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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.

168,657 papers · 148 categories

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6131925 · Mar 202619922001200920172026
48 results for modularization

We find and propose an explanation for a large variety of modularity-related symmetries in problems of 3-manifold topology and physics of 3d N=2\mathcal{N}=2 theories where such structures a priori are not manifest. These modular structures include: mock modular forms, SL(2,Z)SL(2,\mathbb{Z}) Weil representations, quantum mo…

2018-09-26abs ↗pdf ↗

Researchers found the global topology of the Eisenstein-Picard modular surface.

problem Understanding the global topology of the Eisenstein-Picard modular surface.
method Quotient space of the complex hyperbolic plane by the modular group.
result Determined the global topology of the Eisenstein-Picard modular surface as a 4-orbifold.

Study modular surfaces in Lorentz-Minkowski 3-space, classifying and analyzing their curvature and applications.

problem Understanding the curvature properties of modular surfaces in Lorentz-Minkowski space.
method Analyzing the sign of Gaussian and mean curvature, classifying surfaces, and applying to conformal field theories.
result Complete classification of zero Gaussian curvature modular surfaces and non-existence of non-planar maximal modular surfaces.

Modular neural networks generalize better with less data.

problem Theoretical and practical understanding of how modularity improves neural network generalization.
method Theoretical analysis of sample complexity, development of a novel learning rule.
result Modular networks require fewer samples to generalize compared to nonmodular networks, especially in high-dimensional tasks.

Our aim is to introduce and advocate non-ΣΣ (non-symmetric) modular operads. While ordinary modular operads were inspired by the structure of the moduli space of stable complex curves, non-ΣΣ modular operads model surfaces with open strings outputs. An immediate application of our theory is a short proof that the mod…

2014-10-13abs ↗pdf ↗

Neural networks learn modular arithmetic but not all, extending known solutions to generalize.

problem Neural networks struggle with modular arithmetic, especially for polynomials.
method Developed analytical solutions for MLP networks to learn modular addition and multiplication, then combined these solutions to generalize on arbitrary modular polynomials.
result Neural networks can learn and generalize solutions to modular polynomials, supporting the hypothesis that some polynomials are learnable.

Fuchsian groups with a modular embedding have the richest arithmetic properties among non-arithmetic Fuchsian groups. But they are very rare, all known examples being related either to triangle groups or to Teichmueller curves. In Part I of this paper we study the arithmetic properties of the modular embedding and deve…

2015-03-19abs ↗pdf ↗

We introduce the notion of the modular class of a Lie algebroid equipped with a Nambu structure. In particular, we recover the modular class of a Nambu-Poisson manifold MM with its Nambu tensor ΛΛ as the modular class of the tangent Lie algebroid TMTM with Nambu structure Λ.Λ. We show that many known properties of th…

2016-09-16abs ↗pdf ↗

Quantum modularity proved for SU(2) TQFT signature on genus 2 surfaces.

problem Proving quantum modularity of SU(2) TQFT signature for genus 2 surfaces.
method Using quantum modularity of generalized Dedekind sums associated with modular forms and trigonometric sum expressions.
result Quantum modularity of SU(2) TQFT signature on genus 2 surfaces proved.

Motivated by a question of Hirzebruch on the possible topological types of cusp cross-sections of Hilbert modular varieties, we give a necessary and sufficient condition for a manifold M to be diffeomorphic to a cusp cross-section of a Hilbert modular variety. Specialized to Hilbert modular surfaces, this proves that e…

2007-06-25abs ↗pdf ↗

In this paper the exact linear relation between the leading eigenvectors of the modularity matrix and the singular vectors of an uncentered data matrix is developed. Based on this analysis the concept of a modularity component is defined, and its properties are developed. It is shown that modularity component analysis …

2015-10-19abs ↗pdf ↗

Study Alexander polynomials of modular knots, revealing finite and infinite coefficient properties.

problem Investigate Alexander polynomials of modular knots.
method Use Burau representation and geometric SL2(Z)\mathrm{SL}_2(\mathbb{Z})-invariants.
result Alexander polynomials of modular knots have both finite and infinite coefficient properties.

Sharp lower bounds for modular invariants and Dehn twist coefficients in genus 2 and 3.

problem Finding sharp lower bounds for modular invariants and Dehn twist coefficients.
method Analyzing the relation between fractional Dehn twists and modular invariants, classifying pseudo-periodic maps, and proving rigidity properties.
result Sharp lower bounds for modular invariants and Dehn twist coefficients in genus 2 and 3.

This paper develops the exact linear relationship between the leading eigenvector of the unnormalized modularity matrix and the eigenvectors of the adjacency matrix. We propose a method for approximating the leading eigenvector of the modularity matrix, and we derive the error of the approximation. There is also a comp…

2015-05-09abs ↗pdf ↗

A modular functor is constructed from non-semisimple 3d TFTs.

problem Constructing modular functors from non-semisimple 3d topological field theories.
method Using a 3d TFT defined in [arXiv:1912.02063], a symmetric monoidal 2-functor is constructed from a 2-category of bordisms to a 2-category of finite linear categories.
result A modular functor is explicitly described as a symmetric monoidal 2-functor.

We introduce the modular class of a Poisson map. We look at several examples and we use the modular classes of Poisson maps to study the behavior of the modular class of a Poisson manifold under different kinds of reduction. We also discuss their symplectic groupoid version, which lives in groupoid cohomology.

