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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.

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2579 · Feb 202419922001200920172026
48 results for versal unfoldings

Study on unfolding maps of surfaces in 3D space, proving versality conditions.

problem Investigating the versality of rotation unfolding of folding maps for surfaces in R3\mathbb{R}^3.
method Introducing and analyzing the rotation unfolding of folding maps, proving versality conditions in terms of geometry.
result Proved conditions for the rotation unfolding to be versal, showing diffeomorphic type of tangent plane locus.

We study the singularities of the members of the family of height functions on Whitney umbrellas, which is also known as cross-caps, and show that the family of the height functions is a versal unfolding. Moreover, we study local intersections of a Whitney umbrella with a hyperplane through its singular point.

2012-05-14abs ↗pdf ↗

The paper generalizes envelope constructions for chords in circles, revealing complex singularities.

problem Understanding envelopes of chords in circles with varying parameters and configurations.
method Generalizing the embroidery method to rational and concentric circles, breaking symmetry to reveal higher singularities.
result Higher singularities like swallowtails and butterflies can be unfolded, revealing their structure.

We derive simple forms for saddle-node singular points of analytic foliations in the real or complex plane just by gluing foliated complex manifolds. We give the versal analytic deformation of the simplest model. We also derive a unique analytic form for those saddle-node having a central manifold. By this way, we reco…

2004-03-03abs ↗pdf ↗

Researchers describe how special conic bundles deform into double solids.

problem Understanding the versal deformation of conic bundles over 3CP23\mathbb{C}\mathbb{P}^2.
method Explicit description of deformation in a general context.
result Explicit description of the deformation of conic bundles into double solids.

In this paper, we discuss the recognition problem for A_k-type singularities on wave fronts. We give computable and simple criteria of these singularities, which will play a fundamental role in generalizing the authors' previous work "the geometry of fronts" for surfaces. The crucial point to prove our criteria for A_k…

2008-04-04abs ↗pdf ↗

Perfect pairing for tropical cycles on integral affine manifolds.

problem Computing period integrals and versality of Calabi-Yau degenerations.
method Introducing a cap product pairing and using simplicial methods for constructible sheaves.
result The pairing is perfect in degree one for symplectic singularities.

Let π:MBπ:M\to B be a proper holomorphic submersion between complex manifolds and E{\cal E} a holomorphic bundle on MM. We study and describe explicitly the torsion subsheaf Tors(R1π(E))\mathrm{Tors}(R^1π_*({\cal E})) of the first direct image R1π(E)R^1π_*(\mathcal{E}) under the assumption R0π(E)=0R^0π_*(\mathcal{E})=0. We give two applic…

2013-09-02abs ↗pdf ↗

New method for unbinned, profiled unfolding in particle physics.

problem Traditional unfolding methods are limited in the number of unfolded variables and cannot profile nuisance parameters.
method Proposes a machine learning-based method that allows for unbinned differential cross sections and profiles nuisance parameters.
result Demonstrates the method with Gaussian examples and a simulated Higgs boson cross section measurement.

OmniFold simultaneously unfolds all observables using machine learning.

problem Traditional unfolding methods are limited to individual observables and do not incorporate all detector information.
method OmniFold iteratively reweights a simulated dataset using machine learning to handle all available information.
result OmniFold enables the simultaneous measurement of all observables, including those not yet invented.

Study families of Lie algebroids on complex spaces, introducing unfoldings.

problem Investigate singular holomorphic Lie algebroids on complex analytic spaces.
method Introduce and study unfoldings of Lie algebroids, showing a correspondence with holomorphic flat connections.
result Existence of a one-to-one correspondence between transversal unfoldings and holomorphic flat connections.

Given an SL(3)SL(3) spectral curve over a simply connected Riemann surface, we describe in detail the reduction steps necessary to construct the core of a pre-building with versal harmonic map whose differential is given by the spectral curve.

2016-11-26abs ↗pdf ↗

The paper studies phase transitions in random matrices and tensor unfolding for detecting signals.

problem Phase transitions in singular values and vectors of large random matrices.
method Analysis of singular values and vectors of long rectangular random matrices, and tensor unfolding algorithm for asymmetric rank-one spiked tensor models.
result An exact threshold for tensor unfolding to detect signals, independent of unfolding procedure.

We show that every convex polyhedron admits a simple edge unfolding after an affine transformation. In particular there exists no combinatorial obstruction to a positive resolution of Durer's unfoldability problem, which answers a question of Croft, Falconer, and Guy. Among other techniques, the proof employs a topolog…

2013-05-14abs ↗pdf ↗

A simple self-supervised model for tensor RPCA using deep unfolding.

problem Tensor robust principal component analysis (RPCA) challenges in practical applications.
method Deep unfolding with only four hyperparameters.
result Competitive or superior performance compared to supervised methods, even in data-starved scenarios.

