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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.

168,742 papers · 148 categories

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74149223297 · Jun 202019922001200920172026
48 results for prior boundary phenomenon

Framework expands particle filtering to estimate states beyond prior boundaries.

problem Limitations of traditional particle filtering in estimating states outside prior support.
method Diffusion-Enhanced Particle Filtering Framework with adaptive diffusion, entropy-driven regularisation, and kernel-based perturbations.
result Framework significantly improves state estimation accuracy and success rates for out-of-boundary targets.

Estimates scalar curvature without nonnegativity, showing gap phenomenon on manifolds.

problem Estimating scalar curvature without curvature nonnegativity assumption.
method Derive estimates for scalar curvature and mean curvature on manifolds and domains.
result Show that metrics on even dimensional manifolds with nonzero Euler characteristic are ε-gap distance extremal.

Paper develops physics-informed, boundary-constrained Gaussian process for fluid flow field reconstruction.

problem Reconstructing fluid flow fields from limited data.
method Physics-informed, boundary-constrained Gaussian process regression.
result Derives physics-informed kernels for simulating incompressible flows.

Study Dirac-Einstein equations on manifolds with boundary, focusing on constant volume and chiral conditions.

problem Analyzing Dirac-Einstein equations on manifolds with boundary conditions.
method Characterizing bubbling phenomena and classifying ground state bubbles, proving an Aubin-type inequality.
result Proved an Aubin-type inequality and existence result.

Bayesian approach for solving systems of linear PDEs with boundary conditions.

problem Modeling data efficiently with prior knowledge from systems of linear PDEs.
method Construct multi-output Gaussian process priors using Gröbner bases and pullback parametrizations.
result Gaussian process priors can represent solutions to systems of linear PDEs adhering to boundary conditions.

Transformer pretraining yields strong EB performance without explicit adaptation.

problem Empirical Bayes problems with unknown test distributions.
method Indirect analysis of pretrained transformer's performance under universal priors.
result Near-optimal regret bound of O~(1n)\widetilde{O}(\frac{1}{n}) for arbitrary test distributions.

A seminal result in geometric group theory is that a 1-ended hyperbolic group has a locally connected visual boundary. As a consequence, a 1-ended hyperbolic group also has a path connected visual boundary. In this paper, we study when this phenomenon occurs for CAT(0) groups. We show if a 1-ended CAT(0) group with iso…

2019-09-26abs ↗pdf ↗

The aim of this paper is to study a possible "boundary phenomenon" for Spinc Dirac operators in a special case. If you parametrise Spinc Dirac operators by a family of connections on a Spinc 4-manifold with boundary, this boundary inherits also a family of Spinc Dirac operators which has a spectral section (in the sens…

2011-03-02abs ↗pdf ↗

Paper studies curvature of stable surfaces meeting at a common boundary.

problem Stable multiple junction surfaces and their curvature estimates.
method Derived LpL^p estimate of curvature for stable multiple junction surfaces.
result Bernstein Theorem holds for stable multiple junction surfaces in certain cases.

The paper explores new phenomena in boundaries of relatively hyperbolic groups.

problem Exploring new phenomena in boundaries of relatively hyperbolic groups.
method Combination theorem to create examples of relatively hyperbolic groups with parabolic cut pairs.
result All relatively hyperbolic groups with inseparable parabolic cut pairs arise via the combination theorem.

Categorifies Stokes coefficients in Chern-Simons theory models.

problem Stokes phenomenon in Chern-Simons theory around flat connections.
method Finite-dimensional model for analytically continued Chern-Simons theory, categorification of Stokes coefficients.
result Stokes coefficients can be promoted to graded vector spaces.

New static vacuum metrics confirmed for near Euclidean boundary data.

problem Establishing sufficient conditions for near Euclidean boundary data in static vacuum metrics.
method Using new arguments from studying the conjecture for arbitrary static vacuum metrics.
result Any hypersurface in a dense subfamily is static regular.

The paper studies free boundary minimal surfaces with many boundaries and their convergence to closed minimal surfaces.

problem Sharp isoperimetric inequalities for Steklov eigenvalues on surfaces with many boundary components.
method Maximization of Steklov eigenvalues and convergence analysis of free boundary minimal surfaces.
result Free boundary minimal surfaces converge to closed minimal surfaces in the boundary sphere as the number of boundary components increases.

