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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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25.0%50.0%75.0%100.0% · Jun 199319922001200920182026
48 results for boundary instability

Study on minimizing singular capillary cones with stability and instability results.

problem Minimizing singular capillary cones with free boundary.
method Stability criterion à la Jerison-Savin, Simons-type inequality for convex, homogeneous, symmetric functions of principal curvatures, boundary condition specific to capillary setting.
result Minimizing cones with non-sign-changing mean curvature are flat in dimensions up to 4, and non-trivial axially symmetric cones are unstable in dimensions up to 6.

Novel approach analyzes ReLU networks' training dynamics and proposes GmP for improved optimization.

problem Stochastic optimization instability in ReLU networks impedes convergence and generalization.
method Characteristic activation boundaries analysis and Geometric Parameterization (GmP) technique.
result GmP resolves instability, leading to better optimization, convergence, and generalization.

Momentum affects optimization differently at small vs large batch sizes near instability.

problem Understanding how momentum impacts optimization near the edge of stability.
method Demonstrated through batch-size dependent behavior of SGD with momentum.
result Momentum operates in two distinct regimes: amplifying stochastic fluctuations at small batch sizes and stabilizing at large batch sizes.

We improve current instability-based methods for the selection of the number of clusters kk in cluster analysis by developing a normalized cluster instability measure that corrects for the distribution of cluster sizes, a previously unaccounted driver of cluster instability. We show that our normalized instability mea…

2016-08-26abs ↗pdf ↗

The present paper is devoted to the joint motion of two immiscible incompressible liquids in porous media. The liquids have different densities and initially separated by a surface of strong discontinuity (free boundary). We discuss the results of numerical simulations for exact free boundary problems on the microscopi…

2011-10-07abs ↗pdf ↗

We extend in a minimal way the stylized model introduced in in "Tipping Points in Macroeconomic Agent Based Models" [JEDC 50, 29-61 (2015)], with the aim of investigating the role and efficacy of monetary policy of a `Central Bank' that sets the interest rate such as to steer the economy towards a prescribed inflation …

2015-01-02abs ↗pdf ↗

We prove that the Hopf vector field is a unique one among geodesic covariantly normal unit vector fields on spheres such that the submanifold generated by the field is totally geodesic in the unit tangent bundle with Sasaki metric. As application, we give a new proof of stability (instability) of the Hopf vector field …

2005-03-25abs ↗pdf ↗

We study free boundary minimal surfaces in the unit ball of low cohomogeneity. For each pair of positive integers (m,n)(m,n) such that m,n>1m, n >1 and m+n8m+n\geq 8, we construct a free boundary minimal surface Σm,nBm+nΣ_{m, n} \subset B^{m+n}(1) invariant under O(m)×O(n)O(m)\times O(n). When m+n<8m+n<8, an instability of the resulting equat…

2016-01-27abs ↗pdf ↗

Interval Neural Networks detect instabilities in image reconstructions.

problem Detecting instabilities in deep learning image reconstructions.
method Employed uncertainty quantification methods with Interval Neural Networks.
result Interval Neural Networks effectively reveal image reconstruction instabilities.

Note on instabilities in super-time-stepping methods for Heston model.

problem Instabilities in super-time-stepping methods applied to Heston model.
method Exploration of explicit super-time-stepping schemes (RK-Chebyshev, RK-Legendre) for Heston model.
result Relevance of stability remarks beyond super-time-stepping schemes.

The paper explores how word embeddings affect the stability of downstream NLP models.

problem Small changes in training data can cause significant changes in model predictions.
method Empirical and theoretical analysis of embedding instability, including the introduction of eigenspace instability measure.
result Increasing embedding memory can reduce the disagreement in predictions by 5% to 37%.

Binary perceptron's instability linked to replica symmetry breaking.

problem Understanding the relationship between algorithmic instability and replica symmetry breaking in binary perceptron learning.
method Established the connection between algorithmic instability and replica symmetry breaking by comparing the instability condition around the fixed point to the instability for breaking the replica symmetric solution of the free energy function.
result The instability condition around the algorithmic fixed point is identical to the instability for breaking the replica symmetric saddle point solution of the free energy function.

The study shows instability in certain Riemannian manifolds with real Killing spinors.

problem The instability of Riemannian manifolds with real Killing spinors.
method Analyzing families of Riemannian manifolds, including invariant Einstein metrics and Sasaki Einstein circle bundles.
result Proves instability of various Riemannian manifolds, including Aloff-Wallach spaces and homogeneous Einstein spaces.

Stability is an important aspect of a classification procedure because unstable predictions can potentially reduce users' trust in a classification system and also harm the reproducibility of scientific conclusions. The major goal of our work is to introduce a novel concept of classification instability, i.e., decision…

2017-01-20abs ↗pdf ↗

The study examines stability and instability of Poincaré-Einstein metrics using Ricci flow.

problem Stability and instability of Poincaré-Einstein metrics.
method Variant of expander entropy for asymptotically hyperbolic manifolds, local positive mass theorem, volume comparison.
result Characterization of stability and instability in terms of local positive mass theorem and volume comparison.

