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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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12.5%25.0%37.5%50.0% · Jan 199419922001200920172026
48 results for lattice gauge-equivariant CNNs

L-CNNs maintain gauge symmetry on non-Abelian lattice theories.

problem Applying convolutional neural networks to non-Abelian lattice gauge theories while preserving gauge symmetry.
method Developed a geometric formulation of L-CNNs that are equivariant under global symmetries and gauge transformations.
result Convolutional operations in L-CNNs are a specific case of gauge-equivariant neural networks on SU(NN) principal bundles.

L-CNNs preserve gauge symmetry in neural networks.

problem Applying machine learning to lattice gauge theory while preserving gauge symmetry.
method L-CNNs use gauge equivariance to construct a gauge equivariant convolutional layer and bilinear layer.
result L-CNNs achieve higher accuracy in non-linear regression tasks compared to non-equivariant CNNs.

The principle of equivariance to symmetry transformations enables a theoretically grounded approach to neural network architecture design. Equivariant networks have shown excellent performance and data efficiency on vision and medical imaging problems that exhibit symmetries. Here we show how this principle can be exte…

2019-02-11abs ↗pdf ↗

Coordinate-independent convolutions on manifolds avoid reference frame ambiguity.

problem Applying convolutions on non-Euclidean manifolds without reference frame ambiguity.
method Developed coordinate-independent and gauge-equivariant convolutions on Riemannian manifolds.
result Coordinate-independent convolutions are equivariant under local gauge transformations.

Machine learning finds a compact fixed point action for SU(3) gauge theory.

problem Finding accurate and compact parametrizations of fixed point actions for SU(3) gauge theory.
method Used machine learning, specifically a gauge equivariant convolutional neural network.
result Obtained a superior parametrization of a fixed point action for SU(3) gauge theory.

CNN accurately reconstructs lattice topology with strong thermal fluctuations.

problem Reconstructing lattice topology with strong thermal fluctuations and unbalanced data.
method Deep convolutional neural network (CNN) mapping local magnetic moments to coupling probabilities.
result CNN accurately reconstructs lattice topology where thermal fluctuations dominate.

RDL-Net improves speech enhancement with fewer parameters and better performance.

problem Improving speech enhancement with fewer parameters and better performance.
method Proposes RDL-Net, a CNN combining residual and dense aggregations without over-allocating parameters.
result RDL-Net achieves higher speech enhancement performance with fewer parameters and lower computational requirements.

Machine learning predicts critical points for directed percolation models.

problem Determining critical points for directed percolation models.
method Supervised and unsupervised machine learning algorithms (CNN and DBSCAN) were used.
result Machine learning accurately predicts critical points for both models.

A new optimizer DDC improves deep learning models by respecting symmetries.

problem Deep networks' loss is invariant to continuous symmetries, leading to optimization issues.
method DDC builds a Dead-Direction Conditioner that lifts a base optimizer into a G-equivariant one, preserving the quotient geometry.
result DDCAdam and DDCMuon outperform standard optimizers in various tasks, improving validation-train loss gaps and learning dynamics.

In arXiv:math/0605587, the first two authors have constructed a gauge-equivariant Morse stratification on the space of connections on a principal U(n)-bundle over a connected, closed, nonorientable surface. This space can be identified with the real locus of the space of connections on the pullback of this bundle over …

2008-10-27abs ↗pdf ↗

The effectiveness of Convolutional Neural Networks stems in large part from their ability to exploit the translation invariance that is inherent in many learning problems. Recently, it was shown that CNNs can exploit other invariances, such as rotation invariance, by using group convolutions instead of planar convoluti…

2018-03-06abs ↗pdf ↗

Research finds bounds for knots in hexagonal lattice and classifies 11-stick knots.

problem Determining the stick number and edge length of knots in a hexagonal lattice.
method Introducing a linear transformation between lattices to prove strict inequalities and classifying knots.
result Only trefoil and figure-eight knots are 11-stick knots in the hexagonal lattice.

We outline the theory of sets with distributive operations: multishelves and multispindles, with examples provided by semi-lattices, lattices and skew lattices. For every such a structure we define multi-term distributive homology and show some of its properties. The main result is a complete formula for the homology o…

2011-11-21abs ↗pdf ↗

We give a simple example showing that a knot or link diagram that lies in the Z2{\mathbb{Z}}^2 lattice is not necessarily the projection of a lattice stick knot or link in the Z3{\mathbb{Z}}^3 lattice, and we give a necessary and sufficient condition for when a knot or link diagram that lies in the Z2{\mathbb{Z}}^2 lat…

2018-03-09abs ↗pdf ↗

We explore hybrid subgroups of certain non-arithmetic lattices in PU(2,1)\mathrm{PU}(2,1). We show that all of Mostow's lattices are virtually hybrids; moreover, we show that some of these non-arithmetic lattices are hybrids of two non-commensurable arithmetic lattices in PU(1,1)\mathrm{PU}(1,1).

