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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.

168,657 papers · 148 categories

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48 results for Effective Radius

The paper extends radius estimates for stable hypersurfaces in 2, 3, and 4 dimensions.

problem Estimating the radius of nearly stable hypersurfaces in specific dimensions.
method Generalizing existing radius estimates for CMC hypersurfaces in Riemannian manifolds with bounded curvature.
result Radius estimates for nearly stable hypersurfaces in 2, 3, and 4 dimensions are extended.

Study on spherical bodies of constant width on the unit sphere, proving bounds on their relative effective radius.

problem Understanding the smallest spherical bodies of constant width on the unit sphere.
method Analyzing spherical bodies of constant width on the unit sphere, constructing examples and applying geometric arguments.
result Proved non-trivial bounds on the relative effective radius of spherical bodies of constant width.

Study on curvature in finitely generated groups, showing positive curvature in specific cases.

problem Understanding curvature in finitely generated groups.
method Analyzing dead-end elements and related elements to find curvature, studying effect of radius.
result Examples of positive curvature for arbitrary radius in lamplighter and Houghton's group.

Explaining the unreasonable effectiveness of deep learning has eluded researchers around the globe. Various authors have described multiple metrics to evaluate the capacity of deep architectures. In this paper, we allude to the radius margin bounds described for a support vector machine (SVM) with hinge loss, apply the…

2018-11-03abs ↗pdf ↗

Paper introduces robust market making using Wasserstein distance and entropy regularization.

problem Market making robustness under uncertainty.
method Wasserstein distance, entropy regularization, convex optimization, optimal radius selection.
result The robust market making problem can be reformulated as a convex optimization problem.

The paper assesses text classification robustness through maximal safe radius computation.

problem Vulnerability of neural network models to small input modifications.
method Maximal safe radius computation, Monte Carlo Tree Search, syntactic filtering, linear bounding techniques.
result Approximation methods for computing upper and lower bounds of maximal safe radius.

The ratio of convexity radius over injectivity radius may be made arbitrarily small within the class of compact Riemannian manifolds of any fixed dimension at least two. This is proved using Gulliver's method of constructing manifolds with focal points but no conjugate points. The approach is suggested by a characteriz…

2014-12-01abs ↗pdf ↗

Paper optimizes hyperparameters for high-dimensional regression models.

problem Optimizing robustness radius in high-dimensional linear regression.
method Distributionally robust optimization (DRO) with high-dimensional asymptotic statistics.
result Optimal hyperparameter selection minimizes estimation error efficiently.

Upper bound for conjugate radius in open manifolds with scalar curvature and spectrum constraints.

problem Bounding the conjugate radius of open manifolds with specific curvature and spectrum conditions.
method Established an upper bound using scalar curvature and bottom-of-spectrum constraints.
result For certain conditions, the conjugate radius is no more than π.

The study examines how average scalar curvature influences geometric properties of Riemannian manifolds.

problem Investigating the geometric properties of Riemannian manifolds influenced by average scalar curvature.
method Analyzing the conjugate radius, average area of geodesic spheres, average volume of metric balls, and total volume of closed manifolds.
result Improves the Bishop-Gromov estimate on the average volume of metric balls and proves monotone decreasing properties of certain geometric integrals.

Enhances robustness of deep neural networks with randomized smoothing.

problem Improving robustness of deep neural networks against noisy inputs and adversarial attacks.
method Introduces a variance-margin trade-off approach to increase certified robust radius using pre-trained models.
result Significant improvement in certified accuracy compared to state-of-the-art methods.

The paper bounds bandwidth and focal radius for manifolds with positive isotropic curvature.

problem Bounding bandwidth and focal radius for manifolds with positive isotropic curvature.
method Using spectral properties of a twisted de Rham-Hodge operator.
result Upper bounds on bandwidth and focal radius are derived for hypersurfaces in PIC manifolds.

Upper bound on Stiefel manifold's injectivity radius found.

problem Finding the maximum distance within which the Stiefel manifold remains injective.
method Exhibited conjugate points and calculated the minimum of geodesic lengths.
result Upper bound on Stiefel manifold's injectivity radius is conjectured to be equal to the injectivity radius.