Catastrophic forgetting is the notorious vulnerability of neural networks to the change of the data distribution while learning. This phenomenon has long been considered a major obstacle for allowing the use of learning agents in realistic continual learning settings. A large body of continual learning research assumes…

2018-03-27abs ↗pdf ↗

We prove a phenomenon of concentration of total curvature for stable minimal surfaces in the product space H^2xR; where H^2 is the hyperbolic plane. Under some geometric conditions on the asymptotic boundary of an oriented stable minimal surface immersed in H^2xR, it has infinite total curvature. In particular, we infe…

2016-03-10abs ↗pdf ↗

The paper identifies a new geometric and spectral phenomenon in the critical hyperbolic catenoid family.

problem The study investigates the critical hyperbolic catenoid family and its geometric and spectral properties.
method The approach involves analyzing the critical hyperbolic catenoid family, identifying parameter-criticality, and studying the Robin spectrum.
result The paper proves that at a parameter-critical value aa^\sharp, the Robin nullity of ΣaΣ_{a^\sharp} is at least 3, with an additional kernel element in mode k=0k=0.

We consider billiard ball motion in a convex domain of a constant curvature surface influenced by the constant magnetic field. We prove that if the billiard map is totally integrable then the boundary curve is necessarily a circle. This result is a manifestation of the so-called Hopf rigidity phenomenon which was recen…

2012-08-12abs ↗pdf ↗

The n-strand braid group can be defined as the fundamental group of the configuration space of n unlabeled points in a closed disk based at a configuration where all n points lie in the boundary of the disk. Using this definition, the subset of braids that have a representative where a specified subset of these points …

2018-11-14abs ↗pdf ↗

Research shows conditional existence of foliations by CMC and Willmore type half-spheres near a boundary point.

problem Conditional existence of foliations by CMC and Willmore type half-spheres near a boundary point.
method Analyzes the geometry of the domain's boundary to determine foliation conditions.
result Conditional foliation is possible but not guaranteed, depending on the domain's geometry.

Bayesian PINNs learn elliptic PDEs with near-minimax posterior contraction rate.

problem Learning elliptic PDEs with noisy data and non-homogeneous boundary conditions.
method Bayesian approach with a Hölder space prior on neural network weights.
result Posterior contracts at near-minimax rate without prior knowledge of solution smoothness.

Recently Auckly-Kim-Melvin-Ruberman showed that for any finite subgroup G of SO(4) there exists a contractible 4-manifold with an effective G-action on its boundary so that the twists associated to the non-trivial elements of G do not extend to diffeomorphisms of the entire manifold. We use a Heegaard Floer theoretic a…

2016-09-15abs ↗pdf ↗

Theoretical study explains grokking in neural networks.

problem Understanding the abrupt transition from fitting to generalizing in neural networks.
method Characterized a shell-core topological configuration of the solution space induced by Adam's optimization dynamics.
result Derived grokking scaling laws for learning rate, batch size, and regularization coefficient.

This is an expository article, explaining recent work by D. Groisser and myself [GS] on the extent to which the boundary region of moduli space contributes to the ``simple type'' condition of Donaldson theory. The presentation is intended to complement [GS], presenting the essential ideas rather than the analytical det…

1997-12-22abs ↗pdf ↗

We show that the twisted Kähler-Ricci flow on a complex manifold X converges to a flow of moving free boundaries, in a certain scaling limit. This leads to a new phenomenon of singularity formation and topology change which can be seen as a complex generalization of the extensively studied formation of shocks in Hamilt…

2016-04-12abs ↗pdf ↗

This work shows that Gaussian is the only prior for optimal linear estimation in L1L^1 loss.

problem Optimal linear estimation of a random variable from noisy observations under L1L^1 fidelity criterion.
method Analyzes the conditions under which the conditional median is a linear estimator and identifies the Gaussian distribution as the only prior that induces linearity.
result Gaussian is the only prior distribution that induces linearity in the conditional median for L1L^1 loss.

Noise in SGD affects overparameterized models, favoring sparse solutions.

problem Understanding and mitigating implicit bias in SGD with parameter-dependent noise.
method Theoretical analysis of a quadratically-parameterized model with label noise and Gaussian noise.
result SGD with label noise recovers sparse ground-truth solutions, while SGD with Gaussian noise overfits dense solutions.

Paper analyzes weak-to-strong generalization in CNNs, identifying data-scarce and data-abundant regimes.

problem Weak-to-strong generalization in CNNs trained on weak models.
method Formal analysis of gradient descent dynamics in data-scarce and data-abundant regimes.
result Identifies two regimes and distinct mechanisms of generalization in each.

Deep neural networks' decision boundaries move closer to natural images during training.

problem Limited understanding of deep neural networks' decision boundaries and regions.
method Examined the minimum distance of data points to the decision boundary over training.
result The decision boundary moves closer to natural images during training, even in late epochs.

SSMs can be poisoned with clean labels, leading to generalization failure.

problem The implicit bias of SSMs can be manipulated by including special training examples with clean labels.
method Formal proof and empirical demonstration of the phenomenon.
result SSMs can fail to generalize even with clean labels, due to the inclusion of special training examples.