A new machine learning method handles nuisance parameters for better unfolding in particle physics.

problem Improving statistical correction of cross sections in complex particle physics detectors.
method Profile OmniFold, a machine learning-based Expectation-Maximization procedure that incorporates nuisance parameters.
result Demonstrated the effectiveness of Profile OmniFold on both simulated and real data.

In a previous work we proved the uniqueness and functoriality of primary unfoldings on simple Thom-Mather spaces, which is a functor to the category of smooth manifolds. In this article we extend these results for any stratified Thom-Mather pseudomanifold with arbitary finite length, through a new kind of intermediate …

2009-10-04abs ↗pdf ↗

A pseudo-edge graph of a convex polyhedron K is a 3-connected embedded graph in K whose vertices coincide with those of K, whose edges are distance minimizing geodesics, and whose faces are convex. We construct a convex polyhedron K in Euclidean 3-space with a pseudo-edge graph with respect to which K is not unfoldable…

2017-09-14abs ↗pdf ↗

Unified framework for spectral methods, kernel learning, and manifold unfolding.

problem Tackles the unification and optimization of spectral dimensionality reduction methods.
method Unified spectral methods as kernel PCA, kernel learning by SDP, and detailed explanation of MVU variants.
result Unified understanding and optimization of manifold learning techniques.

This is mainly a survey article on the recent development of the theory of graph-like Legendrian unfoldings and its applications. The notion of big Legendrian submanifolds was introduced by Zakalyukin for describing the wave front propagations. Graph-like Legendrian unfoldings belong to a special class of big Legendria…

2014-10-31abs ↗pdf ↗

Deep unfolding accelerates MCMC-based COP solvers.

problem Optimizing combinatorial problems with MCMC and gradient descent.
method Combines MCMC and gradient descent, trains step sizes, uses variance estimation for non-differentiable MCMC.
result Significantly accelerates convergence speed for COPs.

Chebyshev steps improve convergence in deep-unfolded gradient descent.

problem Improving convergence speed in iterative algorithms.
method Introducing Chebyshev steps to bound convergence rate of gradient descent.
result Chebyshev steps lead to asymptotically optimal convergence rate.

In this paper, we introduce the notions of map-germs of pedal unfolding type and normalized Legendrian map-germs; and then we show that the fundamental theorem of calculus provides a natural one to one correspondence between Whitney umbrellas of pedal unfolding type and normalized swallowtails.

2011-12-21abs ↗pdf ↗

ULES embeds dynamic networks with stability guarantees.

problem Stability of time-varying node embeddings in evolving networks.
method Unfolded Laplacian Spectral Embedding (ULSE) using normalized Laplacian operators.
result ULES satisfies cross-sectional and longitudinal stability under dynamic stochastic block model.

Branched covers are applied frequently in topology - most prominently in the construction of closed oriented PL d-manifolds. In particular, strong bounds for the number of sheets and the topology of the branching set are known for dimension d<=4. On the other hand, Izmestiev and Joswig described how to obtain a simplic…

2007-07-10abs ↗pdf ↗

Maximum Variance Unfolding is one of the main methods for (nonlinear) dimensionality reduction. We study its large sample limit, providing specific rates of convergence under standard assumptions. We find that it is consistent when the underlying submanifold is isometric to a convex subset, and we provide some simple e…

2012-08-31abs ↗pdf ↗

The simplest non-trivial solutions of WDVV equations are A_n and B_n-potentials, which describe metrics of K.Saito on spaces of versal deformation of A_n and B_n-singularities. These are some polynomials, which were known for nn\leqslant 4. We find some recurrence relations, which give a possibility to find all A_n an…

1999-04-14abs ↗pdf ↗

New method uses Wasserstein loss for data unfolding, offering better accuracy than classical techniques.

problem Removing noise or artifacts from measurements in physics experiments.
method Alternative formulation using Wasserstein loss, developing a convergent algorithm.
result Optimal transport approach offers robust, accurate performance compared to classical techniques, especially in cases with significant binning artifacts.

A procedure for unfolding the true distribution from experimental data is presented. Machine learning methods are applied for simultaneous identification of an apparatus function and solving of an inverse problem. A priori information about the true distribution from theory or previous experiments is used for Monte-Car…

2010-04-12abs ↗pdf ↗

This paper develops a novel deep recurrent neural network for sequential signal reconstruction.

problem Sequential signal reconstruction from low-dimensional measurements.
method Unfolding a reweighted 1\ell_1-1\ell_1 minimization algorithm to design a deep recurrent neural network.
result The proposed reweighted-RNN significantly outperforms existing RNN models in sequential frame reconstruction.

A new method for unfolding histograms without matrix inversion.

problem Matrix inversion in experimental physics, especially in high-energy particle physics.
method Sampling many distributions, folding them through the response matrix, and choosing the closest one to the data.
result Performs as well as traditional methods in well-defined inverse problems and outperforms them in ill-defined ones.