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.

168,657 papers · 148 categories

Trend · papers per month

69138206275 · May 202619922001200920172026
48 results for Intrinsic mean

New surfaces in Lorentz-Minkowski space with constant mean curvature identified.

problem Identifying surfaces with constant mean curvature in Lorentz-Minkowski space.
method Using a specific coordinate system and properties of the Weingarten endomorphism, the mean curvature is shown to be constant under certain conditions.
result Constant mean curvature surfaces identified, including spacelike and timelike Enneper surfaces.

In this article, we study constant mean curvature isometric immersions into S2×R\mathbb{S}^2 \times \mathbb{R} and H2×R\mathbb{H}^2 \times \mathbb{R} and we classify these isometric immersions when the surface has constant intrinsic curvature. As applications, we use the sister surface correspondence to classify the consta…

2019-11-28abs ↗pdf ↗

The paper corrects biases in estimating intrinsic dimension and differential entropy.

problem Systematic bias in estimating intrinsic dimension and differential entropy.
method A bias-corrected estimator for both measures is proposed, highlighting shared steps and useful consequences.
result Simultaneous estimation of differential entropy and intrinsic dimension provides complementary perspectives on underlying manifolds.

The paper characterizes biconservative surfaces in hyperbolic 4-space.

problem Characterizing biconservative surfaces in hyperbolic 4-space.
method Establishing intrinsic conditions and analyzing geometric properties.
result Biconservative surfaces in hyperbolic 4-space satisfy a specific intrinsic condition.

The mathematical problem concerning intrinsic storage optimisation is formulated and solved by means of variational analysis. The solution, though obtained in implicit form, still sheds light on many important features of the optimal exercise strategy. It is shown how the solution depends on different constraint types …

2015-06-22abs ↗pdf ↗

New Ricci curvature means derived from plane curvatures.

problem Understanding Ricci curvature in geometric contexts.
method Introducing intrinsic and normal mean Ricci curvatures via Jacobi-field expansions and applying Bochner-Weitzenboeck identity.
result Derives a Bochner-Weitzenboeck identity for simple d-vectors.

For submanifolds tangent to the structure vector field in cosymplectic space forms, we establish a basic inequality between the main intrinsic invariants of the submanifold, namely its sectional curvature and scalar curvature on one side; and its main extrinsic invariant, namely squared mean curvature on the other side…

2002-06-04abs ↗pdf ↗

Market activity scales near a constant of 0.632 in intrinsic time.

problem Understanding the stability of market scaling laws.
method Modeling market directional changes as a memoryless exponential hazard process and identifying the intrinsic time scaling constant.
result The intrinsic time scaling constant is 11/e=0.6321 - 1/e = 0.632.

Study uniformly differentiable graphs in Carnot groups, proving area formulas.

problem Characterize uniformly differentiable intrinsic graphs in Carnot groups.
method Characterize uniform intrinsic differentiability via Hölder properties of projections of vector fields.
result Explicit area formula for uniformly intrinsically differentiable maps in Carnot groups.

Diffusion means converge to extrinsic means for long times on spheres.

problem Understanding the long-time behavior of diffusion means on manifolds.
method Introduced diffusion means as a parameterized family of location statistics on manifolds, and analyzed their convergence to extrinsic means for long times.
result For real projective spaces and connected compact symmetric spaces, the long-time limit of diffusion means is conjectured to be the extrinsic mean in the isometric embedding.

Study of intrinsic sub-Laplacian for hypersurfaces in contact sub-Riemannian manifolds.

problem Characterizing the intrinsic sub-Laplacian for hypersurfaces in contact sub-Riemannian manifolds.
method Construction and analysis of the intrinsic sub-Laplacian using Riemannian approximations and stochastic processes.
result The intrinsic sub-Laplacian is stochastically complete, ensuring the process does not hit characteristic points.

The study proves a theorem for surfaces using Codazzi operators and investigates parallel mean curvature surfaces.

problem Understanding surfaces with parallel mean curvature in product spaces.
method Intrinsic Klotz-Osserman theorem and Simons' formula.
result The existence of surfaces with parallel mean curvature in product spaces with non-positive Gaussian curvature.

