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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.

168,695 papers · 148 categories

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130260389519 · Jun 202019922001200920172026
48 results for Analytic Hierarchy Process

Study evaluates sustainability of European banks using a new model.

problem Lack of a framework to evaluate sustainability of banking business models.
method Delphi-Analytic Hierarchy Process method to develop and assess the model.
result Norwegian and German banks have higher sustainability of their business models.

In this paper we describe market in projective geometry language and give definition of a matrix of market rate, which is related to the matrix rate of return and the matrix of judgements in the Analytic Hierarchy Process (AHP). We use these observations to extend the AHP model to projective geometry formalism and gene…

2007-09-27abs ↗pdf ↗

This study proposes an exercise fatigue detection model based on real-time clinical data which includes time domain analysis, frequency domain analysis, detrended fluctuation analysis, approximate entropy, and sample entropy. Furthermore, this study proposed a feature extraction method which is combined with an analyti…

2018-03-07abs ↗pdf ↗

We introduce a structure of an infinite-dimensional Frobenius manifold on a subspace in the space of pairs of functions analytic inside/outside the unit circle with simple poles at 0/infinity respectively. The dispersionless 2D Toda equations are embedded into a bigger integrable hierarchy associated with this Frobeniu…

2009-02-09abs ↗pdf ↗

Main mathematical applications of Frobenius manifolds are in the theory of Gromov - Witten invariants, in singularity theory, in differential geometry of the orbit spaces of reflection groups and of their extensions, in the hamiltonian theory of integrable hierarchies. The theory of Frobenius manifolds establishes rema…

1998-07-08abs ↗pdf ↗

A new hierarchy quantifies agency in systems based on information processing.

problem Lack of a measurable, universal definition for agency in intelligent systems.
method Developed a bottom-up framework based on information processing hierarchy.
result Identified three orders of information processing (I, II, III) as necessary for agency.

DiffEnc improves diffusion models by adding flexibility and achieving better likelihood on CIFAR-10.

problem Improving the likelihood of diffusion models on image datasets.
method Introducing a data- and depth-dependent mean function and a free weight parameter for noise variance.
result Achieved statistically significant improvement in likelihood on CIFAR-10.

We adapt tools from information theory to analyze how an observer comes to synchronize with the hidden states of a finitary, stationary stochastic process. We show that synchronization is determined by both the process's internal organization and by an observer's model of it. We analyze these components using the conve…

2010-07-30abs ↗pdf ↗

We propose dynamical systems trees (DSTs) as a flexible class of models for describing multiple processes that interact via a hierarchy of aggregating parent chains. DSTs extend Kalman filters, hidden Markov models and nonlinear dynamical systems to an interactive group scenario. Various individual processes interact a…

2012-07-11abs ↗pdf ↗

Constructs integrable hierarchies for generalized Frobenius manifolds with non-flat unity.

problem Integrable hierarchies for generalized Frobenius manifolds with non-flat unity.
method Constructs a bihamiltonian integrable hierarchy of hydrodynamic type.
result Integrable hierarchy possesses Virasoro symmetries and a tau structure.

A new method for non-rigid point set registration reduces computational complexity.

problem Efficiently registering non-rigid point sets with large numbers of points.
method Structured Analytic Coherent Point Drift (Analytic-CPD) reformulates CPD for structured analytic mappings.
result Analytic-CPD reduces computational complexity by controlling the deformation model's dimensionality.

To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet allocation, which infers posterior samples via a hybrid of stochastic-gradient MCMC and…

2018-03-04abs ↗pdf ↗

Study identifies pitfalls in assessing hierarchies for multi-class classification.

problem Lack of understanding in selecting hierarchies for multi-class classification.
method Analyzed and compared popular approaches to extracting hierarchies.
result Hierarchy quality becomes irrelevant when using powerful classifiers.

Solving different types of optimization models (including parameters fitting) for support vector machines on large-scale training data is often an expensive computational task. This paper proposes a multilevel algorithmic framework that scales efficiently to very large data sets. Instead of solving the whole training s…

2014-10-13abs ↗pdf ↗

Dense Associative Memories outperform classical networks in robustness and signal processing.

problem Improving neural network performance in adversarial attacks and weak signal processing.
method Relaxing replica symmetry in statistical mechanics of spin glasses to analyze unsupervised and supervised learning.
result Explicit analytical investigation of phase diagrams and storage capacities for Dense Associative Memories.

