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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 spherical width

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 a relative aspherical conjecture and prove 3-manifold obstruction to positive scalar curvature.

problem Obstructing the existence of positive scalar curvature in higher dimensions.
method Introduced a relative aspherical condition and a new geometric quantity called spherical width.
result Proved results on how 3-manifolds obstruct the existence of positive scalar curvature.

The width of a closed convex subset of Euclidean space is the distance between two parallel supporting planes. The Blaschke-Lebesgue problem consists of minimizing the volume in the class of convex sets of fixed constant width and is still open in dimension n > 2. In this paper we describe a necessary condition that th…

2009-06-17abs ↗pdf ↗

The setting for this brief paper is R^3. Distance between two spheres is understood as distance delta between spherical centers. For instance, a Reuleaux tetrahedron T is the intersection of four unit balls satisfying delta=1 pairwise. Volume and surface area of T are already well-known; our humble contribution is to c…

2013-01-23abs ↗pdf ↗

Two-layer NN with channel attention learns low-degree spherical polynomials efficiently.

problem Learning low-degree spherical polynomials with over-parameterized neural networks.
method Two-layer neural network with channel attention, vanilla gradient descent, learnable channel selection.
result Minimally improved sample complexity of $n \asymp Θ(d^{\ell_0}/\eps)$ for learning low-degree polynomials.

In this paper we study the isoperimetric-type equalities for rosettes, i.e. regular closed planar curves with non-vanishing curvature. We find the exact relations between the length and the oriented area of rosettes based on the oriented areas of the Wigner caustic, the Constant Width Measure Set and the Spherical Meas…

2016-05-26abs ↗pdf ↗

The paper proves rigidity for certain product spaces and bounds for band widths.

problem Proving rigidity for product spaces and bounds for band widths.
method Combining stable weighted slicing with a spectral Dirac operator argument.
result Closed spin (Mn,g)(M^n,g) is isometrically covered by SnmimesRmS^{n-m} imes\mathbb{R}^m under certain conditions.

The volume spectrum of fiber bundles is bounded by the product of the base's volume spectrum and the fiber's volume.

problem Bounding the volume spectrum of fiber bundles and understanding its relationship with the base and fibers.
method Established an inequality relating the volume spectrum of a fiber bundle to the volume spectrum of its base and the volume of the largest fiber.
result The volume spectrum of a fiber bundle is bounded by the product of the volume spectrum of the base and the volume of the largest fiber.

For a Riemannian manifold Mn+1M^{n+1} and a compact domain ΩMn+1Ω\subset M^{n+1} bounded by a hypersurface Ω\partial Ω with normal curvature bounded below, estimates are obtained in terms of the distance from OO to Ω\partial Ω for the angle between the geodesic line joining a fixed interior point OO in ΩΩ to a point on…

2012-12-28abs ↗pdf ↗

For a convex domain DD bounded by the hypersurface D\partial D in a space of constant curvature we give sharp bounds on the width RrR-r of a spherical shell with radii RR and rr that can enclose D\partial D, provided that normal curvatures of D\partial D are pinched by two positive constants. Furthermore, in the …

2014-02-11abs ↗pdf ↗

Study on curvature measures in non-Euclidean spaces linked to Euclidean geometry.

problem Investigating curvature measures in spherical, hyperbolic, and de Sitter spaces.
method Establishing a unifying framework for curvature measures in real-analytic spaces of constant curvature.
result Floating bodies and duality in non-Euclidean spaces are connected to curvature measures in Euclidean space.

Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regularized regression, such as Lasso, needs to consider four aspects: the norm, the loss function, the design matrix, and the noise model. This paper presents generalizations of such estimation error analysis on all four aspe…

2015-05-09abs ↗pdf ↗

We define the Wirtinger width of a knot. Then we prove the Wirtinger width of a knot equals its Gabai width. The algorithmic nature of the Wirtinger width leads to an efficient technique for establishing upper bounds on Gabai width. As an application, we use this technique to calculate the Gabai width of approximately …

2019-12-04abs ↗pdf ↗

Empirical study compares finite- and infinite-width BNNs, revealing performance differences under model mismatch.

problem Comparing BNNs with different widths due to conflicting model properties and inference intractability.
method Empirical comparison of finite- and infinite-width BNNs, analyzing performance under model mismatch.
result Increasing width can hurt BNN performance when the model is mis-specified, and finite-width BNNs generalize better under model mismatch.