Sharp estimates for mean curvature flow confirm bounded diameter conjecture.

problem Bounding the diameter of mean curvature flow in 3D.
method Quantitative estimates on second fundamental form and singular set.
result Uniform boundedness of intrinsic diameter and sharp estimates on flow properties.

In this paper we prove an extrinsic one-sided curvature estimate for disks embedded in R3\mathbb{R}^3 with constant mean curvature which is independent of the value of the constant mean curvature. We apply this extrinsic one-sided curvature estimate in [24] to prove to prove a weak chord arc type result for these disks…

2014-08-22abs ↗pdf ↗

Study of Brown--York mass for four-dimensional asymptotically flat manifolds.

problem Calculating mass for hypersurfaces in four-dimensional asymptotically flat manifolds.
method Intrinsic definition of mean curvature, expansion analysis for large uniformly convex hypersurfaces.
result Shape-dependent correction to ADM mass for nearly round surfaces vanishes under certain conditions.

We study SU(3)-structures induced on orientable hypersurfaces of seven-dimensional manifolds with G_2-structure. Taking Gray-Hervella types for both structures into account, we relate the type of SU(3)-structure and the type of G_2-structure with the shape tensor of the hypersurface. Additionaly, we show how to compute…

2004-10-28abs ↗pdf ↗

Develop intrinsic consensus-based optimization framework on Riemannian manifolds with bounded curvature.

problem Nonconvex optimization on manifolds
method Intrinsic consensus-based optimization on Riemannian manifolds with bounded curvature
result Global convergence of the mean-field equation toward a global minimizer of the objective function.

We study the rigidity of complete, embedded constant mean curvature surfaces in R^3. Among other things, we prove that when such a surface has finite genus, then intrinsic isometries of the surface extend to isometries of R^3 or its isometry group contains an index two subgroup of isometries that extend.

2008-01-22abs ↗pdf ↗

We prove that for the mean curvature flow of two-convex hypersurfaces the intrinsic diameter stays uniformly controlled as one approaches the first singular time. We also derive sharp Ln1L^{n-1}-estimates for the regularity scale of the level set flow with two-convex initial data. Our proof relies on a detailed analysis…

2017-10-27abs ↗pdf ↗

eDCF estimates intrinsic dimension using local connectivity.

problem Challenges in estimating intrinsic dimension due to scale dependence.
method eDCF: a novel, scalable, and parallelizable method based on Connectivity Factor (CF).
result eDCF consistently matches leading estimators with comparable MAE and higher exact intrinsic dimension match rates.

Deep networks can approximate high-dimensional distributions from low-dimensional ones.

problem Approximating high-dimensional distributions from low-dimensional ones.
method Proved neural networks can transform low-dimensional distributions to high-dimensional ones with arbitrary closeness measured by Wasserstein distances and maximum mean discrepancy.
result Upper bounds of the approximation error are obtained in terms of the width and depth of neural network.

We first partially extend a theorem of Topping, on the relation between mean curvature and intrinsic diameter, from immersed submanifolds of Rn\mathbb{R} ^{n} to almost everywhere immersed, closed submanifolds of a compact Riemannian manifold. We use this to prove quantization of energy for pseudo-holomorphic closed c…

2019-02-05abs ↗pdf ↗

Develop a framework for barycentric projections of optimal transport plans on Riemannian manifolds.

problem Optimal transport couplings are probabilistic objects, while many learning pipelines require deterministic maps.
method Develop a framework for barycentric projections of transport couplings on Riemannian manifolds.
result The intrinsic projection maps each source point to the conditional Fréchet mean of its destination law and is shown to be the best deterministic representative under squared geodesic loss.

