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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.

169,291 papers · 148 categories

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64129193257 · May 202619922001200920182026
48 results for Mackey's theory

New map constructed from equivariant spectra for manifold study.

problem Understanding equivariant parametrized h-cobordism in non-manifold settings.
method Constructed a map from suspension G-spectrum to equivariant A-theory spectrum, compatible with tom Dieck splitting formulas.
result Fiber of constructed map is wedge of stable h-cobordism spectra.

Generalizes CNNs on homogeneous spaces like Euclidean and spherical surfaces.

problem Classifying and understanding equivariant CNNs on homogeneous spaces.
method Develops a theory for equivariant maps between field spaces of given types.
result Equivariant kernels correspond to the most general kind of equivariant linear maps.

We construct for an equivariant cohomology theory for proper equivariant CW-complexes an equivariant Chern character, provided that certain conditions about the coefficients are satisfied. These conditions are fulfilled if the coefficients of the equivariant cohomology theory possess a Mackey structure. Such a structur…

2004-01-06abs ↗pdf ↗

Study on diffeologies on locally convex spaces and smooth multiplication of distributions.

problem Geometric characterization and smoothness of distribution multiplication.
method Investigation of canonical and cc^\infty-diffeologies on locally convex spaces, proving geometric characterizations, and comparing diffeologies.
result Established a framework for nonlinear distribution theory beyond manifolds, realizing microlocally multipliable distributions as a diffeological colimit.

RPNN-EOFs model improves time series forecasting accuracy.

problem Improving time series forecasting accuracy for complex systems.
method Combines higher-order neural networks with error-output feedbacks.
result RPNN-EOFs outperformed other models in forecasting the Mackey-Glass time series.

This paper establishes a mathematical framework for G-CNNs on homogeneous spaces.

problem Designing equivariant neural networks for data with symmetries.
method Using Mackey's theory on induced representations, the paper presents a general framework for G-CNNs.
result G-CNNs are a universal class of equivariant network architectures.

Mackey showed that for a compact Lie group KK, the pair (K,C0(K))(K,C^{0}(K)) has a unique non-trivial irreducible covariant pair of representations. We study the relevance of this result to the unitary equivalence of quantizations for an infinite-dimensional family of K×KK\times K invariant polarizations on TKT^{\ast}K. The …

2012-11-09abs ↗pdf ↗

Convex functions in dual Orlicz spaces are studied under specific topologies.

problem Characterizing convex functions in dual Orlicz spaces under different topologies.
method Using a Komlós type result, the study examines sequences and their convex combinations to understand continuity and lower semicontinuity.
result A proper convex function is lower semicontinuous for the Mackey topology if and only if it is so on each order interval.

Functor connects Lie groupoid algebras to bornological structures.

problem Establishing a functorial relationship between Lie groupoid convolution algebras and bornological structures.
method Developed a monoidal functor from differentiable stacks to Morita 2-category of complete bornological algebras.
result Convolution algebras are self-induced and convolution modules are smooth.

Deep learning model simulates noisy dynamical systems without distributional assumptions.

problem Simulating noisy dynamical systems with unknown distributional properties.
method DE-LSTM model using LSTM network for multi-label classification and penalized maximum log likelihood.
result DE-LSTM makes accurate predictions of probability distributions for noisy dynamical systems.

Counterexample disproves completeness of model space forcing regularity in infinite-dimensional Lie groups.

problem Whether every Lie group modeled on a complete locally convex space is regular.
method Constructing a specific contractible complex analytic BCH-Lie group with unique properties.
result The group is not even C0C^0-semiregular, and smooth controls have no C1C^1 evolution.

ICA accurately estimates treatment effects even with confounders.

problem Estimating treatment effects in the presence of confounding variables.
method Uses Independent Component Analysis (ICA) to identify latent sources and estimate mixing coefficients.
result Linear ICA can consistently estimate multiple treatment effects, even with Gaussian confounders, and is more sample-efficient than Orthogonal Machine Learning (OML).

Extends particle classification to curved space-times using groupoids.

problem Classifying elementary particles in curved space-time.
method Developed a new definition of elementary particles as irreducible projective representations of kinematical groupoids, extending Wigner's program.
result Classification of elementary particles valid for a wide range of space-times, including new massless particles in magnetic-like backgrounds.

Combines Fourier methods and RNNs for efficient time series prediction.

problem Efficiently processing and predicting time series data with memory and computational constraints.
method Uses short-time Fourier transform and weight reductions through low pass filtering in a Spectral RNN.
result Predicts time series data from chaotic systems and real-world data.

