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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.

168,742 papers · 148 categories

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48 results for transverse symbols

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.

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 ↗

We consider a consider the case of a compact manifold M, together with the following data: the action of a compact Lie group H and a smooth H-invariant distribution E, such that the H-orbits are transverse to E. These data determine a natural equivariant differential form with generalized coefficients J(E,X) whose prop…

2008-10-02abs ↗pdf ↗

We introduce a new class of natural, explicitly defined, transversally elliptic differential operators over manifolds with compact group actions. Under certain assumptions, the symbols of these operators generate all the possible values of the equivariant index. We also show that the components of the representation-va…

2008-05-21abs ↗pdf ↗

We investigate various structures associated with the hyperbolic Markov and homological spectra of a pseudoAnosov map φφ on a surface. Each unstable eigenvalue of the action of φφ on first cohomolgy yields an eigen-cocycle that is transverse and holonomy invariant to the stable foliation Fs\mathcal{F}^s of φφ. Each …

2010-09-15abs ↗pdf ↗

We construct certain spectral triples in the sense of A. ~Connes and H. Moscovici (``The local index formula in noncommutative geometry'' {\it Geom. Funct. Anal.}, 5(2):174--243, 1995) that is transversally elliptic but not necessarily elliptic. We prove that these spectral triples satisfie the conditions which ensure …

2003-11-05abs ↗pdf ↗

These notes are the first chapter of a monograph, dedicated to a detailed proof of the equivariant index theorem for transversally elliptic operators. In this preliminary chapter, we prove a certain number of natural relations in equivariant cohomology. These relations include the Thom isomorphism in equivariant cohomo…

2007-11-25abs ↗pdf ↗

Let DD be a bounded logarithmically convex complete Reinhardt domain in Cn\mathbb{C}^n centered at the origin. Generalizing a result for the one-dimensional case of the unit disk, we prove that the CC^*-algebra generated by Toeplitz operators with bounded measurable separately radial symbols (i.e., symbols depending …

2012-01-10abs ↗pdf ↗

In \cite{KOT:MORITA}, Kotschick and Morita showed that the Gel'fand-Kalinin-Fuks class in $\ds \HGF{7}{2}{}{8}$ is decomposed as a product ηωη\wedge ω of some leaf cohomology class ηη and a transverse symplectic class ωω. In other words, the Kontsevich homomorphism $\dsω\wedge :\HGF{5}{2}{0}{10} \rightarrow\HGF{7}{2}…

2014-07-03abs ↗pdf ↗

Fredholm conditions for invariant operators on compact manifolds.

problem Characterizing operators with Fredholm maps induced by their action on manifolds.
method Defining transversally αα-elliptic operators and proving Fredholmness based on their principal symbols and group actions.
result Operators are Fredholm if and only if they are transversally αα-elliptic.

In Riemann geometry, the relations among two transversal submanifolds and global manifold are discussed. By replacing the normal vector of a submanifold with the tangent vector of another submanifold, the metric tensors, Christoffel symbols and curvature tensors of the three manifolds are linked together. When the inne…

1999-01-27abs ↗pdf ↗

Machine learning approximates Calabi-Yau Hodge numbers from weight systems.

problem Approximating Hodge numbers of Calabi-Yau manifolds from weight systems.
method Neural networks learned Hodge numbers from weight systems, symbolic regression inspired truncation, and machine learning generated new datasets.
result Approximation provides tight lower bounds and dramatically faster computation.

In this article, we start to recall the inversion formula for the convolution with the Box spline. The equivariant cohomology and the equivariant K-theory with respect to a compact torus G of various spaces associated to a linear action of G in a vector space M can be both described using some vector spaces of distribu…

2010-12-05abs ↗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 ↗

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.

Study on symplectic Dirac operators on foliations, estimating eigenvalues.

problem Estimating eigenvalues of transversely symplectic Dirac operators.
method Analysis of transversely symplectic structures and use of Weitzenbock formula.
result Estimation of lower bounds for eigenvalues of transversely symplectic Dirac operators.

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.

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.

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.

Study on transverse Ricci solitons on compact foliated manifolds.

problem Characterizing transverse Ricci solitons on compact foliated manifolds.
method Investigation of self-similar solutions of the transverse Ricci flow, analysis of taut Riemannian foliations.
result Established relations between taut Riemannian foliations and transverse Ricci solitons, found examples of transverse Ricci solitons.

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.

New examples show transverse knots are determined by their branched covers.

problem Transverse knots and their isotopy classes.
method Constructing and analyzing non-isotopic transverse knots with contactomorphic cyclic branched covers.
result Transverse isotopy classes of many transverse knots are determined by the contactomorphism type of their cyclic branched covers.

Study transverse Dolbeault cohomology for almost complex structures.

problem Understanding cohomology of transverse structures on manifolds.
method Define transverse Dolbeault cohomology, extend transverse complex structure, introduce involutive limit distribution.
result Cohomology spaces of (p,0) for almost complex structures coincide with transverse Dolbeault cohomology.

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.