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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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48 results for translation distance

This paper studies Heegaard splittings of surface bundles via the curve complex of the fibre. The translation distance of the monodromy is the smallest distance it moves any vertex of the curve complex. We prove that the translation distance is bounded above in terms of the genus of any strongly irreducible Heegaard sp…

2002-12-06abs ↗pdf ↗

Given MφM_\varphi, a fibered 3-manifold with boundary, we show that the translation distance of the monodromy φ\varphi can be bounded above by the complexity of an essential surface with non-zero slope. Furthermore we prove that the minimal complexity of a surface with non-zero slope in MφnM_{\varphi^n} tends to infini…

2019-02-18abs ↗pdf ↗

High dimensional structured data such as text and images is often poorly understood and misrepresented in statistical modeling. The standard histogram representation suffers from high variance and performs poorly in general. We explore novel connections between statistical translation, heat kernels on manifolds and gra…

2012-06-20abs ↗pdf ↗

ATD measures language distance using neural models, recovering linguistic groupings.

problem Lack of a unified quantitative measure for cross-linguistic distance.
method Pretrained multilingual language models, attention mechanisms, optimal transport.
result ATD quantifies representational distance between languages, recovering linguistic groupings.

Given a closed hyperbolic 3-manifold T_ψthat fibers over the circle with monodromy ψ: S -> S, the monodromy ψψ determines an isometry of Teichmuller space with its Weil-Petersson metric whose translation distance ||ψ||_WP is positive. We show there is a constant K >= 1 depending only on the topology of S so that the v…

2001-09-07abs ↗pdf ↗

Generating point clouds, e.g., molecular structures, in arbitrary rotations, translations, and enumerations remains a challenging task. Meanwhile, neural networks utilizing symmetry invariant layers have been shown to be able to optimize their training objective in a data-efficient way. In this spirit, we present an ar…

2019-10-07abs ↗pdf ↗

The study defines Finsler metrics on special surfaces and constructs geodesic currents.

problem Defining and studying Finsler metrics on specific geometric structures.
method Defined compatible Finsler distances, studied geodesics, and constructed Liouville currents.
result Constructs a Liouville current for each metric, encoding curve lengths.

Random walks on hyperbolic spaces follow predictable large deviation principles.

problem Understanding the behavior of random walks on hyperbolic spaces.
method Large deviation principles for displacement and translation distances.
result Translation and displacement distances satisfy large deviation principles with the same rate function.

For any pseudo-Anosov diffeomorphism on a closed orientable surface SS of genus greater than one, it is known by the work of Bers and Thurston that the topological entropy agrees with the translation distance on the Teichmüller space with respect to the Teichmüller metric. In this paper, we consider random walks on th…

2016-04-04abs ↗pdf ↗

An isometry of a Finsler space is called Clifford-Wolf translation (CW-translation) if it moves all points the same distance. A Finsler space (M,F)(M, F) is called Clifford-Wolf homogeneous (CW-homogeneous) if for any x,yMx, y\in M there is a CW-translation σσ such that σ(x)=yσ(x)=y. We prove that if FF is a homogeneous Finsl…

2013-12-03abs ↗pdf ↗

A Clifford-Wolf translation of a connected Finsler space is an isometry which moves each point the same distance. A Finsler space (M,F)(M, F) is called Clifford-Wolf homogeneous if for any two points x1,x2Mx_1, x_2\in M there is a Clifford-Wolf translation ρρ such that ρ(x1)=x2ρ(x_1)=x_2. In this paper, we give a complete classifi…

2012-06-14abs ↗pdf ↗

This paper develops optimal transport methods on the roto-translation group SE2.

problem Optimal transport on the roto-translation group SE2 for image analysis.
method Develops a computational framework for optimal transportation over Lie groups, focusing on SE2. Uses Sinkhorn-like algorithm with efficient distance approximations.
result Advances in image barycentric interpolation, orientation field interpolation, and Wasserstein flows on SE2.

