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48 results for modular functions

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.

New techniques prove quantum modularity for various functions.

problem Proving quantum modularity of false theta functions and related series.
method Developed techniques including Poisson summation formula and modular series framework.
result Unified approach to proving quantum modularity for various functions.

The study examines if ReLU activation function is optimal for modularity in neural networks.

problem Finding the best activation function for modularity in neural networks.
method Comparing ReLU with other activation functions for modularity and performance.
result ReLU may not be the best choice for modularity, suggesting other functions could be more suitable.

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.

Empirical risk minimization frequently employs convex surrogates to underlying discrete loss functions in order to achieve computational tractability during optimization. However, classical convex surrogates can only tightly bound modular loss functions, sub-modular functions or supermodular functions separately while …

2016-04-12abs ↗pdf ↗

New methods detect modular structure in neural networks, revealing surprising effects of dropout.

problem Detecting functional modules in neural networks for learning, compositionality, and generalization.
method Two families of methods: upstream and downstream, to define similarity between units.
result Dropout dramatically increased modularity, and there's little agreement between upstream and downstream methods.

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 ↗

Origami structures are enumerated and shown to be quantum modular.

problem Counting and understanding origami structures with real structures.
method Using combinatorics of zonal polynomials and Schur polynomials, and relating to quantum modular forms and double Hurwitz numbers.
result The generating functions of certain origami structures are quantum modular forms.

We investigate properties that intuitively ought to be satisfied by graph clustering quality functions, that is, functions that assign a score to a clustering of a graph. Graph clustering, also known as network community detection, is often performed by optimizing such a function. Two axioms tailored for graph clusteri…

2013-08-15abs ↗pdf ↗

We review the polynomial structure of the topological string partition functions as solutions to the holomorphic anomaly equations. We also explain the connection between the ring of propagators defined from special Kähler geometry and the ring of almost-holomorphic modular forms defined on modular curves.

2015-01-02abs ↗pdf ↗

Definition of the partition function of U(1) gauge theory is extended to a class of four-manifolds containing all compact spaces and certain asymptotically locally flat (ALF) ones including the multi-Taub--NUT spaces. The partition function is calculated via zeta-function regularization with special attention to its mo…

2010-05-31abs ↗pdf ↗

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.

New modularity function improves clustering of spatially embedded networks.

problem Improving clustering in spatially embedded networks for unsupervised learning.
method Developed a new modularity function and compared its performance with existing methods.
result Our modularity function outperforms existing methods in partitioning 2D and 3D granular assemblies.

In this paper we investigate the curvature of conformal deformations by noncommutative Weyl factors of a flat metric on a noncommutative 2-torus, by analyzing in the framework of spectral triples functionals associated to perturbed Dolbeault operators. The analogue of Gaussian curvature turns out to be a sum of two fun…

2011-10-16abs ↗pdf ↗

Unified approach to conformal and modular invariants on surfaces.

problem Constructing a general family of conformal invariants on surfaces.
method Using an identification of Teichmüller space and rigged moduli space, and analytic work on harmonic functions.
result Unified conformal and modular invariants can be viewed as generalized modular invariants and functions on the rigged moduli space.

Networks capture pairwise interactions between entities and are frequently used in applications such as social networks, food networks, and protein interaction networks, to name a few. Communities, cohesive groups of nodes, often form in these applications, and identifying them gives insight into the overall organizati…

2017-07-28abs ↗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.

We analyze different re-ranking algorithms for diversification and show that majority of them are based on maximizing submodular/modular functions from the class of parameterized concave/linear over modular functions. We study the optimality of such algorithms in terms of the `total curvature'. We also show that by adj…

2019-06-26abs ↗pdf ↗

Sine activation functions enable two-layer neural networks to learn modular addition more efficiently.

problem Learning modular addition with two-layer neural networks.
method Introduced and analyzed sine activation functions, providing theoretical and empirical evidence.
result Sine activation functions allow for constant-width network realizations of modular addition, whereas ReLU networks require linear width scaling.

A general question behind this paper is to explore a good notion for intrinsic curvature in the framework of noncommutative geometry started by Alain Connes in the 80s. It has only recently begun (2014) to be comprehended via the intensive study of modular geometry on the noncommutative two tori. In this paper, we exte…

2015-10-15abs ↗pdf ↗

A framework for modular training of robust generative models.

problem Training large generative models is resource-intensive and requires heuristic tuning.
method Modular training using a gating mechanism and a minimax game to find a robust gate.
result The modular approach can theoretically outperform monolithic baselines and is scalable.

Revealing a community structure in a network or dataset is a central problem arising in many scientific areas. The modularity function QQ is an established measure quantifying the quality of a community, being identified as a set of nodes having high modularity. In our terminology, a set of nodes with positive modular…

2017-08-18abs ↗pdf ↗

Zagier's conjecture on knot invariants is proven for irrationals, with applications to quantum modular forms.

problem Proving the continuity of a function related to the figure-eight knot's colored Jones polynomial at irrationals.
method Analyzing the asymptotic behavior of the colored Jones polynomial and using properties of continued fractions.
result The continuity conjecture for the function h(x)h(x) holds almost everywhere on the real line, and a smooth approximation is established.

We first show that hypergeometric functions appear naturally as spectral functions when applying pseudo-differential calculus to decipher heat kernel asymptotic in the situation where the symbol algebra is noncommutative. Such observation leads to a unified (works for arbitrary dimension) method of computing the modula…

2017-11-05abs ↗pdf ↗

Improved graph clustering with modularity and coarsening for attributes and communities.

problem Inaccurate community detection and computational inefficiency in graph clustering.
method Integrates coarsening and modularity maximization, using a loss function with log-determinant, smoothness, and modularity components.
result Superior clustering outcomes, proven consistent under DC-SBM, and efficient algorithm integration with GNNs and VGAEs.

We consider an asymptotic expansion of Kashaev's invariant or the colored Jones function for the torus link T(2,2m). We shall give q-series identity related to these invariants, and show that the invariant is regarded as a limit of q being N-th root of unity of the Eichler integral of the modular form of weight 3/2.

2003-05-20abs ↗pdf ↗

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 ↗

Clustering on hypergraphs has been garnering increased attention with potential applications in network analysis, VLSI design and computer vision, among others. In this work, we generalize the framework of modularity maximization for clustering on hypergraphs. To this end, we introduce a hypergraph null model, analogou…

2018-12-28abs ↗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.