Research
On-device research index

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

Trend · papers per month

4398781,3161,755 · Jun 202019922001200920182026
48 results for perturbations by potentials

The study introduces a new function to analyze special holonomy manifolds.

problem Analyzing harmonic forms on special holonomy manifolds.
method Introducing a global perturbation potential function and studying its effects on harmonic forms.
result Established vanishing theorems on L2L^{2} harmonic forms under certain conditions.

Study magnetic perturbations in Riemannian and Lorentzian Calderón problems.

problem Determining metrics from boundary measurements under magnetic perturbations.
method Runge approximation for Riemannian case, microlocal analysis for Lorentzian case.
result Metrics can be uniquely determined in both Riemannian and Lorentzian cases under specific perturbations.

The paper identifies potential adversarial samples near decision boundaries of neural networks.

problem Vulnerability of deep neural networks to small perturbations of inputs.
method Developed a method to explore near decision boundaries of trained classifiers to identify potential adversarial samples.
result Potential adversarial samples represent only 61% of the test data but cover more than 82% of adversarial samples produced by iFGSM and 92% of those by DeepFool on CIFAR10.

Essential self-adjointness proved for perturbed quadharmonic operators on Riemannian manifolds.

problem Proving essential self-adjointness for perturbed quadharmonic operators.
method Using bounded geometry assumptions and a non-positive potential function.
result Essential self-adjointness condition established for perturbed quadharmonic operators.

We consider scattering by an abstract compactly supported perturbation in R^n. To include the traditional cases of potential, obstacle and metric scattering without going into their particular nature we adopt the "black box" formalism developed jointly with Sjostrand [23]. It is quite likely that one could extend the r…

1999-01-21abs ↗pdf ↗

Universal perturbations can fool deep neural networks with tiny changes.

problem Deep neural networks are vulnerable to very small, imperceptible changes that misclassify natural images.
method Systematic algorithm for computing universal perturbations, analyzing their properties.
result State-of-the-art deep neural networks are highly vulnerable to universal perturbations that are quasi-imperceptible to humans.

Wider networks improve natural accuracy but worsen perturbation stability, affecting overall robustness.

problem Understanding the tradeoff between natural accuracy and perturbation stability in wider neural networks for adversarial robustness.
method Careful examination of the relationship between network width, robust regularization parameter λ, and perturbation stability using neural tangent kernels.
result Wider networks can achieve better natural accuracy but worse perturbation stability, leading to potentially worse overall model robustness.

The Ma-Trudinger-Wang curvature --- or cross-curvature --- is an object arising in the regularity theory of optimal transportation. If the transportation cost is derived from a Hamiltonian action, we show its cross-curvature can be expressed in terms of the associated Jacobi fields. Using this expression, we show the l…

2009-08-31abs ↗pdf ↗

Unified framework for solving first passage times of diffusion processes.

problem Solving first passage times of time-homogeneous diffusion processes.
method Unified framework based on killed version potential theory and perturbation theory.
result Closed-form solutions for probability densities of level crossing problems.

Simple regional perturbations maintain model transferability while reducing adversarial example distortion.

problem Comparing efficacy of regional adversarial attacks without complex methods.
method Developed a simple regional adversarial perturbation attack using cross-entropy sign.
result Localized adversarial examples require significantly less LpL_p norm distortion compared to non-local counterparts.

Paper optimizes FTPL for adversarial and stochastic bandits with specific tail distributions.

problem Optimizing Follow-the-Perturbed-Leader (FTPL) policy for bandit problems.
method Analyzes FTPL with Fréchet-type tail distributions in adversarial and stochastic settings.
result FTPL with certain Fréchet-type tail distributions achieves O(KT)\mathcal{O}(\sqrt{KT}) regrets in adversarial bandits.

New sigma models compute graviton scattering amplitudes from quaternionic geometry.

problem Computing graviton scattering amplitudes from quaternionic geometry.
method Introducing new twistor sigma models that encode finite non-linear perturbations of flat structures.
result Provides a first-principles derivation of Hodges' formula for MHV graviton amplitudes.

The paper assesses machine learning robustness with covariate perturbations.

problem Ensuring robustness of machine learning models against adversarial attacks and data changes.
method Proposes a framework using covariate perturbation techniques to assess model robustness.
result Demonstrates the effectiveness of the approach in comparing robustness across models and identifying instabilities.

Develops new methods to create imperceptible image changes that fool classifiers.

problem Improving the robustness of image classifiers by creating subtle changes undetectable to humans.
method Two methods: Edge-Aware and Color-Aware, designed to reduce detectability of image perturbations.
result Demonstrated that the new methods effectively cause misclassification and are computationally efficient.

