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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.

168,742 papers · 148 categories

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326395126 · May 202619922001200920172026
48 results for strangeness invariant

Recently V. Arnold introduced Strangeness and J±J^{\pm} invariants of generic immersions of an oriented circle to R2\R^2. Here these invariants are generalized to the case of generic immersions of an oriented circle to an arbitrary surface FF. We explicitly describe all the invariants satisfying axioms, which naturall…

1999-06-18abs ↗pdf ↗

In this paper we study the Lorenz equations using the perspective of the Conley index theory. More specifically, we examine the evolution of the strange set that these equations posses throughout the different values of the parameter. We also analyze some natural Morse decompositions of the global attractor of the syst…

2018-12-12abs ↗pdf ↗

The literature postulates that the dynamic time warping (dtw) distance can cope with temporal variations but stores and processes time series in a form as if the dtw-distance cannot cope with such variations. To address this inconsistency, we first show that the dtw-distance is not warping-invariant. The lack of warpin…

2019-03-04abs ↗pdf ↗

Constructing transitive nilpotent Lie algebras from dilations and analyzing their prolongations.

problem Understanding the derivations of Tanaka prolongations of transitive nilpotent Lie algebras.
method Constructing transitive nilpotent Lie algebras from dilations and analyzing their prolongations.
result Derivations of degree 0 are given by vector fields of degree 0, and the Tanaka prolongation recovers the whole algebra of polynomial vectors defined by the dilation.

Quantum modularity proved for a knot manifold.

problem Proving quantum modularity for a specific closed hyperbolic 3-manifold.
method Using factorization of state integrals and proving quantum modularity for functions and qq-series.
result Quantum modularity for the closed manifold provides a unification of volume conjecture and Witten's asymptotic expansion conjecture.

We investigate the issue of model selection and the use of the nonconformity (strangeness) measure in batch learning. Using the nonconformity measure we propose a new training algorithm that helps avoid the need for Cross-Validation or Leave-One-Out model selection strategies. We provide a new generalisation error boun…

2009-09-12abs ↗pdf ↗

The concept of causality has a controversial history. The question of whether it is possible to represent and address causal problems with probability theory, or if fundamentally new mathematics such as the do calculus is required has been hotly debated, e.g. Pearl (2001) states "the building blocks of our scientific a…

2019-06-17abs ↗pdf ↗

It is shown that a lagrangian system whose Legendre transformation degenerates along a hypersurface behaves in a strange manner by jumping from time to time without any ''visible cause''. In such a jump the system changes instantaneously its coordinates as well as its momenta. The mathematical dscription of the phenome…

1999-02-19abs ↗pdf ↗

The use of artificial neural networks as models of chaotic dynamics has been rapidly expanding. Still, a theoretical understanding of how neural networks learn chaos is lacking. Here, we employ a geometric perspective to show that neural networks can efficiently model chaotic dynamics by becoming structurally chaotic t…

2019-12-11abs ↗pdf ↗

In this thesis, we prove several results concerning field-theoretic invariants of knots and 3-manifolds. In Chapter 2, for any knot KK in a closed, oriented 3-manifold MM, we use SU(2)SU(2) representation spaces and the Lagrangian field theory framework of Wehrheim and Woodward to define a new homological knot invariant…

2014-07-03abs ↗pdf ↗

Following \cite{citeSavelyevVirtualMorsetheoryonOmegaOmegaHam(Momega)(Momega).}, we develop here a connection between Morse theory for the (positive) Hofer length functional L:ΩHam(M,ω)RL: Ω\text {Ham}(M, ω) \to \mathbb{R}, with Gromov-Witten/Floer theory, for monotone symplectic manifolds (M,ω) (M, ω) . This gives some immediate restrictio…

2013-08-15abs ↗pdf ↗

Study discrete analog of zeta-determinant maximization on triangulated surfaces.

problem Maximizing zeta-determinant for discrete Laplacian on triangulated surfaces.
method Analogous to Osgood, Phillips, and Sarnak's theorem, study stationary points of determinants for discrete cotan-Laplacian.
result Discrete metrics of constant discrete Gaussian curvature are stationary points of the determinant, suggesting minima.

