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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.

168,695 papers · 148 categories

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295786114 · Jun 202019922001200920172026
48 results for perturbation argument

We prove that the mass endomorphism associated to the Dirac operator on a Riemannian manifold is non-zero for generic Riemannian metrics. The proof involves a study of the mass endomorphism under surgery, its behavior near metrics with harmonic spinors, and analytic perturbation arguments.

2010-09-28abs ↗pdf ↗

The study examines the long-term behavior of mean curvature flows in closed 3-manifolds.

problem Understanding the long-term behavior of mean curvature flows in closed 3-manifolds.
method The approach involves constructing piecewise almost regular flows and applying perturbative arguments.
result The study constructs minimal surfaces in 3-manifolds via parabolic methods.

This short review is the result of a minicourse at the Sapienza University of Rome the author gave about the proof of the gg-theorem. We review the hard Lefschetz theorem for simplicial spheres, as well as the theory at its core: perturbations of maps, biased Poincaré pairings and a cobordism argument that relates the…

2019-06-14abs ↗pdf ↗

New method proves instability of naked singularity and censors it.

problem Proving instability and censoring naked singularity.
method Einstein-scalar field system, hyperbolic short-pulse method, non-perturbative elliptic arguments.
result Tiny anisotropic perturbation leads to anisotropic apparent horizon censoring the naked singularity.

We propose an online algorithm for cumulative regret minimization in a stochastic multi-armed bandit. The algorithm adds O(t)O(t) i.i.d. pseudo-rewards to its history in round tt and then pulls the arm with the highest average reward in its perturbed history. Therefore, we call it perturbed-history exploration (PHE). Th…

2019-02-26abs ↗pdf ↗

Self-similar solutions to geometric flows are stable under small perturbations.

problem Stability of self-similar solutions in geometric flows.
method Global analytic solutions, compactness arguments, spatial equi-decay properties, and estimates of linearized operator.
result Perturbed solutions are asymptotically self-similar as time tends to infinity.

The cc-curvature of a complete surface with Gauss curvature close to 1 in C2C^2 norm is almost-positive (in the sense of Kim--McCann). Our proof goes by a careful case by case analysis combined with perturbation arguments from the constant curvature case, keeping track of an estimate on the closeness curvature conditi…

2010-09-18abs ↗pdf ↗

Solves the Merton investment-consumption problem using a new approach.

problem Infinite-horizon Merton investment-consumption problem in a constant-parameter Black-Scholes-Merton market.
method Simple and elegant argument involving a stochastic perturbation of the utility function.
result Overcomes complications in existing primal verification proofs.

We study the problem of option pricing and hedging strategies within the frame-work of risk-return arguments. An economic agent is described by a utility function that depends on profit (an expected value) and risk (a variance). In the ideal case without transaction costs the optimal strategy for any given agent is fou…

1998-03-19abs ↗pdf ↗

Given a smooth function f on R^n and a submanifold M, we prove that the set of diagonal quadratic forms q such that the restriction of f+q to M is Morse is a dense set (in the n-dimensional space of diagonal quadratic forms). The standard transversality argument seems not to work and we need a more refined approach.

2011-11-16abs ↗pdf ↗

The study shows that certain metrics on spheres prevent stable tangent cones for area-minimizing boundaries.

problem Preventing stable tangent cones for area-minimizing boundaries under specific metrics.
method Developed a perturbation theorem and used spectral theory and compactness arguments.
result A residual set of metrics on Sn+1S^{n+1} precludes linearly stable tangent cones for area-minimizing boundaries.

Following \cite{citeSavelyevVirtualMorsetheoryonOmegaOmegaHam(Momega)(Momega).}, we develop here a connection between Morse theory for the (positive) Hofer length functional L:ΩHam(M,ω)RL: Ω\text {Ham}(M, ω) \to \mathbb{R}, with Gromov-Witten/Floer theory, for monotone symplectic manifolds (M,ω) (M, ω) . This gives some immediate restrictio…

2013-08-15abs ↗pdf ↗

We prove a version the Penrose inequality for black hole space-times which are perturbations of the Schwarzschild exterior in a slab around a null hypersurface N0\underline{\mathcal{N}}_0. N0\underline{\mathcal{N}}_0 terminates at past null infinity I\mathcal{I}^- and S0:=N0\mathcal{S}_0:=\partial\underline{\mathcal{N}}_0

2015-06-21abs ↗pdf ↗

New bounds for KANs trained with DP-SGD, addressing correlated noise.

problem Risk bounds for Kolmogorov-Arnold Networks trained by DP-SGD with correlated noise.
method Established new optimization and population risk analysis for KANs trained with DP-SGD, addressing correlated noise.
result First optimization and population risk analysis of correlated-noise mechanisms for DP training in non-convex settings, including neural networks.

