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7142128 · Jun 202019922001200920172026
48 results for Getzler symbol

The classical Getzler rescaling theorem is extended to the transverse geometry of foliations. More precisely, a Getzler rescaling calculus, as well as a Block-Fox calculus of asymptotic operators, is constructed for all transversely spin foliations. This calculus applies to operators of degree mm globally times degree…

2015-11-18abs ↗pdf ↗

The purpose of this article is to study Ezra Getzler's approach to the Atiyah-Singer index theorem from the perspective of Alain Connes' tangent groupoid. We shall construct a "rescaled" spinor bundle on the tangent groupoid, define a convolution operation on its smooth, compactly supported sections, and explain how th…

2019-02-22abs ↗pdf ↗

In this paper we give a construction of Fedosov quantization incorporating the odd variables and an analogous formula to Getzler's pseudodifferential calculus composition formula is obtained. A Fedosov type connection is constructed on the bundle of Weyl tensor Clifford algebras over the cotangent bundle of a Riemannia…

2012-11-08abs ↗pdf ↗

In this paper, we prove that infinitesimal equivariant Chern-Connes characters are well-defined. We decompose an equivariant index as a pairing of infinitesimal equivariant Chern-Connes characters with the Chern character of an idempotent matrix. We compute the limit of infinitesimal equivariant Chern- Connes character…

2014-11-25abs ↗pdf ↗

Let MM be a compact oriented dd-dimensional smooth manifold and XX a topological space. Chas and Sullivan \cite{Chas-Sullivan:stringtop} have defined a structure of Batalin-Vilkovisky algebra on H(LM):=H+d(LM)\mathbb{H}_*(LM):=H_{*+d}(LM). Getzler \cite{Getzler:BVAlg} has defined a structure of Batalin-Vilkovisky algebra on the…

2009-08-13abs ↗pdf ↗

In this paper we extend to the difference case the notion of Poisson-Lichnerowicz cohomology, an object encapsulating the building blocks for the theory of deformations of Hamiltonian operators. A local scalar difference Hamiltonian operator is a polynomial in the shift operator and its inverse, with coefficients in th…

2018-10-19abs ↗pdf ↗

In this paper we present the construction of explicit quasi-isomorphisms that compute the cyclic homology and periodic cyclic homology of crossed-product algebras associated with (discrete) group actions. In the first part we deal with algebraic crossed-products associated with group actions on unital algebras over any…

2017-06-27abs ↗pdf ↗

We introduce a new kind of groupoid--a pseudo étale groupoid, which provides many interesting examples of noncommutative Poisson algebras as defined by Block, Getzler, and Xu. Following the idea that symplectic and Poisson geometries are the semiclassical limits of the corresponding quantum geometries, we quantize thes…

2004-05-19abs ↗pdf ↗

We construct and analyse models of equivariant cohomology for differentiable stacks with Lie group actions extending classical results for smooth manifolds due to Borel, Cartan and Getzler. We also derive various spectral sequences for the equivariant cohomology of a differentiable stack generalising among others Bott'…

2019-12-01abs ↗pdf ↗

First, we review the notion of a Poisson structure on a noncommutative algebra due to Block-Getzler and Xu and introduce a notion of a Hamiltonian vector field on a noncommutative Poisson algebra. Then we describe a Poisson structure on a noncommutative algebra associated with a transversely symplectic foliation and co…

2009-12-10abs ↗pdf ↗

After a review of several methods designed to produce equivariant cohomology classes, we apply one introduced by Berline, Getzler and Vergne, to get a family of representatives of the universal Thom class of a vector bundle. Surprisingly, this family does not contain the representative given by Mathaï and Quillen. Howe…

1997-01-20abs ↗pdf ↗

From an operad C with an action of a group G, we construct new operads using the homotopy fixed point and orbit spectra. These new operads are shown to be equivalent when the generalized G-Tate cohomology of C is trivial. Applying this theory to the little disk operad C_2 (which is an S^1 operad) we obtain variations o…

2006-05-03abs ↗pdf ↗

Given an n-term L-infinity algebra L, we construct a Kan simplicial manifold which we think of as the 'Lie n-group' integrating L. This extends work of Getzler math.AT/0404003 . In the case of an ordinary Lie algebra, our construction gives the simplicial classifying space of the corresponding simply connect Lie group.…

2006-03-23abs ↗pdf ↗

We describe dimensionally constrained symbolic regression which has been developed for mass measurement in certain classes of events in high-energy physics (HEP). With symbolic regression, we can derive equations that are well known in HEP. However, in problems with large number of variables, we find that by constraini…

2011-06-20abs ↗pdf ↗

Paper closes neural-symbolic learning loop with grammar model and back-search algorithm.

problem Slow convergence in neural-symbolic learning due to error propagation issues.
method Introduces grammar model as symbolic prior and back-search algorithm for efficient error propagation.
result Significantly outperforms RL methods in performance, converging speed, and data efficiency.

