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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 transformed spaces

Study on conformal transformations of Cahen-Wallach spaces, focusing on fixed points and discontinuous groups.

problem Characterizing conformal transformations of Cahen-Wallach spaces.
method Analyzing conformal transformations of indecomposable Lorentzian symmetric spaces, focusing on fixed points and discontinuous groups.
result Essential conformal transformations of conformally curved Cahen-Wallach spaces have fixed points, and such transformations cannot centralize essential homotheties.

A real valued function φ\varphi of one variable is called a metric transform if for every metric space (X,d)(X,d) the composition dφ=φdd_\varphi = \varphi\circ d is also a metric on XX. We give a complete characterization of the class of approximately nondecreasing, unbounded metric transforms φ\varphi such that the trans…

2017-10-13abs ↗pdf ↗

New proof of injectivity for broken non-abelian X-ray transform in Minkowski space.

problem Injectivity of broken non-abelian X-ray transform in Minkowski space.
method Stability estimate considering gauge, light-sink connections, Bayesian inversion.
result Consistent recovery of light-sink connections from noisy data.

In this paper we study a symmetry group of vector space. Basis manifold is a homogeneous space of a symmetry group. This concept leads us to the definition of active and passive transformations on basis manifold. Active transformation can be expressed as a transformation of vector space. Passive transformation gives ab…

2004-12-20abs ↗pdf ↗

Calculates affine transformations for specific homogeneous spaces.

problem Computing groups of affine transformations on homogeneous spaces.
method Analyzes conditions for affine connections and uses them to establish group isomorphisms.
result Groups of affine transformations are locally isomorphic under specified conditions.

We study several problems concerning conformal transformation on metric measure spaces, including the Sobolev space, the differential structure and the curvature-dimension condition under conformal transformations. This is the first result about preservation of lower curvature bounds under perturbation, which is new ev…

2015-11-10abs ↗pdf ↗

In this paper we prove a new inversion theorem and a refinement of an old support theorem for two Radon transforms on a symmetric space. Included are some new identities for the Abel transform and some results about the Fourier transform from a joint work with Rawat, Sengupta and Sitaram.

2005-06-02abs ↗pdf ↗

The paper studies the Poisson transform of differential forms on hyperbolic spaces.

problem Analyzing the Poisson transform of differential forms on real hyperbolic spaces.
method Proving the Poisson transform is a topological isomorphism between boundary forms and eigenforms.
result The Poisson transform is a topological isomorphism for LrL^r-differential forms on the boundary of hyperbolic spaces.

Paper proves injectivity of non-abelian X-ray transform on certain spaces.

problem Injectivity of non-abelian X-ray transform on asymptotically hyperbolic spaces.
method Gauge equivalence for unitary connections and skew-Hermitian Higgs fields.
result Injectivity result for non-abelian X-ray transform over skew-Hermitian Higgs fields.

Study fundamental groups of geometric transformation groups using loop spaces.

problem Understanding fundamental groups of geometric transformation groups.
method Use differential forms on loop spaces to prove infinite fundamental groups.
result Proves infinite fundamental groups for specific geometric transformation groups.

Study on Bayesian transformers finds issues with weight-space inference and prior specification.

problem Challenges in obtaining meaningful uncertainty estimates for transformer models.
method Proposed a novel method based on implicit reparameterization of the Dirichlet distribution for variational inference on attention weights.
result Proposed method performs competitively with baselines in estimating predictive uncertainty.

Continuum transformers learn operators in context via gradient descent.

problem Generalizing transformers to handle infinite-dimensional inputs for in-context learning.
method Gradient descent in an operator RKHS, leveraging generalized representer theorems and gradient flows.
result Operator learned in context is Bayes Optimal Predictor in infinite depth limit.

Transformers are explained as infinite-dimensional kernel machines.

problem Understanding the mechanics of Transformers in AI.
method Characterized Transformers' attention mechanism as a kernel learning method on Banach spaces.
result Transformer's kernel has infinite feature dimension and can learn any binary non-Mercer reproducing kernel Banach space pair.

This paper analyzes kNN convergence over feature transformations.

problem The curse of dimensionality affects kNN performance in transformed feature spaces.
method Developed a novel analysis on kNN convergence rates over transformed features, linking properties of the transformed space to raw feature space.
result Theoretical analysis explains why some feature transformations are better for kNN.