Study shows instability of naked singularities in perfect fluid models.

problem Instability of naked singularities in Einstein equations coupled with isothermal perfect fluid.
method Investigated spherically symmetric self-similar naked singularities under C1,αC^{1,α} perturbations of an external massless scalar field.
result Spherically symmetric self-similar naked singularities are unstable to trapped surface formation.

Clinical models can be unstable, leading to unreliable predictions.

problem Stability of clinical prediction models developed using statistical or machine learning methods.
method Simulation and case studies of statistical and machine learning approaches to show instability in model predictions.
result Model instability often leads to miscalibration of predictions in new data.

A data-driven approach predicts morphological development under structural instability.

problem Understanding and predicting spatiotemporal complexities of morphogenesis under structural instability.
method Machine-learning framework based on physical modeling of morphogenesis.
result Identification of key bifurcation characteristics and prediction of history-dependent development.

Study shows instability of naked singularities in scalar field models.

problem Stability of naked singularities in spherically symmetric Einstein-Scalar field systems.
method Analysis of a family of incoming null cones becoming increasingly singular.
result Naked singularities are unstable to black hole formation under certain perturbations.

The paper studies stability and instability of minimal submanifolds in complex Einstein spaces.

problem Stability and instability of minimal submanifolds in complex Einstein spaces.
method Computation of index and nullity, investigation of stability, and algorithm for higher eigenvalues.
result Criterion for instability of minimal submanifolds in some cases.

The paper analyzes instability in large-scale machine learning models.

problem Unexpected instability and variance in neural predictive algorithms.
method Measuring changes in geometric models with output consistency and topological stability.
result Identifying the influence of data points, approximation methods, and parameter settings on model stability.

Paper extends Simons theorem to FF-Yang-Mills connections for instability.

problem Tackles instability of FF-Yang-Mills connections.
method Extends Simons theorem to FF-Yang-Mills connections using Kobayashi-Ohnita-Takeuchi's method.
result Derives a sufficient condition for instability of non-flat FF-Yang-Mills connections.

The study examines the stability of Einstein metrics on Sasaki Einstein and nearly parallel G2 manifolds.

problem Linear instability of Einstein metrics on Sasaki Einstein and nearly parallel G2 manifolds.
method Analysis of the second and third Betti numbers for Sasaki Einstein and nearly parallel G2 manifolds.
result Positive second and third Betti numbers lead to linear instability for the respective manifolds.

Proposes a new measure to evaluate stability of statistical parameters under distributional shifts.

problem Difficulty in transferring knowledge across data sets due to distributional changes.
method Introduces a measure of instability quantifying sensitivity of statistical parameters to Kullback-Leibler divergence and directional shifts.
result The proposed measure can elucidate the type of shifts a parameter is sensitive to and improve estimation accuracy under shifted distributions.

This paper describes the black hole threshold in a moduli space of spherically symmetric spacetimes.

problem Understanding the black hole threshold in a moduli space of spherically symmetric spacetimes.
method Complete description and analysis of the black hole threshold in the moduli space M\mathfrak M.
result The black hole threshold is the extremal leaf of a C1C^1 foliation of the moduli space, separating black hole solutions from non-collapsing solutions.

Ghost points affect stability in finite difference schemes for diffusion equations.

problem Impact of ghost points on stability of finite difference schemes.
method Exploration of explicit Euler finite difference scheme with ghost points on diffusion equation.
result Stability of the scheme is affected by ghost points.

The paper analyzes numerical instability in variational flows and proposes a diagnostic method.

problem Numerical instability in variational flows affects sampling, density evaluation, and ELBO estimation.
method Treated variational flows as dynamical systems, used shadowing theory for theoretical guarantees, and developed a diagnostic procedure.
result Despite numerical instability, results from variational flows can be accurate enough for practical applications.

Proposes a continuous flow model to understand and control instability in gradient descent for deep learning.

problem Understanding and controlling the instability of gradient descent in deep learning.
method Introduces the Principal Flow (PF), a continuous time flow that approximates gradient descent dynamics.
result The PF captures divergent and oscillatory behaviors of gradient descent, including escaping local minima and saddle points.

Study on instability of extreme Reissner-Nordström spacetime perturbations.

problem Linear stability of gravitational and electromagnetic perturbations in extreme Reissner-Nordström spacetime.
method Extends Giorgi's framework to prove instability results for a set of gauge invariant quantities along the event horizon.
result Proves decay, non-decay, and polynomial blow-up estimates for certain quantities along the event horizon, depending on the number of derivatives.

Modeling financial institution dependence structures for systemic risk.

problem Understanding and measuring systemic risk in financial systems.
method Dynamic model of dependence structure using Markov structures of joint credit migrations.
result Different Markov structures with distinct dependence structures lead to varying systemic instability.