This work examines how adversarial vulnerability changes with the dimensionality of the subspace of perturbations.

problem Understanding adversarial vulnerability in constrained input spaces.
method Investigates adversarial vulnerability in subspace VV of the input space XX with varying dimensions, using PGD attacks and analyzing the dependence on εε and dim(V)/dim(X)dim(V)/dim(X).
result Adversarial success of PGD attacks is a monotonically increasing function of $ε( rac{dim(V)}{dim(X)})^{ rac{1}{q}}$.

Laplacian Eigenvectors of the graph constructed from a data set are used in many spectral manifold learning algorithms such as diffusion maps and spectral clustering. Given a graph constructed from a random sample of a dd-dimensional compact submanifold MM in RD\mathbb{R}^D, we establish the spectral convergence rate…

2015-10-27abs ↗pdf ↗

Analyzes perturbed contact instantons with Legendrian boundary conditions using geometric analysis.

problem Analyzing nonlinear elliptic systems associated with contact Hamiltonian trajectories.
method Identifies correct action and energy functionals, develops elliptic regularity theory, and proves asymptotic convergence.
result Established CC^\infty convergence of perturbed contact instantons under finite energy hypothesis.

Low-entropy surfaces can be flowed into spheres and cylinders.

problem Proving mean curvature flow for low-entropy hypersurfaces.
method Low-entropy density drop argument and recent work on hypersurfaces.
result Closed hypersurfaces with entropy ≤ 2 can be flowed into spherical and cylindrical shapes.

Quantum Kerr learning shows enhancements in convergence and generalization for kernel-based methods.

problem Improving convergence and generalization in kernel-based methods for quantum computing.
method Combining quantum mechanics with neural tangent kernel theory and first-order perturbation theory.
result Quantum enhancements in terms of convergence time and generalization error.

The paper connects neural network ensembles to Bayesian inference using variational methods.

problem Explaining the behavior of ensemble methods in neural networks.
method Deriving conditions for ensemble optimization to reduce divergence to the posterior distribution.
result Ensemble methods can be a valid alternative to approximate Bayesian inference.

This article finds constant scalar curvature Kahler metrics on certain compact complex surfaces. The surfaces considered are those admitting a holomorphic submersion to a curve, with fibres of genus at least 2. The proof is via an adiabatic limit. An approximate solution is constructed out of the hyperbolic metrics on …

2004-01-21abs ↗pdf ↗

Ambitwistor string matches superstring chiral integrands at zero tension.

problem Matching scattering amplitudes in superstring theory and ambitwistor string theory.
method Direct computation and reduction to ordinary moduli space.
result Chiral half integrands of superstring match those of ambitwistor string in the zero tension limit.

The traceless SU(2)SU(2) character variety R(S2,{ai,bi}i=1n)R(S^2,\{a_i,b_i\}_{i=1}^n) of a 2n2n-punctured 2-sphere is the symplectic reduction of a Hamiltonian nn-torus action on the SU(2)SU(2) character variety of a closed surface of genus nn. It is stratified with a finite singular stratum and a top smooth symplectic stratum of dimens…

2015-11-01abs ↗pdf ↗

Neural networks learn to mimic brain neurons with two-input activation functions, improving performance and robustness.

problem Training neural networks to mimic the complex interactions of brain neurons.
method Developed a network-in-network architecture with two-input activation functions, optimized hyperparameters, and compared to conventional ReLU networks.
result Two-input activation functions can learn soft XOR functions, improving network performance and robustness.

Optimizes trading large volumes of volatile assets with fast mean-reverting volatility.

problem Challenges of executing large volumes of illiquid or volatile assets.
method Modeling uncertain volatility and liquidity with fast mean-reverting dynamics, using singular perturbation arguments and high-frequency data.
result Approximately optimal trade execution strategies under fast mean-reversion.

Deep-learning based classification algorithms have been shown to be susceptible to adversarial attacks: minor changes to the input of classifiers can dramatically change their outputs, while being imperceptible to humans. In this paper, we present a simple hypothesis about a feature compression property of artificial i…

2019-05-25abs ↗pdf ↗

New deep learning model robust to adversarial attacks using stochastic LWTA units.

problem Adversarial robustness in deep learning networks.
method Introduces deep networks with stochastic LWTA activations, combining them with Bayesian non-parametric tools.
result Achieves high robustness to adversarial perturbations, outperforming state-of-the-art methods.

New model improves deep learning robustness against adversarial attacks.

problem Improving adversarial robustness of deep learning models.
method Local competition principle, LWTA nonlinearities, Bayesian non-parametrics.
result The new model achieves high robustness to adversarial perturbations on MNIST and CIFAR10 datasets.

Let $(M, \om)$ be a symplectic manifold, endowed with a compatible almost complex structure J and the associated metric g . For any p \in {1, 2, ... (dim M)/2} the form $\Om := \frac{\om^p}{p!}$ is a calibration. More generally, dropping the closedness assumption on $\om$, we get an almost hermitian manifold $(M, \om, …

2011-11-07abs ↗pdf ↗