2019-05-29abs ↗pdf ↗

This paper studies the covolumes of nonuniform arithmetic lattices in PU(n, 1). We determine the smallest covolume nonuniform arithmetic lattices for each n, the number of minimal covolume lattices for each n, and study the growth of the minimal covolume as n varies. In particular, there is a unique lattice (up to conj…

2011-07-26abs ↗pdf ↗

The paper finds incommensurable lattices in complex models of Baumslag-Solitar groups.

problem Locally finite 2-complexes and their automorphism groups contain incommensurable lattices.
method Constructing lattices in combinatorial models of Baumslag-Solitar groups and analyzing their properties.
result The constructed lattices are incommensurable and have specific properties like isomorphic Cayley graphs.

Proves a lattice version of the Atiyah-Singer index theorem.

problem Index problems of Wilson-Dirac operators on lattice approximations of manifolds.
method Formulates and proves a KK-theoretic formula for an index-type invariant.
result Main theorem gives a formula for an index-type invariant of operators on lattice approximations of closed integral affine manifolds.

In this paper we use techniques from convex projective geometry to produce many new examples of thin subgroups of lattices in special linear groups that are isomorphic to the fundamental groups of finite volume hyperbolic manifolds. More specifically, we show that for a large class of arithmetic lattices in SO(n,1) it …

2018-09-07abs ↗pdf ↗

We show that the set of even positive definite lattices that arise from smooth, simply-connected 4-manifolds bounded by a fixed homology 3-sphere can depend on more than the ranks of the lattices. We provide two homology 3-spheres with distinct sets of such lattices, each containing a distinct nonempty subset of the ra…

2018-08-30abs ↗pdf ↗

The paper proves residual finiteness of certain lattices and constructs surfaces with specific fundamental groups.

problem Residual finiteness of lattices in PU(2,1)~\widetilde{\mathrm{PU}(2,1)} and existence of smooth projective surfaces.
method Proved residual finiteness of certain lattices and constructed surfaces using central extensions.
result First examples of residually finite lattices in PU(2,1)~\widetilde{\mathrm{PU}(2,1)} and construction of surfaces with specific fundamental groups.

The cubic lattice stick index of a knot type is the least number of sticks necessary to construct the knot type in the 3-dimensional cubic lattice. We present the cubic lattice stick index of various knots and links, including all (p,p+1)-torus knots, and show how composing and taking satellites can be used to obtain t…

2012-05-23abs ↗pdf ↗

The paper refines transformations of lattice diagrams and introduces dotted diagrams.

problem Investigating transformations and deformations of lattice diagrams and their associated dotted diagrams.
method Introducing dotted diagrams and investigating deformations of these diagrams, relating them to transformations of lattice diagrams.
result Refined results on the relation between deformations of admissible dotted diagrams and transformations of lattice diagrams.

Classifies knots by lattice size, finding unknot ratios and crossing numbers.

problem Understanding the distribution of knots within different lattice sizes.
method Introduced a new knot classification by lattice size, analyzed ratios of unknots and knots with more than 10 crossings, and compared with theoretical estimates.
result Ratio of unknots decreases exponentially with lattice size, and computational results match theoretical estimates.

Let GG be a simply connected, solvable Lie group and ΓΓ a lattice in GG. The deformation space D(Γ,G)\mathcal{D}(Γ,G) is the orbit space associated to the action of $\Aut(G)$ on the space X(Γ,G)\mathcal{X}(Γ,G) of all lattice embeddings of ΓΓ into GG. Our main result generalises the classical rigidity theorems of Mal'tsev…

2011-11-23abs ↗pdf ↗

We study the arithmeticity of the Couwenberg-Heckman-Looijenga lattices in PU(n,1), and show that they contain a non-arithmetic lattice in PU(3,1) which is not commensurable to the non-arithmetic Deligne-Mostow lattice in PU(3,1).

2017-10-12abs ↗pdf ↗

Improved lattice field theory simulations with local-Autoregressive Conditional Normalizing Flow.

problem Efficiently sampling lattice field theories with computational challenges.
method Integrates locality into autoregressive conditional normalizing flows.
result Autocorrelation times improved by orders of magnitude for φ4φ^{4} theory on a 2D lattice.