Classification of Finslerian spaces with nontrivial concircular transformations.

problem Classifying Finslerian spaces with nontrivial concircular transformations.
method Proving the existence of at most two critical points in a conformal circle-preserving transformation and presenting a diffeomorphism classification based on these critical points.
result Presented a diffeomorphism classification of Finslerian manifolds that admit nontrivial conformal circle-preserving transformations.

Canonical transformation plays a fundamental role in simplifying and solving classical Hamiltonian systems. We construct flexible and powerful canonical transformations as generative models using symplectic neural networks. The model transforms physical variables towards a latent representation with an independent harm…

2019-09-30abs ↗pdf ↗

Proves conditions for Fourier transforms in rank 1 symmetric spaces.

problem Understanding Fourier transform bounds in symmetric spaces.
method Proves sufficient and necessary conditions using Lipschitz and Fourier type integral conditions.
result Establishes bounds for Fourier transforms in rank 1 symmetric spaces with specific moduli of continuity.

Unified proof of end-point estimates for Radon transform on curved spaces.

problem Proving end-point estimates for the totally-geodesic Radon transform on spaces of constant curvature.
method Unified geometric approach to prove end-point estimates for Radon transform on spaces of constant curvature.
result Unified formula for the kk-plane transform of radial functions on spaces of constant curvature.

State spaces of multifactor approximations of nonnegative Volterra processes are linear transformations of the nonnegative orthant.

problem Characterizing state spaces of multifactor approximations of nonnegative Volterra processes.
method Explicit linear transformation of the nonnegative orthant.
result State spaces of multifactor approximations of nonnegative Volterra processes are given by explicit linear transformation of the nonnegative orthant.

Researchers found a Calabi-Yau structure and constructed a Bargmann type transformation on the Cayley projective plane.

problem Existence of Calabi-Yau structure and construction of Bargmann type transformation.
method Pairing of polarizations, natural Lagrangian foliation, and Kähler structure.
result Quantization of geodesic flow through elliptic Fourier integral operators.

Efficient algorithms find optimal monotone transforms for calibration under strictly convex losses.

problem Calibrating estimations to improve performance with monotone transforms.
method Proposed linear-time and space algorithm for finding optimal monotone transforms for specific loss functions. Also proposed an anytime algorithm with linear space and pseudo-linearithmic time complexity.
result Optimal monotone transforms are unique and can be found efficiently for various strictly convex loss functions.

The paper provides convergence guarantees for ODE-based generative models using transformers.

problem Theoretical guarantees for ODE-based generative models.
method A pre-trained autoencoder maps inputs to a latent space, and a transformer predicts the velocity field.
result The distribution of samples generated via estimated ODE flow converges to the target distribution in Wasserstein-2 distance.

In this paper, we study the long existence problem of non Berwaldian Landsberg spaces using the conformal transformation point of view. Under conformal transformation, the Berwald and Landesberg tensors are calculated in terms of the T-tensor. By giving examples, we show that under conformal transformation, there are L…

2018-01-26abs ↗pdf ↗

Paper shows invertibility of tensor X-ray transform on certain manifolds.

problem Invertibility of tensor X-ray transform on asymptotically conic manifolds.
method Used 1-cusp pseudodifferential operator algebra and modified solenoidal gauge condition.
result Invertibility of tensor X-ray transform up to natural obstruction.

Noting the importance of the latent variables in inference and learning, we propose a novel framework for autoencoders based on the homeomorphic transformation of latent variables, which could reduce the distance between vectors in the transformed space, while preserving the topological properties of the original space…

2019-01-24abs ↗pdf ↗

New minimal hypersurfaces found via transformations.

problem Finding new axially symmetric minimal hypersurfaces in 4D Minkowski space.
method Combining scaling symmetries and a non-obvious symmetry (analogous to Bianchi's transformation) to generate new hypersurfaces.
result Infinitely many axially symmetric minimal hypersurfaces can be generated from any given one.

Unified method for deriving ridgelet transforms for various neural network architectures.

problem Deriving closed-form expressions for ridgelet transforms in modern neural network architectures.
method Unified Fourier slice method to derive ridgelet transforms for diverse neural network types.
result Systematic method to derive ridgelet transforms for various neural network architectures.

We interpret the setting for a Radon transform as a submanifold of the space of generalized functions, and compute its extrinsic curvature: it is the Hessian composed with the Radon transform.

1992-10-01abs ↗pdf ↗

This article gives a study of the higher-dimensional Penrose transform between conformally invariant massless fields on space-time and cohomology classes on twistor space, where twistor space is defined to be the space of projective pure spinors of the conformal group. We focus on the 6-dimensional case in which twisto…

2011-11-10abs ↗pdf ↗