We study stability of non-compact gradient Kaehler-Ricci flow solitons with positive holomorphic bisectional curvature. Our main result is that any compactly supported perturbation and appropriately decaying perturbations of the Kaehler potential of the soliton will converge to the original soliton under Kaehler-Ricci …

2003-07-22abs ↗pdf ↗

Paper examines stability of Bayesian posterior measures using integral probability metrics.

problem Stability of Bayesian inference in large-scale inverse problems.
method New families of integral probability metrics for likelihood and prior perturbations.
result Constructs new stability results for Bayesian posterior measures.

Image classifiers are sensitive to small changes, affecting most images in a class.

problem Sensitivity of image classifiers to small perturbations.
method Demonstrated sensitivity for any classifier over images, showing that for most classes, a tiny perturbation can change the classification of a majority of images.
result Image classifiers are sensitive to small perturbations, affecting most images in a class.

BioBO optimizes gene perturbation design using Bayesian optimization with biological priors.

problem Efficient design of genomic perturbation experiments in drug discovery.
method Integrates Bayesian optimization with multimodal gene embeddings and enrichment analysis.
result Improves labeling efficiency by 25-40% and identifies top-performing perturbations more effectively.

Stability of Minkowski space-time in higher dimensions proven for arbitrary small perturbations.

problem Stability of Minkowski space-time solution to Einstein-Yang-Mills equations in higher dimensions.
method Global stability proof for arbitrary small perturbations using wave coordinates and gauge invariant norms.
result Global stability of Minkowski space-time in higher dimensions n5n \geq 5 for arbitrary small perturbations.

This work develops sampling methods for differential privacy using SHK geometry.

problem Approximating sampling for the exponential mechanism in differential privacy.
method Develops perturbation theory for SHK gradient flows and applies to differential privacy.
result Derives time-dependent Pure-DP guarantees and Approximate-DP certificates.

New potential theory on minimal hypersurfaces shows stable growth of solutions near singularities.

problem Analyzing potential theory on minimal hypersurfaces.
method Introducing Hardy structures to study classical operators and showing stable growth of solutions.
result Minimal growth of positive solutions of Lw = 0 is stable and persists under perturbations or blow-ups.

ShapeShifter attacks physical object detectors with robust adversarial perturbations.

problem Crafting physical adversarial perturbations to fool object detectors.
method Adapted Expectation over Transformation technique for object detection.
result Successfully generated adversarial perturbations that fool Faster R-CNN.

We first analyze the integrated density of states (IDS) of periodic Schrödinger operators on an amenable covering manifold. A criterion for the continuity of the IDS at a prescribed energy is given along with examples of operators with both continuous and discontinuous IDS'. Subsequently, alloy-type perturbations of th…

2007-05-08abs ↗pdf ↗

CausalRegNet generates accurate data for gene perturbation experiments, improving CSL methods.

problem Assessing and selecting causal structure learning methods in gene perturbation experiments.
method CausalRegNet, a multiplicative effect structural causal model, generates accurate observational and interventional data.
result CausalRegNet generates more accurate distributions and scales better than current simulation frameworks.

The study analyzes prediction errors in systems with memory kernels, providing bounds and stability results.

problem Prediction errors in stochastic dynamical systems with memory kernels.
method Analysis of generalized Langevin equations (GLEs) with Volterra equations, integrating synchronized noise coupling and weighted norms.
result Prediction discrepancies decay at a rate determined by the memory kernel's decay, quantitatively bounded by kernel estimation errors.

PHE adds pseudo-rewards to history to minimize regret in stochastic bandits.

problem Minimizing cumulative regret in stochastic multi-armed bandits.
method PHE algorithm that adds O(t)O(t) i.i.d. pseudo-rewards to history and pulls the best arm based on the perturbed history.
result Near-optimal regret bounds derived for PHE.

This paper tackles adversarial perturbations in multi-label classification problems.

problem Vulnerability and robustness of multi-label learning models under adversarial attacks.
method Proposes a general attacking framework and a ranking-based framework for generating multi-label adversarial perturbations.
result Demonstrates the effectiveness of the proposed frameworks and provides insights into the vulnerability of multi-label deep learning models.

This paper calibrates option pricing models using quantum mechanics.

problem Tackling the inverse problem of extracting potential and bubble shape from empirical financial data.
method Interpreting the Black-Scholes model as a quantum Schrödinger equation and using semi-classical methods to approximate solutions.
result The non-equilibrium model provides better estimations of real financial data than the traditional equilibrium model.

S2SNets defend against adversarial attacks by interpreting perturbations as signal.

problem Fragility of deep neural networks to adversarial attacks.
method Two-stage training of S2SNets: unsupervised first, fine-tuning second, using classifier gradients.
result S2SNets achieve comparable resilience in white-box attacks and robustness in gray-box attacks.

This paper examines how graph topology affects adversarial attacks on vertex classification.

problem Adversarial attacks on vertex classification are vulnerable to graph topology changes.
method Examined two topological graph characteristics and their impact on adversary perturbation budgets.
result Training sets including high-degree vertices or those ensuring all unlabeled nodes have neighbors can significantly increase the adversary's perturbation budget.