Let (M,g)(M,g) be a compact manifold and let Δφk=λkφk-Δφ_k = λ_k φ_k be the sequence of Laplacian eigenfunctions. We present a curious new phenomenon which, so far, we only managed to understand in a few highly specialized cases: the family of functions fN:MR0f_N:M \rightarrow \mathbb{R}_{\geq 0} $$ f_N(x) = \sum_{k \leq N}{ \frac{…

2017-06-05abs ↗pdf ↗

Bayesian Neural Networks improve uncertainty modeling in facial emotion recognition.

problem High aleatoric uncertainty and visual ambiguity in facial emotion recognition.
method Bayesian Neural Networks approximated using MC-Dropout, MC-DropConnect, or Ensemble methods.
result Bayesian Neural Networks produce more human-like output probabilities.

100 years ago exactly, in 1906, Hartogs published a celebrated extension phenomenon (birth of Several Complex Variables), whose global counterpart was stated in full generality later by Osgood (1929): holomorphic functions in a connected neighborhood V(bD) of a connected boundary bD contained in C^n (n >= 2) do extend …

2006-10-31abs ↗pdf ↗

Study shows Lefschetz fibrations on Milnor fibers of certain singularities.

problem Understanding Lefschetz fibrations on Milnor fibers of specific singularities.
method Analyzes Milnor fibers of cusp and simple elliptic singularities to construct Lefschetz fibrations.
result Milnor fibers of cusp and simple elliptic singularities admit genus-one Lefschetz fibrations.

Proposes a new method combining Reservoir Computing and Normalizing Flow for predicting stochastic dynamical systems.

problem Predicting and capturing long-term behaviors of stochastic dynamical systems.
method Data-driven framework combining Reservoir Computing and Normalizing Flow, integrating error modeling and both approaches virtues.
result Successfully predicts the long-term evolution of stochastic dynamical systems and replicates dynamical behaviors.

In adversarial attacks intended to confound deep learning models, most studies have focused on limiting the magnitude of the modification so that humans do not notice the attack. On the other hand, during an attack against autonomous cars, for example, most drivers would not find it strange if a small insect image were…

2019-11-20abs ↗pdf ↗

Motivated by analogies with basic density theorems in analytic number theory, we introduce a notion (and variations) of the homological density of one space in another. We use Weil's number field/ function field analogy to predict coincidences for limiting homological densities of various sequences $\mathcal{Z}^{(d_1,\…

2016-11-14abs ↗pdf ↗

The definition of Kaehler manifold is superized. In the super setting, it admits a continuous parameter, unlike their analogs on manifolds. This parameter runs the same singular supervariety of parameters that parameterize deformations of the Schouten bracket (a.k.a. Buttin bracket, a.k.a. anti-bracket) considered as d…

2019-11-28abs ↗pdf ↗

Study geodesic X-ray transform and streaking artifacts on simple surfaces or spaces of constant curvature.

problem Streaking artifacts in CT images due to metal regions.
method Geodesic X-ray transform on nontrapping compact Riemannian manifolds with strictly convex boundaries.
result Streaking artifacts result from conormal singularities along common tangent geodesics.

New method improves conformal prediction for machine learning models.

problem Improving the efficiency and informativeness of conformal prediction models.
method Introduces Penalized Inverse Probability (PIP) and Regularized PIP (RePIP) nonconformity score functions.
result PIP-based conformal classifiers strike a good balance between informativeness and efficiency.

Wireless sensor networks usually comprise a large number of sensors monitoring changes in variables. These changes in variables represent changes in physical quantities. The changes can occur for various reasons; these reasons are highlighted in this work. Outliers are unusual measurements. Outliers are important; they…

2017-08-27abs ↗pdf ↗

Machine learning (ML) has become a commodity in our every-day lives. We routinely ask ML empowered smartphones to suggest lovely food places or to guide us through a strange place. ML methods have also become standard tools in many fields of science and engineering. A plethora of ML applications transform human lives a…

2018-05-14abs ↗pdf ↗

Abstract invariant cannot be expressed using various slice-torus invariants.

problem Cannot express Iida-Taniguchi's slice-torus invariant using other known invariants.
method Analysis of various known invariants and their properties.
result Iida-Taniguchi's slice-torus invariant cannot be realized as a linear combination of other invariants.