We study diffeologies on locally convex spaces and their application to smooth multiplication of distributions.

problem Constructing smooth multiplication of distributions on locally convex spaces.
method Using diffeological colimits and wavefront-set criterion.
result Proving smooth multiplication of microlocally multipliable distributions.

ForGAN uses GANs for probabilistic forecasting of sensory data.

problem Challenges in traditional forecasting methods and difficulties in probabilistic methods.
method ForGAN combines GANs with conditional generative adversarial networks to learn data distributions and generate probabilistic forecasts.
result ForGAN outperforms traditional regression methods in probabilistic forecasting of sensory data.

Global models outperform univariate benchmarks in complex time series forecasting.

problem Comparing global forecasting models to univariate benchmarks in various challenging scenarios.
method Simulated datasets with controlled characteristics, including homogeneity, complexity, and series lengths. Global forecasting models (RNN, LGBM) compared to univariate techniques.
result Global models like RNN and LGBM are competitive in complex scenarios with short series lengths and heterogeneous data.

Characterizes continuity of monotone functionals in mixed topology.

problem Continuity of monotone functionals in mixed topology.
method Characterization through lower semicontinuity and dual representations.
result Continuity in mixed topology is equivalent to dual representation in terms of countably additive measures.

Compress++ speeds up distribution compression to near-linear time.

problem Accurately summarize a probability distribution using a small number of points efficiently.
method Introduces Compress++, a meta-procedure to speed up any thinning algorithm.
result Achieves n\sqrt{n} points with O(logn/n)\mathcal{O}(\sqrt{\log n/n}) integration error in O(nlog3n)\mathcal{O}(n \log^3 n) time and O(nlog2n)\mathcal{O}( \sqrt{n} \log^2 n ) space.

Improved convergence rates for Stein Variational Gradient Descent in finite-particle settings.

problem Improving convergence rates for Stein Variational Gradient Descent in finite-particle settings.
method Analyzing the time derivative of relative entropy and splitting it into dominant and smaller parts.
result Finite-particle convergence rates of order 1/\sqrt{N} for Kernelized Stein Discrepancy and Wasserstein-2 metrics.

Enhanced fuzzy system predicts chaotic time series with improved accuracy.

problem Forecasting chaotic time series with high uncertainty.
method Combines evolving fuzzy systems, participatory learning, KRLS, and type-2 fuzzy sets.
result Proposed model outperforms other methods in accuracy and complexity.

The paper connects function theory, dynamics, and ergodic theory via Thurston's theory.

problem Function theory on Teichmüller space and dynamics of mapping class groups.
method Utilizes Thurston's theory and Sullivan's theory on discrete subgroups of hyperbolic space.
result Establishes connections between function theory, dynamics, and ergodic theory.

Lectures on topological field theories and differential cohomology.

problem Exploring topological field theories and their connections to differential cohomology.
method Introduction to topological field theory and generalized Abelian gauge theories.
result Explains the relationship between topological field theories and differential cohomology.

The paper defines strong emergence in field theories and proves it exists between certain theories.

problem Defining and proving the existence of strong emergence phenomena between field theories.
method Formal definition and sufficient conditions for emergence, proving existence in Euclidean background.
result Strong emergence exists between certain parameterized Lagrangian field theories.

Researchers solve M-theory's gauge enhancement problem using advanced homotopy theory.

problem Lift nonabelian gauge fields from D-branes to M-theory.
method Universal constructions in super homotopy theory, focusing on the cyclification adjunction and fiberwise stabilization.
result Gauge enhancement in M-theory is explained by lifting against the fiberwise stabilization of the unit of the cyclification adjunction.

Researchers find new G2G_2-conifolds in MM-theory with potential field theory duals.

problem Exploring the field theory interpretation of MM-theory G2G_2-conifolds.
method Constructing G2G_2-holonomy orbifolds from circle bundles over Calabi-Yau cones.
result Many UV perturbative gauge theories have an infrared dual described by smooth G2G_2-holonomy backgrounds in MM-theory.

We survey three different ways in which K-theory in all its forms enters quantum field theory. In Part 1 we give a general argument which relates topological field theory in codimension two with twisted K-theory, and we illustrate with some finite models. Part 2 is a review of pfaffians of Dirac operators, anomalies, a…

2002-06-18abs ↗pdf ↗