2011-03-22abs ↗pdf ↗

ETQFTs created from non-semisimple modular categories.

problem Constructing ETQFTs from non-semisimple modular categories.
method Explicitly identify linear categories and functors in the image of ETQFTs constructed from modular categories.
result The circle category of ETQFTs is equivalent to the full subcategory of projective objects of the underlying modular category, which need not be semisimple.

We study the behavior of the modular class of a Lie algebroid under general Lie algebroid morphisms by introducing the relative modular class. We investigate the modular classes of pull-back morphisms and of base-preserving morphisms associated to Lie algebroid extensions. We also define generalized morphisms, includin…

2007-12-18abs ↗pdf ↗

Picard modular groups are shown to be generated by complex reflections.

problem Understanding the structure of Picard modular groups using reflections.
method Using presentations from previous works to show generation by reflections.
result Picard modular groups mPU(2,1,Od){ m PU}(2,1,\mathcal{O}_d) are generated by complex reflections.

Artificial neural networks (ANNs) have achieved significant success in tackling classical and modern machine learning problems. As learning problems grow in scale and complexity, and expand into multi-disciplinary territory, a more modular approach for scaling ANNs will be needed. Modular neural networks (MNNs) are neu…

2019-04-29abs ↗pdf ↗

Modular operads are a special type of operad: in fact, they bear the same relationship to operads that graphs do to trees (i.e. simply connected graphs). One of the basic examples of a modular operad is the collection of Deligne-Mumford-Knudsen moduli spaces Mˉg,n\bar{M}_{g,n} of stable pointed algebraic curves; hence the…

1994-08-17abs ↗pdf ↗

Mathematical study supports connection between 3D manifolds and modular tensor categories.

problem Connecting geometric topology and quantum topology using Chern-Simons invariants and Reidemeister torsions.
method Developed an algorithm to generate modular TT-matrices and quantum dimensions from Seifert fibered spaces and torus bundles over the circle.
result Mathematically constructed premodular categories from Seifert fibered spaces and torus bundles over the circle, conjecturing their modularity under specific conditions.

We introduce a method in differential geometry to study the derivative operators of Siegel modular forms. By determining the coefficients of the invariant Levi-Civita connection on a Siegel upper half plane, and further by calculating the expressions of the differential forms under this connection, we get a non-holomor…

2012-07-07abs ↗pdf ↗

A new method integrates forms on Riemann surfaces, leading to modular forms.

problem Integrating differential forms with poles on Riemann surfaces.
method Simple procedure to integrate differential forms with arbitrary holomorphic poles, establishing an analytic theory for integrals over configuration spaces.
result Regularized graph integrals on elliptic curves are almost-holomorphic modular forms.

We show that the modular group has an infinite family of finite index subgroups, each of which has the same trace set as the modular group itself. Various congruence subgroups of the modular group, and the Bianchi groups, are also shown to have this property. In the case of the modular group, we construct examples of s…

2013-12-30abs ↗pdf ↗

The framed little 2-discs operad is homotopy equivalent to a cyclic operad. We show that the derived modular envelope of this cyclic operad (i.e., the modular operad freely generated in a homotopy invariant sense) is homotopy equivalent to the modular operad made from classifying spaces of diffeomorphism groups of 3-di…

2010-08-19abs ↗pdf ↗

New methods for scalable inference in modular models with misspecified sub-models.

problem Model misspecification in multi-modular models complicates evidence combination.
method Variational methods for approximating Cut and SMI posteriors, and Variational Meta-Posterior.
result Feasibility of analysis with multiple cuts using a single set of variational parameters.

New method uses hyperspherical geometry to improve community detection.

problem Improving community detection methods in network analysis.
method Mapping networks to points on a hypersphere, then projecting to clustering vectors.
result Modularity maximization is equivalent to minimizing angular distance on the hypersphere.

Skew algebroid is a natural generalization of the concept of Lie algebroid. In this paper, for a skew algebroid E, its modular class mod(E) is defined in the classical as well as in the supergeometric formulation. It is proved that there is a homogeneous nowhere-vanishing 1-density on E* which is invariant with respect…

2011-08-11abs ↗pdf ↗

We prove that the modular operad of diffeomorphism classes of Riemann surfaces with both `open' and `closed' boundary components, in the sense of string field theory, is the modular completion of its genus 0 part quotiented by the Cardy condition. We also provide a finitary presentation of a version of this modular two…

2016-11-26abs ↗pdf ↗

A modular object in a symmetric monoidal bicategory is a Frobenius algebra object whose product and coproduct are biadjoint, equipped with a braided structure and a compatible twist, satisfying rigidity, ribbon, pivotality, and modularity conditions. We prove that the oriented 3-dimensional bordism bicategory of 1-, 2-…

2014-11-04abs ↗pdf ↗

Linking numbers of modular knots derived from geometric and algebraic properties.

problem Understanding linking numbers between modular knots and the trefoil.
method Geometric and algebraic properties of the modular group and its action on the hyperbolic plane.
result Derived several formulae for linking numbers with arithmetical, combinatorial, topological and group theoretical flavors.

Noise-driven neural networks emerge modular structures, improving robustness and generalization.

problem Artificial neural networks struggle with modular solutions, leading to poor generalization and robustness.
method Inspired by brain's modular architecture, the study uses neural noise and nonlinear responses to drive the emergence of modular solutions.
result Noise-driven modularisation improves robustness and generalization in neural networks.