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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 Group Composition

New conditions for weighted composition operators in group homomorphisms.

problem Conditions for weighted composition operators in group homomorphisms.
method Range decreasing group homomorphisms.
result New insights into weighted composition operators and their algebraic structure.

Knot contact homology is an invariant of knots derived from Legendrian contact homology which has numerous connections to the knot group. We use basic properties of knot groups to prove that knot contact homology detects every torus knot. Further, if the knot contact homology of a knot is isomorphic to that of a cable …

2015-09-05abs ↗pdf ↗

Generalizes quandle constructions and defines a multiplication that results in an abelian group.

problem Tackles the construction and multiplication of quandle structures.
method Defines a composition of quandle structures and proves conditions for it to form a quandle, then shows the resulting group is abelian.
result Multiplication of quandle structures results in an abelian group.

New geometric approach for analyzing compositional data like gut microbiomes.

problem Analyzing non-negative compositional data with relative values only.
method Reinterpret compositional data as quotient topology of a sphere, using spherical harmonics and reflection group actions.
result Construction of Reproducing Kernel Hilbert Space (RKHS) for compositional data.

Building on the universal covering group of the general linear group, we introduce the composite spinor bundle whose subbundles are Lorentz spin structures associated with different gravitational fields. General covariant transformations of this composite spinor bundle are canonically defined.

1997-05-21abs ↗pdf ↗

Neural networks learn spectral representations for group composition.

problem Understanding structured emergence in neural network training.
method Lifting gradient flow to Fourier domain, proving convergence to irreducible representations.
result Neurons converge to single irreducible representations, cross-layer coefficients align.

The paper explores the dynamics of composite symplectic Dehn twists with nonuniform hyperbolicity.

problem Understanding the dynamics and properties of composite symplectic Dehn twists.
method Analyzing the form of nonuniform hyperbolicity, growth of Floer cohomology, and classification of symplectic mapping classes.
result Composite symplectic Dehn twists exhibit positive topological entropy and exponential growth in Floer cohomology.

Algorithmic fairness, and in particular the fairness of scoring and classification algorithms, has become a topic of increasing social concern and has recently witnessed an explosion of research in theoretical computer science, machine learning, statistics, the social sciences, and law. Much of the literature considers…

2018-06-15abs ↗pdf ↗

Composite development indicators used in policy making often subjectively aggregate a restricted set of indicators. We show, using dimensionality reduction techniques, including Principal Component Analysis (PCA) and for the first time information filtering and hierarchical clustering, that these composite indicators m…

2019-11-25abs ↗pdf ↗

Godin introduced the categories of open closed fat graphs FatocFat^{oc} and admissible fat graphs FatadFat^{ad} as models of the mapping class group of open closed cobordism. We use the contractibility of the arc complex to give a new proof of Godin's result that FatadFat^{ad} is a model of the mapping class group of open-close…

2015-08-14abs ↗pdf ↗

We establish an interesting connection between Morin singularities and stable homotopy groups of spheres. We apply this connection to computations of cobordism groups of certain singular maps. The differentials of the spectral sequence computing these cobordism groups are given by the composition multiplication in the …

2015-06-17abs ↗pdf ↗

We show local rigidity of hyperbolic triangle groups generated by reflections in pairs of nn-dimensional subspaces of R2nR^{2n} obtained by composition of the geometric representation in PGL(2,R)PGL(2, R) with the diagonal embeddings into PGL(2n,R)PGL(2n, R) and PSp±(2n,R)PSp^\pm(2n, R).

2019-06-07abs ↗pdf ↗

Diffusion models learn hierarchical composition rules from data.

problem How many samples do generative models need to learn hierarchical composition rules?
method Theoretical and empirical investigation of diffusion models on probabilistic context-free grammars.
result Diffusion models learn hierarchical composition rules with sample complexity scaling polynomially with context size.

We address several problems concerning the geometry of the space of Hermitian operators on a finite-dimensional Hilbert space, in particular the geometry of the space of density states and canonical group actions on it. For quantum composite systems we discuss and give examples of measures of entanglement.

2006-03-20abs ↗pdf ↗

New framework interprets deep neural networks through input structure.

problem Limited understanding of how input structure, network parameters, and optimization algorithms work together.
method Introducing a novel theoretical framework based on the compositional structure of piecewise linear activation functions.
result Shows that input instances can be grouped based on their similarity in the internal representation of the neural network.

Constellation learns group-level visual relationships for abstract reasoning.

problem Learning configurational properties of entire groups of objects.
method Introduces Constellation, a network that learns relational abstractions over static visual scenes.
result Offers a basis for abstract relational reasoning and sensory imagination.

A formulation for a non-trivial composition of two classical gauge structures is given: Two parent gauge structures of a common base space are synthesized so as to obtain a daughter structure which is fundamental by itself. The model is based on a pair of related connections that take their values in the product space …

1996-01-09abs ↗pdf ↗

The study visualizes Spanish fish and meat processing companies using financial, environmental, and social ratios.

problem Mapping financial, environmental, and social performance of Spanish processing companies.
method Used compositional data and principal-component analysis biplot for statistical analysis.
result Identified clusters of companies with similar financial, environmental, and social performance.

In this paper, we define the equivariant eta form of Bismut-Cheeger for a compact Lie group and establish a formula about the functoriality of equivariant eta forms with respect to the composition of two submersions.

