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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.

168,742 papers · 148 categories

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147294441588 · Jun 202019922001200920172026
48 results for even case

Derives stability for curvature measure near constant density, proving dual Minkowski problem solutions.

problem Stability of curvature measure near constant density
method Derives stability result for curvature measure, proves existence and uniqueness of solutions to dual Minkowski problem.
result Existence and uniqueness of solutions to dual Minkowski problem for positive indices, stability result for curvature measure.

The paper studies a flow equation on even-dimensional manifolds, proving convergence under critical conditions.

problem Proving convergence of the prescribed QQ-curvature flow equation in critical cases.
method Analyzes the flow equation on arbitrary even-dimensional closed Riemannian manifolds, proving convergence under specific geometric hypotheses.
result Proves convergence of the flow equation when the integral of QQ equals (n1)!Vol(Sn)(n-1)!Vol(S^n), extending previous results.

In this paper we introduce the twistor space of a Riemannian manifold with an even Clifford structure. This notion generalizes the twistor space of quaternion-Hermitian manifolds and weak-Spin(9) structures. We also construct almost complex structures on the twistor space for parallel even Clifford structures and check…

2016-02-12abs ↗pdf ↗

This paper refines previous work by the first author. We study the question of which links in the 3-sphere can be obtained as closures of a given 1-manifold in an unknotted solid torus in the 3-sphere (or genus-1 tangle) by adjoining another 1-manifold in the complementary solid torus. We distinguish between even and o…

2014-07-30abs ↗pdf ↗

A geometrical structure on even-dimensional manifolds is defined which generalizes the notion of a Calabi-Yau manifold and also a symplectic manifold. Such structures are of either odd or even type and can be transformed by the action of both diffeomorphisms and closed 2-forms. In the special case of six dimensions we …

2002-09-10abs ↗pdf ↗

Non-rigidity degree of a lattice LL, nrdLL, is dimension of the L-type domain to which LL belongs. We complete here the table of nrd's of all root lattices and their duals; namely, the hardest remaining case of DnD_n^*, and the case of E7E_7^* are decided. We describe explicitly the LL-type domain D(Dn){\cal D}(D_n^*)

2002-02-11abs ↗pdf ↗

AMP algorithms can be efficiently simulated by SDPs even with corrupted data.

problem Optimizing average-case optimization problems with corrupted data.
method Local statistics hierarchy semidefinite programs (SDPs) simulate AMP algorithms robustly.
result Robust guarantees for many AMP algorithms are offered, contrasting with strong lower bounds for SDPs.

We study an online learning framework introduced by Mannor and Shamir (2011) in which the feedback is specified by a graph, in a setting where the graph may vary from round to round and is \emph{never fully revealed} to the learner. We show a large gap between the adversarial and the stochastic cases. In the adversaria…

2016-05-23abs ↗pdf ↗

Researchers construct irreducible 4-manifolds with specific properties.

problem Creating smooth manifolds with specific topological and geometric properties.
method Equivariant fiber sums of Lefschetz fibrations and symplectic manifolds.
result Constructed irreducible manifolds with even intersection forms and specific topological invariants.

For any n>1 we determine the uniform and nonuniform lattices of the smallest covolume in the Lie group Sp(n,1). We explicitly describe them in terms of the ring of Hurwitz integers in the nonuniform case with n even, respectively, of the icosian ring in the uniform case for all n>1.

2018-02-21abs ↗pdf ↗

Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a …

2016-07-08abs ↗pdf ↗

Using a probabilistic argument we show that the second bounded cohomology of an acylindrically hyperbolic group GG (e.g., a non-elementary hyperbolic or relatively hyperbolic group, non-exceptional mapping class group, Out(Fn){\rm Out}(F_n), \dots) embeds via the natural restriction maps into the inverse limit of the secon…

2017-01-03abs ↗pdf ↗

Meta-learning enables a model to learn from very limited data to undertake a new task. In this paper, we study the general meta-learning with adversarial samples. We present a meta-learning algorithm, ADML (ADversarial Meta-Learner), which leverages clean and adversarial samples to optimize the initialization of a lear…

2018-06-08abs ↗pdf ↗

In previous work, we introduced eta invariants for even dimensional manifolds. It plays the same role as the eta invariant of Atiyah-Patodi-Singer, which is for odd dimensional manifolds. It is associated to K1K^1 representatives on even dimensional manifolds and is closely related to the so called WZW theory in physic…