For an arbitrary Riemannian manifold XX and Hermitian vector bundles EE and FF over XX we define the notion of the normal symbol of a pseudodifferential operator PP from EE to FF. The normal symbol of PP is a certain smooth function from the cotangent bundle TXT^*X to the homomorphism bundle Hom(E,F)Hom (E,F) and dep…

1996-12-11abs ↗pdf ↗

Based on the ideas of Optimal Control, we introduce the new basic characteristic of a bracket generating distribution, the Jacobi symbol. In contrast to the classical Tanaka symbol, the set of Jacobi symbols is discrete and classifiable. We give an explicit and unified algebraic procedure for the construction of the ca…

2016-10-29abs ↗pdf ↗

We show that any degree at least gg polynomial in descendant or tautological classes vanishes on Mg,nM_{g,n} when g2g\ge 2. This generalizes a result of Looijenga and proves a version of Getzler's conjecture. The method we use is the study of the relative Gromov-Witten invariants of P1P^1 relative 2 points combined with…

1999-08-13abs ↗pdf ↗

Symbolic knowledge in neural models can inadvertently make them more vulnerable to adversarial attacks.

problem Symbolic knowledge in neural models can make models more susceptible to adversarial attacks.
method Investigated deep probabilistic graphical models that incorporate symbolic knowledge and neural nets.
result Symbolic knowledge can propagate the negative effects of adversarial examples, making models more vulnerable.

We generalize some of the results of Harvey, Lawson and Latschev about transgression formulas. The focus here is on flowing forms via vertical vector fields, especially Morse-Bott-Smale vector fields. We prove a very general transgression formula including also a version covering non-compact situations. Among applicati…

2014-05-05abs ↗pdf ↗

The paper classifies symbols of differential operators on vector bundles.

problem Classifying symbols of linear differential operators on vector bundles.
method Associated tuples of linear operators to non-degenerate symbols and used C. Procesi's results to find rational invariants and equivalence criteria.
result Generators for rational invariants and a criterion for symbol equivalence.

NeSS combines neural and symbolic approaches for better compositional generalization.

problem Lack of compositional generalization in deep learning models.
method NeSS uses a neural network to generate traces, executed by a symbolic stack machine with sequence manipulation.
result Achieves 100% generalization performance across multiple domains.

Defines transverse symbols for foliated manifolds and proves their K-homology class.

problem Transverse index theory for foliated manifolds.
method Using filtrations of tangent bundles, defining transverse symbols, and constructing equivariant KK-classes.
result Transversally Rockland operators yield a K-homology class and there is a Poincare duality result.

A new method for spotting symbols in CAD images reduces annotation costs and improves accuracy.

problem Challenging task of labeling symbols from CAD drawings.
method Pixel-wise point location via Progressive Gaussian Kernels (PGK) and local offset.
result The proposed method achieves good generalization on real-world CAD images.

Paper proposes a bijective approach for signal/symbol translation using variational auto-encoders.

problem Extracting symbolic information from signals, especially in music, is challenging and non-generic.
method Turned into a density estimation task, using two variational auto-encoders with additive constraint.
result Bijective signal/symbol translation achieved, allowing both signal-to-symbol and symbol-to-signal inference.

The Wodzicki residue and the cut-off integral extend to classical symbol-valued forms. We show that they obey a Stokes' type property and that the extended Wodzicki residue can be interpreted as a complex residue like the ordinary one. In the case of cut-off integrals, Stokes' property (i.e. vanishing on exact forms) o…

2005-10-21abs ↗pdf ↗

Bayesian symbolic regression automates model discovery from data.

problem Learning closed-form mathematical models from data using heuristic methods.
method Probabilistic approach to symbolic regression, connecting to information theory and statistical physics.
result Probabilistic approach provides model plausibility and performance guarantees.

Getzler-Jones-Petrack introduced AA_\infty structures on the equivariant complex for manifold MM with smooth S1\mathbb{S}^1 action, motivated by geometry of loop spaces. Applying Witten's deformation by Morse functions followed by homological perturbation we obtained a new set of AA_\infty structures. We extend and …

2019-01-28abs ↗pdf ↗

Study shows how transformers classify symbols without naming them, proving a margin-versus-collision criterion.

problem How transformers classify symbols without naming them.
method Logistic classification analysis of transformer-kernel regime, colored collision graph.
result Decomposes learned predictor into ideal template-level classifier and finite-sample perturbation.

S2KAN integrates symbolic primitives into neural network activations for improved interpretability.

problem Training activations in KANs often lack symbolic fidelity, leading to unintelligible models.
method Softly Symbolified Kolmogorov-Arnold Networks (S2KAN) integrates symbolic primitives into training with learnable gates and a Minimum Description Length objective.
result S2KAN discovers interpretable forms when symbolic terms suffice, gracefully degrading to dense splines when necessary.

Symbolic LSTM improves time series forecasting by reducing hyperparameter sensitivity.

problem High sensitivity to hyperparameters and random initialization in numerical time series forecasting.
method Combining LSTM with a dimension-reducing symbolic representation.
result Symbolic representation alleviates forecasting problems and speeds up training.

Let X be a topological space. The homology of the iterated loop space HΩnXH_*Ω^n X is an algebra over the homology of the framed n-disks operad HfDnH_*f\mathcal{D}_n \cite{Getzler:BVAlg,Salvatore-Wahl:FrameddoBVa}. We determine completely this HfDnH_*f\mathcal{D}_n-algebra structure on H(ΩnX;Q)H_*(Ω^n X;\mathbb{Q}). We show that t…

2007-07-20abs ↗pdf ↗

Meta-learning symbolic default hyperparameters from dataset properties.

problem Empirical hyperparameter optimization is slow and requires manual configuration.
method Evolutionary algorithm to learn symbolic hyperparameter formulas from dataset properties.
result Meta-learning finds viable symbolic defaults for ML algorithms.