Adversarial training improves linear regression solutions, offering robustness against small perturbations.

problem Vulnerability of linear models to adversarial perturbations.
method Formulated as a min-max problem, adversarial training minimizes the best solution under worst-case attacks.
result Adversarial training yields the minimum-norm interpolating solution in overparameterized models, equivalent to parameter shrinking methods in underparameterized models.

Smooths metrics on manifolds with curvature bounds and injectivity radius constraints.

problem Smooth metrics on manifolds with curvature and injectivity constraints.
method Bi-Lipschitz smoothing with controlled smoothing and volume lower bounds.
result Proves existence of smooth metrics with curvature bounds and injectivity radius constraints.

A translation structure equips a Riemann surface with a singular flat metric. Not much is known about the shape of a random translation surface. We compute an upper bound on the expected value of the covering radius of a translation surface in any stratum H_1(kappa). The covering radius of a translation surface is the …

2018-09-27abs ↗pdf ↗

This paper considers metric balls B(p,R)B(p,R) in two dimensional Riemannian manifolds when RR is less than half the convexity radius. We prove that Area(B(p,R))8πR2Area(B(p,R)) \geq \frac{8}πR^2. This inequality has long been conjectured for RR less than half the injectivity radius. This result also yields the upper bound $μ_2(B(p,R)…

2017-01-23abs ↗pdf ↗

Study finds shortest geodesic loops on Stiefel manifold and calculates its injectivity radius.

problem Determining the shortest geodesic loops and injectivity radius on Stiefel manifold.
method Combining bounds on sectional curvature with existing metrics.
result Exact value of the injectivity radius for a wide range of metrics.

Study compares eigenvalues on spherically symmetric manifolds to Euclidean balls.

problem Comparing eigenvalues on spherically symmetric manifolds to Euclidean balls.
method Examines Dirichlet Laplace eigenvalues on balls of spherically symmetric manifolds and Euclidean space.
result Eigenvalues on spherically symmetric manifolds are smaller for small radii, but larger for hyperbolic spaces.

The paper improves bounds on injectivity radius for manifolds with positive scalar curvature.

problem Finding tighter bounds on the injectivity radius for manifolds with positive scalar curvature.
method Utilizing Green's inequality and topological assumptions on manifolds, including specific 3-manifolds and products.
result Stronger upper bounds on injectivity radius for certain manifolds, including products and 3-manifolds with positive scalar curvature.

Quantum reservoir computing needs coherence influx for effective information processing.

problem Understanding and optimizing quantum reservoir computing.
method Theoretical and numerical analysis of quantum systems, focusing on coherence influx and spectral radius of Pauli transfer matrix.
result Coherence influx is essential for realizing nonstationary echo state property in quantum reservoir computing.

In this note we relate the geometric notion of fill radius with the fundamental group of the manifold. We prove: ''Suppose that a closed Riemannian manifold M satisfies the property that its universal cover has bounded fill radius. Then the fundamental group of M is virtually free.'' We explain the relevance of this th…

2009-06-24abs ↗pdf ↗

Characterizes submanifolds with minimum ratio of diameter to focal radius.

problem Finding submanifolds with the minimum ratio of extrinsic diameter to focal radius.
method Combining K. Sakamoto's classification of submanifolds with planar geodesics and A. Schur's Bow Lemma for space curves.
result Essentially round spheres or Veronese embeddings of projective spaces achieve the minimum ratio.

We define a capacity which measures the size of Weinstein tubular neighbourhoods of Lagrangian submanifolds. In symplectic vector spaces this leads to bounds on the codisc radius for any closed Lagrangian submanifold in terms of Viterbo's isoperimetric inequality. Moreover, we prove a generalization of Gromov's packing…

2012-10-08abs ↗pdf ↗

We discuss inverse resonance scattering for the Laplacian on a rotationally symmetric manifold M=(0,)×YM = (0,\infty) \times Y whose rotation radius is constant outside some compact interval. The Laplacian on MM is unitarily equivalent to a direct sum of one-dimensional Schrödinger operators with compactly supported potenti…

2019-04-18abs ↗pdf ↗