2012-05-02abs ↗pdf ↗

We prove a formula relating the analytic torsion and Reidemeister torsion on manifolds with boundary in the general case when the metric is not necessarily a product near the boundary. The product case has been established by W. Luck and S. M. Vishik. We find that the extra term that comes in here in the nonproduct cas…

1999-01-12abs ↗pdf ↗

Given data drawn from an unknown distribution, DD, to what extent is it possible to ``amplify'' this dataset and output an even larger set of samples that appear to have been drawn from DD? We formalize this question as follows: an (n,m)(n,m) amplification procedure\text{amplification procedure} takes as input nn independent draws from an …

2019-04-26abs ↗pdf ↗

We provide an explicit formula for the Fefferman-Graham-ambient metric of an nn-dimensional conformal pppp-wave in those cases where it exists. In even dimensions we calculate the obstruction explicitly. Furthermore, we describe all 4-dimensional pppp-waves that are Bach-flat, and give a large class of Bach-flat examp…

2008-10-16abs ↗pdf ↗

We introduce a new geometric invariant called the obtuse constant of spaces with curvature bounded below. We first find relations between this invariant and the normalized volume. We also discuss the case of maximal obtuse constant equal to π/2π/2, where we prove some rigidity for spaces. Although we consider Alexandrov…

2017-10-02abs ↗pdf ↗

Study null geodesics on even-dimensional conformal manifolds, finding Einstein metrics and CR structures.

problem Investigate null geodesics and their geometric properties on conformal manifolds.
method Analyze the Weyl tensor and its effects on the geometry of null geodesic congruences.
result Find Einstein metrics and CR structures on the leaf space of null geodesic congruences.

Stochastic approximation extended to infinite dimensions, especially Banach spaces.

problem Applying stochastic approximation to infinite-dimensional spaces, particularly Banach spaces.
method Extending stochastic approximation to Banach spaces, including cases like C([0,1],Rd)C([0,1],\mathbb{R}^d) and L1([0,1],Rd)L^1([0,1],\mathbb{R}^d).
result Stochastic approximation can be applied to Banach spaces, including those without the Radon-Nikodym property.

This paper introduces a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The main advantage is that we only require a prior distribution on a class of simulators (generative models). This is useful in domains where an analytical probabilistic…

2013-03-27abs ↗pdf ↗

We study the index of the APS boundary value problem for a strongly Callias-type operator DD on a complete even dimensional Riemannian manifold MM (the odd dimensional case was considered in our previous paper arXiv:1706.06737). We use this index to define the relative ηη-invariant η(A1,A0)η(A_1,A_0) of two strongly Calli…

2017-08-24abs ↗pdf ↗

Deep neural networks can generalize well even with perfect fits to noisy data.

problem Understanding the conditions under which deep neural networks generalize well in the presence of noise.
method Comprehensive study of linear maximum margin classifiers, focusing on noisy and noiseless cases.
result Discovery of a phase transition in test error bounds for the noisy model.

New method exploits independence in instrumental variable models for better causal inference.

problem Identify causal functions in the presence of unobserved confounders.
method HSIC-X method that exploits independence between response, hidden confounders, and instruments.
result The method provides better finite sample results and is invariant to distributional shifts.

This paper extends NCFI to odd codimension and computes examples.

problem Extending NCFI to foliations of odd codimension.
method Computing NCFI for various foliated manifolds in both even and odd codimensions.
result NCFI is an invariant of foliations in odd codimension, requiring an odd \(K_1\)-class.

We give a complete characterization of those f:[0,1]Xf: [0,1] \to X (where XX is a Banach space which admits an equivalent Fréchet smooth norm) which allow an equivalent C2C^2 parametrization. For X=RX=\R, a characterization is well-known. However, even in the case X=R2X=\R^2, several quite new ideas are needed. Moreover, the …

2006-03-31abs ↗pdf ↗

We consider scattering by an abstract compactly supported perturbation in R^n. To include the traditional cases of potential, obstacle and metric scattering without going into their particular nature we adopt the "black box" formalism developed jointly with Sjostrand [23]. It is quite likely that one could extend the r…

1999-01-21abs ↗pdf ↗