For finitely generated groups GG and HH equipped with word metrics, a translation-like action of HH on GG is a free action where each element of HH moves elements of GG a bounded distance. Translation-like actions provide a geometric generalization of subgroup containment. Extending work of Cohen, we show that co…

2019-11-27abs ↗pdf ↗

A Clifford-Wolf translation of a connected Finsler space is an isometry which moves each point the sam distance. A Finsler space (M,F)(M, F) is called Clifford-Wolf homogeneous if for any two point x1,x2Mx_1, x_2\in M there is a Clifford-Wolf translation ρρ such that ρ(x1)=x2ρ(x_1)=x_2. In this paper, we study Clifford-Wolf transl…

2012-04-23abs ↗pdf ↗

This work aims to improve semi-supervised learning in a neural network architecture by introducing a hybrid supervised and unsupervised cost function. The unsupervised component is trained using a differentiable estimator of the Maximum Mean Discrepancy (MMD) distance between the network output and the target dataset. …

2018-10-28abs ↗pdf ↗

Let (M,F)(M,F) be a connected Finsler space and dd the distance function of (M,F)(M,F). A Clifford translation is an isometry ρρ of (M,F)(M,F) of constant displacement, in other words such that d(x,ρ(x))d(x,ρ(x)) is a constant function on MM. In this paper we consider a connected simply connected symmetric Finsler space and a discr…

2012-06-16abs ↗pdf ↗

Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning. However, to model high-dimensional distributions, sequential training and stacked architectures …

2019-04-11abs ↗pdf ↗

Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as multi-domain image translation. However, addressing this problem has two critical challenges: (i) Measuring the multi-marginal distance amon…

2019-11-03abs ↗pdf ↗

We show that if the monodromy of a 3-manifold M that fibers over the circle has large translation distance in the curve complex, then the rank of the fundamental group of M is 2g+1, where g is the genus of the fiber.

2014-09-05abs ↗pdf ↗

While it is well known from examples that no interesting `halfspace theorem' holds for properly immersed complete nn-dimensional self-translating mean curvature flow solitons in Euclidean space Rn+1\mathbb{R}^{n+1}, we show that they must all obey a general `bi-halfspace theorem': Two transverse vertical halfspaces can …

2018-09-04abs ↗pdf ↗

We propose a geometric method for quantifying the difference between parametrized curves in Euclidean space by introducing a distance function on the space of parametrized curves up to rigid transformations (rotations and translations). Given two curves, the distance between them is defined as the infimum of an energy …

2014-01-20abs ↗pdf ↗

LightSBB-M improves generative diffusion modeling with lower 2-Wasserstein distances.

problem Improving generative diffusion models using Schrödinger Bridge and Bass methods.
method Optimizes SBB transport plan with dual representation and tunable beta parameter.
result Achieves up to 32% improvement in 2-Wasserstein distance on synthetic datasets.

We prove an effective version of a theorem relating curve complex distance to electric distance in hyperbolic 3-manifolds, up to errors that are polynomial in the complexity of the underlying surface. We use this to give an effective proof of a result regarding maps between curve complexes of surfaces induced by finite…

2018-10-30abs ↗pdf ↗

This paper improves MDS visualization by adjusting Wasserstein distances for heavy-tailed data.

problem Enhancing Multidimensional Scaling (MDS) for better pattern recognition with heavy-tailed distributions.
method Introduces Max-D-SW, a metric adjustment of Max-Sliced Wasserstein distance that aggregates over orthonormal bases.
result Max-D-SW provides a clear numerical advantage in MDS outcomes, especially for heavy-tailed distributions.

A method distills GANs for mobile devices, reducing computation and storage.

problem Heavy computation and storage cost of GANs on mobile devices.
method Knowledge distillation to train a smaller generator with inherited information from a larger teacher generator, including a discriminator.
result Portable GAN models with strong performance achieved.

We consider Lie groups equipped with arbitrary distances. We only assume that the distance is left-invariant and induces the manifold topology. For brevity, we call such object metric Lie groups. Apart from Riemannian Lie groups, distinguished examples are sub-Riemannian Lie groups and, in particular, Carnot groups equ…

2016-01-29abs ↗pdf ↗

We are concerned with unbounded sets of RN\mathbb{R}^N whose boundary has constant nonlocal (or fractional) mean curvature, which we call CNMC sets. This is the equation associated to critical points of the fractional perimeter functional under a volume constraint. We construct CNMC sets which are the countable union o…

2017-02-04abs ↗pdf ↗

The study determines Z2\mathbb{Z}_2-Thurston norms in Sol manifolds and embeds non-orientable surfaces.

problem Determining Z2\mathbb{Z}_2-Thurston norms in Sol manifolds and embedding non-orientable surfaces.
method Analyzing the action of torus maps on curve complexes and constructing incompressible surfaces.
result Determination of Z2\mathbb{Z}_2-Thurston norms and embeddability of non-orientable surfaces in Sol manifolds.