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48 results for mock modularity

Quantum invariants of three-manifolds linked to mock theta functions.

problem Quantum invariants of three-manifolds and their mock modular properties.
method Study of a specific class of Seifert three-manifolds and a conjecture on their quantum invariants.
result Illustration of mock modular properties of a quantum invariant for a specific three-manifold.

Researchers clarify modular group representations and vertex operator algebras for 3d invariants.

problem Understanding the full set of 3d invariants and their modular properties.
method Introducing supersymmetric defects and constructing cone vertex operator algebras.
result The full vector-valued quantum modular form for \(\widetilde{ m SL}_2(\mathbb{Z})\) captures all \(\hat Z\)-invariants of a given three-manifold.

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 ↗

We compute the Moore-Witten regularized u-plane integral on CP^2, and we confirm their conjecture that it is the generating function for the SO(3)-Donaldson invariants of CP^2. We prove this conjecture using the theory of mock theta functions and harmonic Maass forms. We also derive further such generating functions fo…

2008-08-11abs ↗pdf ↗

Revisits SYM theory to compute Donaldson invariants using mock modular forms.

problem Computing Donaldson invariants in topological SYM theory.
method Uses mock modular forms and indefinite theta functions to evaluate correlation functions.
result Explicit evaluation of correlation functions leading to modular data predictions.

Study compares two methods to extend Z^\widehat{Z} invariants, finding incompatibility for Brieskorn spheres.

problem Comparing two methods to extend Z^\widehat{Z} invariants for 3-manifolds.
method Two prescriptions: regularized +1/r+1/r-surgery combined with false-mock modular conjecture, and resurgence-based construction.
result Incompatibility found between the two prescriptions for some Brieskorn spheres.

The u-plane integral is the contribution of the Coulomb branch to correlation functions of N=2 gauge theory on a compact four-manifold. We consider the u-plane integral for correlators of point and surface observables of topologically twisted theories with gauge group SU(2), for an arbitrary four-manifold with (b1,b2+)…

2019-10-29abs ↗pdf ↗

Study topological correlators for SU(2)SU(2) SYM on four-manifolds, deriving explicit formulae and confirming S-duality.

problem Topological correlation functions of SU(2)SU(2), N=2\mathcal{N}=2^* SYM on four-manifolds.
method Coupling to a Spin^c structure, deriving explicit formulae, and confirming S-duality.
result Topological correlators are mock modular forms for b2+=1b_2^+=1.

New unoriented algebraic concordance group defined using mock Seifert matrices.

problem Understanding unoriented algebraic concordance of knots in thickened surfaces.
method Introducing mock Seifert matrices and using them to define unoriented algebraic concordance.
result The unoriented algebraic concordance group is abelian and infinitely generated.

Eguchi, Ooguri, and Tachikawa recently conjectured a new moonshine phenomenon. They conjecture that the coefficients of a certain mock modular form H(tau), which arises from the K3 surface elliptic genus, are sums of dimensions of irreducible representations of the Mathieu group M24. We prove that H(tau) surprisingly a…

2012-07-21abs ↗pdf ↗

MOCK learns complex systems from trajectories efficiently.

problem Learning nonparametric differential equations from high-dimensional data.
method MOCK uses multivariate occupation kernel functions to learn vector fields linearly.
result MOCK outperforms other methods on various datasets.

CHARM creates mock halo catalogs from dark matter density fields using neural networks.

problem Creating accurate mock halo catalogs for cosmological studies is computationally expensive.
method CHARM uses multi-stage neural spline flow networks to learn the mapping from dark matter density fields to halo catalogs.
result Mock halo catalogs have the same statistical properties as those from high-resolution N-body simulations.

The ERI is a new index for measuring exam readiness.

problem Measuring exam readiness in a clear and actionable way.
method The ERI combines six signals derived from practice and mock tests, formalizing axioms for component maps and the composite.
result The ERI is a composite score interpretable and actionable for exam readiness.

New method extracts cosmological information from dark matter halo catalogues using graph neural networks.

problem Quantifying cosmological information from large-scale structure data.
method Implicit likelihood approach with Information Maximising Neural Networks (IMNNs) on graph representations of dark matter halo catalogues.
result Graph neural network summaries can extract information from noisy catalogues and improve parameter constraints.

DFKI Cabin Simulator tests visual monitoring functions in vehicles.

problem Validating novel human-vehicle interfaces and driver assistance systems.
method Driving simulator with in-cabin mock-up and camera system.
result Validation of in-cabin monitoring functions for advanced driver assistance and automated driving.

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 ↗