2015-05-17abs ↗pdf ↗

New financial ratios using compositional data improve analysis of firm health.

problem Statistical issues with standard financial ratios, especially skewness and outliers.
method Compositional data (CoDa) methodology to analyze financial statements.
result Outliers and skewness reduced, results invariant to numerator and denominator permutation.

This is a survey of our research on geometric structures of projective embeddings and includes some topics of our talks in several symposia during 1990-99. We clarify our main problem, which is to construct a kind of geometric composition series of projective embeddings. The concept of "geometric composition series" is…

2000-01-03abs ↗pdf ↗

Paper tackles distributed linear regression with compositional covariates.

problem Solving distributed statistical methodology and computing for massive compositional data.
method Proposes two distributed optimization techniques based on ADMM and CDMM for solving constrained convex optimization problems.
result Established convergence theories for the proposed algorithms under regularity conditions.

The structure set $\ST^{TOP}(M)$ of an nn-dimensional topological manifold MM for n5n \geqslant 5 has a homotopy invariant functorial abelian group structure, by the algebraic version of the Browder-Novikov-Sullivan-Wall surgery theory. An element $(N,f) \in \ST^{TOP}(M)$ is an equivalence class of nn-dimensional ma…

2006-08-29abs ↗pdf ↗

This research tackles group fairness in predictive process monitoring by ensuring predictions are independent of sensitive group membership.

problem Predictive models using biased historical data can perpetuate unfair behavior in new cases.
method Investigates independence through metrics like ΔDP and a composite loss function balancing predictive performance and fairness.
result Proposes and validates a composite loss function for training models that balance fairness and performance.

We introduce a special class of nilpotent Lie groups of step 2, that generalizes the so called HH(eisenberg)-type groups, defined by A. Kaplan in 1980. We change the presence of inner product to an arbitrary scalar product and relate the construction to the composition of quadratic forms. We present the geodesic equat…

2012-07-24abs ↗pdf ↗

Bayesian modeling predicts hydroxide ion conductivity in polymer membranes.

problem Quantitative relationship between hydrophilic domain size and hydroxide ion conductivity in polymer membranes is unknown.
method Bayesian sparse modeling applied to copolymer composition data.
result Composition-derived features are identified as critical for predicting hydroxide ion conductivity.

The paper calculates Alexander polynomials for knots using finite group representations.

problem Calculating Alexander polynomials for knots using specific group representations.
method Defined twisted Alexander polynomials associated with regular representations of finite groups.
result Several formulas for the twisted Alexander polynomial are provided.

If f is a conformal mapping defined on a connected open subset of a Carnot group G, then either f is the composition of a translation, a dilation and an isometry, or G is the nilpotent Iwasawa component of a real rank 1 simple Lie group S, and f arises from the action of S on G, viewed as an open subset of S/P, where P…

2013-12-22abs ↗pdf ↗

Floer cohomology is computed for certain elements of the mapping class group of a surface ΣΣ of genus g>1g>1 which are compositions of positive and negative dehn twists along some loops in ΣΣ. The computations cover a certain class of pseudo-Anasov maps.

2002-05-02abs ↗pdf ↗

For a compact contact manifold it is shown that the anisotropic Folland-Stein function spaces form an algebra. The notion of anisotropic regularity is extended to define the space of Folland-Stein contact diffeomorphisms, which is shown to be a topological group under composition and a smooth Hilbert manifold. These re…

2010-07-13abs ↗pdf ↗

This work is an analytical and numerical study of the composition of several fractals into one and of the relation between the composite dimension and the dimensions of the component fractals. In the case of composition of standard IFS with segments of equal size, the composite dimension can be expressed as a function …

2014-07-10abs ↗pdf ↗

We give an explicit handy (and cocycle-free) description of the groupoid of weak maps between two crossed-modules in terms of certain digrams of groups which we we call a {\em butterflies}. We define composition of butterflies and this way find a bicategory that is naturally biequivalent to the 2-category of pointed ho…

2005-06-15abs ↗pdf ↗

We give a complete classification of intertwining operators (symmetry breaking operators) between spherical principal series representations of G=O(n+1,1) and G'=O(n,1). We construct three meromorphic families of the symmetry breaking operators, and find their distribution kernels and their residues at all poles explic…

2013-10-11abs ↗pdf ↗

Study on deep neural networks using branching processes and Mehler's formula.

problem Understanding the mathematical role of activation functions in compositional neural networks.
method Connection between compositional kernels and branching processes via Mehler's formula; new random features algorithm.
result Explicit formulas for eigenvalues of compositional kernels quantify complexity.

We establish conditions for compositional generalization in machine learning.

problem Achieving compositional generalization in machine learning models.
method We reformulate compositionality as a property of the data-generating process and derive mild conditions on the training distribution and model architecture.
result Our theoretical framework enables compositional generalization under mild conditions.

Develops methods for causal inference in compositional data using instrumental variables.

problem Interpreting summary statistics like diversity indices as causal effects in compositional data.
method Statistical data transformations and regression techniques tailored for compositional data.
result Advantages and limitations of the proposed methods demonstrated on synthetic and real microbiome data.

In classical field theory, the composite fibred manifolds Y -> Z -> X provides the adequate mathematical formulation of gauge models with broken symmetries, e.g., the gauge gravitation theory. This work is devoted to connections on composite fibred manifolds. In particular, we get the horizontal splitting of the vertic…

1994-12-17abs ↗pdf ↗