volume-renormalized mass from Hamiltonian perspective.

problem Calculating volume-renormalized mass for asymptotically hyperbolic manifolds.
method Using Michel's mass invariants and a reduced Hamiltonian perspective, the volume-renormalized mass is deduced.
result The reduced Hamiltonian recovers the volume-renormalized mass and its variations.

Study on pseudo-Einstein 3-manifolds for a specific inequality, introducing Robin mass.

problem Existence of contact structures on pseudo-Einstein CR manifolds.
method Introduced Robin mass and used it to study the variation of total mass under conformal change.
result Existence of a minimizer for total mass yielding the classical LHLS inequality.

Study large mass G2\mathrm{G}_2 and Calabi--Yau monopoles, proving convergence and identifying key sets.

problem Large mass limits of G2\mathrm{G}_2 and Calabi--Yau monopoles on specific manifolds.
method Common ΘΘ-monopole framework, variational compactness theory, and finer analysis.
result Identifies currents and shows saturation of calibration inequalities; defines sets S\mathcal S, Z\mathcal Z, and C\mathcal C.

Improved clustering and anomaly detection using latent space conditioning.

problem Anomaly detection on unlabeled data.
method Conditional latent space variational autoencoder (cLSVAE) that separates latent space by conditioning on data labels.
result The method outperforms typical variational autoencoders in clustering and anomaly detection.

Proves existence of Yamabe metrics on conical 4-manifolds using min-max method.

problem Existence of Yamabe metrics on conical 4-manifolds with singular points.
method Min-max scheme adapted to singular setting, leveraging recent positive mass theorems.
result Existence of Yamabe metrics on conical 4-manifolds with finitely-many singular points.

New framework improves variational inference for high-dimensional posteriors.

problem Challenges in choosing variational objectives and approximating families for high-dimensional posteriors.
method Conceptual framework and experimental tools to understand and optimize variational objectives and families.
result For moderate-to-high-dimensional posteriors, exclusive KL divergence is recommended due to optimization ease; for low-dimensional, heavy-tailed variational families are effective.

Sharp mass bounds for ALE and ALF toric 4-manifolds.

problem Establishing lower bounds for the mass of ALE and ALF toric 4-manifolds.
method Using gravitational instantons and conical angle defects, the mass is bounded below by a sum of the mass of the corresponding instanton and an expression determined by conical angle defects.
result The mass of an ALE or ALF toric 4-manifold is not less than the mass of the corresponding gravitational instanton.

We study a functional on the boundary of a compact Riemannian 3-manifold of nonnegative scalar curvature. The functional arises as the second variation of the Wang-Yau quasi-local energy in general relativity. We prove that the functional is positive definite on large coordinate spheres, and more general on nearly roun…

2013-01-20abs ↗pdf ↗

A possible evolution of a compact hypersurface in R^n by mean curvature past singularities is defined via the level set flow. In the case that the initial hypersurface has positive mean curvature, we show that the Brakke flow associated to the level set flow is actually a Brakke flow with equality. We obtain as a conse…

2006-10-06abs ↗pdf ↗

Paper introduces a new variational objective using Alpha-Beta divergence.

problem Improving variational inference methods for complex distributions.
method Direct optimization of the sAB divergence with two control parameters.
result The sAB divergence framework provides a smooth interpolation and trade-offs between distribution properties.

Bayesian model predicts drip-line locations in heavy calcium isotopes.

problem Determining the neutron drip line in the Ca region of heavy nuclei.
method Bayesian model averaging with Gaussian-process-based extrapolations.
result Predicted posterior probabilities for drip-line locations in heavy calcium isotopes.

This paper addresses the so-called conformal capacities in Rn\mathbb R^n, n3n\ge 3, through comparing three existing definitions (due to Betsakos, Colesanti-Cuoghi, Anderson-Vamananmurthy-Fuglede respectively) and studying their associated iso-capacitary inequalities with connection to half-diameter, mean-width, mean-c…

2013-09-14abs ↗pdf ↗

Method identifies galaxies with recent star formation variations.

problem Identify galaxies with recent star formation variations.
method Approximate Bayesian Computation (ABC) with machine learning.
result Flexible star formation histories are needed for accurate modeling.

The Duffing oscillator's parameters are identified online using variational message passing.

problem Estimating parameters of a nonlinear Duffing oscillator in real-time.
method Variational message passing on a factor graph of the Duffing oscillator's generative model.
result The online inference procedure performs as well as offline methods.

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.

Constructs fill-ins with scalar curvature lower bounds for geometric applications.

problem Realizing (n1)(n-1)-dimensional manifolds as boundaries of higher-dimensional ones with controlled scalar curvature.
method Variations of an argument by Miao and the author, constructing fill-ins with different scalar curvature lower bounds.
result Illustrates applications to geometric inequalities in general relativity, including mass bounds and Penrose inequalities.

New method improves variational inference for better posterior approximation.

problem Challenges in minimizing inclusive KL divergence for amortized variational inference.
method Likelihood-tempered sequential Monte Carlo samplers to estimate inclusive KL gradient.
result SMC-Wake method fits variational distributions more accurately than existing methods.

A new method for categorical variational inference using discrete normalizing flows.

problem Challenges in optimizing variational approximations for discrete latent variables.
method Differentiable reparameterization using a mixture of discrete normalizing flows.
result Improves optimization of evidence lower bound and reduces sensitivity to hyperparameters.

New variational bounds improve posterior covariances and likelihoods.

problem Improving variational inference with different divergence measures.
method Applying variational perturbation theory to construct new variational bounds.
result New variational bounds lead to more accurate posterior covariances and higher likelihoods.