Lectures on deep learning properties in infinite and large-width networks.

problem Understanding deep neural networks in extreme width conditions.
method Analysis of random deep neural networks, connections to linear models, kernels, and Gaussian processes, perturbative and non-perturbative treatments.
result Properties and behaviors of deep neural networks in the infinite-width limit and large-width regime.

A number of results for C2^2-smooth surfaces of constant width in Euclidean 3-space E3{\mathbb{E}}^3 are obtained. In particular, an integral inequality for constant width surfaces is established. This is used to prove that the ratio of volume to cubed width of a constant width surface is reduced by shrinking it along…

2007-04-24abs ↗pdf ↗

Residual networks with block width max(d_x, d_y) approximate all functions.

problem Achieving universal approximation with residual networks.
method Established bounds on block width for different activation functions.
result Minimum block width for universal approximation is max(d_x, d_y) with inner width 1.

While studying the existence of closed geodesics and minimal hypersurfaces in compact manifolds, the concept of width was introduced in different contexts. Generally, the width is realized by the energy of the closed geodesics or the volume of minimal hypersurfaces, which are found by the Minimax argument. Recently, Ma…

2016-12-20abs ↗pdf ↗

Study infinite-depth limits of neural networks with fixed width.

problem Understanding the behavior of neural networks as depth increases with fixed width.
method Analyzing finite-width residual networks with random Gaussian weights, focusing on the infinite-depth limit.
result The pre-activations converge to a zero-drift diffusion process, differing from the infinite-width limit.

In "Width complexes for knots and 3-manifolds," Jennifer Schultens defines the width complex for a knot in order to understand the different positions a knot can occupy in the 3-sphere and the isotopies between these positions. She poses several questions about these width complexes; in particular, she asks whether the…

2010-08-30abs ↗pdf ↗

Wide neural networks can degrade performance, contrary to conventional wisdom.

problem Understanding the limitations of increasing network width in neural networks.
method Using Deep Gaussian Processes to decouple capacity and width, analyzing their effects on representational power and non-Gaussianity.
result Wide neural networks can become less adaptable and more Gaussian, leading to performance degradation.

The abstract proves spherical surface decompositions with conical singularities.

problem Decomposing surfaces with spherical metrics and conical singularities.
method Geometric triangulations and irreducible components of standard shapes.
result Spherical polygons, including half-spherical concave polygons, can be arbitrarily complicated.

We discuss a possible definition for "kk-width" of both a closed dd-manifold MdM^d, and on embedding MdeRnM^d \overset{e}{\hookrightarrow} \mathbb{R}^n, n>dkn > d \ge k, generalizing the classical notion of width of a knot. We show that for every 3-manifold 2-width(M3)2(M^3) \le 2 but that there are embeddings $e_i: T^3 \hoo…

2019-07-30abs ↗pdf ↗

Study spherical curves with curvature dependent on distance to a great circle.

problem Understanding spherical curves with curvature dependent on distance to a great circle.
method Introducing spherical angular momentum, characterizing known curves, finding new families, and obtaining arc length parametrizations.
result New families of spherical curves with intrinsic equations in elementary or Jacobi elliptic functions.

Study calculates eigenvalues and eigenfunctions for spherical triangles and finds fundamental gap behavior.

problem Understanding eigenvalues and gaps in spherical triangles.
method Explicit computation of Dirichlet eigenvalues and eigenfunctions for spherical lunes and triangles.
result Fundamental gap of spherical triangles increases as the angle of the lune decreases.

We extend the classical definition of {\it width} to higher dimensional, smooth codimension 2 knots and show in each dimension there are knots of arbitrarily large width.

2019-02-19abs ↗pdf ↗

Self-attention models benefit equally from width and depth, but beyond a certain point, depth becomes less efficient.

problem Understanding the optimal balance between depth and width in self-attention models.
method Theoretical predictions and empirical ablations on networks of varying depths and widths.
result An optimal width of 30K is recommended for a 1-Trillion parameter network, marking a significant width for self-attention models.