We give new estimates for the eigenvalues of the hypersurface Dirac operator in terms of the intrinsic energy-momentum tensor, the mean curvature and the scalar curvature. We also discuss their limiting cases as well as the limiting cases of the estimates obtained by X. Zhang and O. Hijazi in [13] and [10]. We compare …

2001-01-12abs ↗pdf ↗

We present an intrinsic formulation of the kinematic problem of two nn-dimensional manifolds rolling one on another without twisting or slipping. We determine the configuration space of the system, which is an n(n+3)2\frac{n(n+3)}2-dimensional manifold. The conditions of no-twisting and no-slipping are decoded by means of …

2010-08-11abs ↗pdf ↗

Study relationships between intrinsic and extrinsic invariants of Riemannian almost k-product manifolds.

problem Find a relationship between intrinsic and extrinsic invariants of Riemannian almost k-product manifolds isometrically immersed in another Riemannian manifold.
method Establish an optimal inequality involving mixed scalar curvature and square of mean curvature.
result Optimal inequality that includes mixed scalar curvature and square of mean curvature.

Scaling laws found for reinforcement learning performance with model size and compute.

problem Challenges in extending generative modeling scaling laws to reinforcement learning.
method Introduced intrinsic performance as a monotonic function of mean episode return.
result Intrinsic performance scales as a power law in model size and environment interactions.

The Wintgen inequality (1979) is a sharp geometric inequality for surfaces in the 4-dimensional Euclidean space involving the Gauss curvature (intrinsic invariant) and the normal curvature and squared mean curvature (extrinsic invariants), respectively. In the present paper we obtain a Wintgen inequality for statistica…

2015-11-16abs ↗pdf ↗

In this paper, geometric characterizations of conformally flat and radially flat hypersurfaces in Sn×R\mathbb{S}^n \times \mathbb{R} and Hn×R\mathbb{H}^n \times \mathbb{R} are given by means of their extrinsic geometry. Under suitable conditions on the shape operator, we classify conformally flat hypersurfaces in terms of …

2017-04-16abs ↗pdf ↗

We give a relationship that yields an effective geometric way of evaluating mean curvature of surfaces. The approach is reminiscent of the Gauss's contour based evaluation of intrinsic curvature. The presented formula may have a number of potential applications including estimating the normal vector and mean curvature …

2011-08-07abs ↗pdf ↗

Given a constant mean curvature surface that bounds a compact manifold with nonnegative scalar curvature, we obtain intrinsic conditions on the surface that guarantee the positivity of its Hawking mass. We also obtain estimates of the Bartnik mass of such surfaces, without assumptions on the integral of the squared mea…

2018-09-11abs ↗pdf ↗

In this paper we consider a set EΩE\subsetΩ with prescribed mean curvature fC(Ω)f\in C(Ω) and Euclidean Lipschitz boundary E=Σ\partial E=Σ inside a three-dimensional contact sub-Riemannian manifold MM. We prove that if ΣΣ is locally a regular intrinsic graph, the characteristic curves are of class C2C^2. The result is sh…

2015-07-26abs ↗pdf ↗

The focus of this paper is on intrinsic methods to detect overfitting. By intrinsic methods, we mean methods that rely only on the model and the training data, as opposed to traditional methods (we call them extrinsic methods) that rely on performance on a test set or on bounds from model complexity. We propose a famil…

2019-07-03abs ↗pdf ↗

The paper analyzes Karcher means on restricted PSD matrices with statistical guarantees.

problem Statistical analysis of non-linear manifolds in machine learning.
method Intrinsic mean model on restricted PSD matrices, Karcher mean analysis, extrinsic signal-plus-noise model.
result Non-asymptotic statistical analysis of Karcher means with deterministic error bounds.

Study on predicting sequences with Gaussian constraints, linking to intrinsic volumes and metric complexity.

problem Predicting sequences almost as well as the best Gaussian distribution with mean in a given subset.
method Expressed minimax regret in terms of intrinsic volumes, established comparison inequality for Wills functional, characterized global covering numbers and local Gaussian widths.
result Sharp estimates on the log-Laplace transform of intrinsic volume sequence for a general nonconvex set.

Paper introduces new bounds linking data compressibility to generalization error.

problem Establishing data-dependent generalization bounds.
method Variable-size compressibility framework linking generalization error to compression rate of input data.
result New bounds depend on empirical data measure, subsuming existing PAC-Bayes and intrinsic dimension bounds.