Any function can be constructed using a hierarchy of simpler functions through compositions. Such a hierarchy can be characterized by a binary rooted tree. Each node of this tree is associated with a function which takes as inputs two numbers from its children and produces one output. Since thinking about functions in …

2019-04-04abs ↗pdf ↗

Legendre transformations link related integrable hierarchies.

problem Understanding relationships between integrable hierarchies.
method Legendre-type transformations of generalized Frobenius manifolds.
result Linear reciprocal transformations link related hierarchies.

Twisted UU- and twisted U/KU/K-hierarchies are soliton hierarchies introduced by Terng to find higher flows of the generalized sine-Gordon equation. Twisted O(J,J)O(J)×O(J)\frac {O(J,J)}{O(J)\times O(J)}-hierarchies are among the most important classes of twisted hierarchies. In this paper, interesting first and higher flows of twi…

2011-03-31abs ↗pdf ↗

We propose a formally completely integrable extension of heat hierarchy based on the space of symmetries isomorphic to the Weyl algebra A1\mathcal{A}_1. The extended heat hierarchy will be the basic model for the analysis of the extension of KP hierarchy, and other integrable equations.

2014-01-19abs ↗pdf ↗

Wise's Quasiconvex Hierarchy Theorem classifying hyperbolic virtually compact special groups in terms of quasiconvex hierarchies played an essential role in Agol's proof of the Virtual Haken Conjecture. Answering a question of Wise, we construct a new virtual quasiconvex hierarchy for relatively hyperbolic virtually co…

2019-03-28abs ↗pdf ↗

New hierarchies derived from KP hierarchy using non-formal operators and Yang-Mills action.

problem Formal solutions of KP hierarchy and their non-formal counterparts.
method Developed new hierarchies of non-linear equations on non-formal pseudo-differential operators.
result Expressed one hierarchy as Yang-Mills action minimization.

We study the problem of topic modeling in corpora whose documents are organized in a multi-level hierarchy. We explore a parametric approach to this problem, assuming that the number of topics is known or can be estimated by cross-validation. The models we consider can be viewed as special (finite-dimensional) instance…

2014-09-11abs ↗pdf ↗

In this paper, we present Gamma-LSTM, an enhanced long short term memory (LSTM) unit, to enable learning of hierarchical representations through multiple stages of temporal abstractions. Gamma memory, a hierarchical memory unit, forms the central memory of Gamma-LSTM with gates to regulate the information flow into var…

2019-10-25abs ↗pdf ↗

Symmetry reduction of Painlevé IV to Flaschka-Newell Painlevé II

problem Isomonodromic deformation problem associated with rank-two meromorphic connections
method Symmetry Ψ(λ)=σ1Ψ(λ)σ1Ψ(-λ)= σ_1 Ψ(λ) σ_1
result Induced isomonodromic dynamics coincides with Flaschka-Newell Painlevé II hierarchy

Scroll structures on solutions of 4D integrable equations are involutive and governed by a dispersionless hierarchy.

problem Characterizing the geometry of solutions to 4D integrable equations.
method Defining rational normal scrolls and showing their involutivity.
result Involutive scroll structures are governed by a dispersionless integrable hierarchy.

A relation between the Goldstein-Petrich hierarchy for plane curves and the Toda lattice hierarchy is investigated. A representation formula for plane curves is given in terms of a special class of ττ-functions of the Toda lattice hierarchy. A representation formula for discretized plane curves is also discussed.

2013-10-26abs ↗pdf ↗

Constructs tri-Hamiltonian structure and Frobenius manifold for asymmetric gAL hierarchy

problem Tri-Hamiltonian structure and Frobenius manifold for asymmetric gAL hierarchy
method Local tri-Hamiltonian structure construction and Frobenius manifold construction
result Dispersionless limits of flows belong to Principal Hierarchy

New integrable deformations for topological hierarchies from Frobenius manifolds.

problem Integrable deformations of topological hierarchies from Frobenius manifolds.
method Construction of integrable deformations with polynomial tau-structures.
result Conjecture of universal object for Riemann--Hopf hierarchy.

We introduce two families of soliton hierarchies: the twisted hierarchies associated to symmetric spaces. The Lax pairs of these two hierarchies are Laurent polynomials in the spectral variable. Our constructions gives a hierarchy of commuting flows for the generalized sine-Gordon equation (GSGE), which is the Gauss-Co…

2010-10-27abs ↗pdf ↗

Paper investigates methods to improve classification by inducing a hierarchy from flat labels.

problem Improving classification performance on datasets lacking a natural hierarchy.
method The approach involves clustering conditional distributions and using a hierarchical classifier with the induced hierarchy.
result The methods can discover latent hierarchies and improve accuracy in various applications.

We prove that the extended Toda hierarchy of \cite{CDZ} admits nonabelian Lie algebra of infinitesimal symmetries isomorphic to the half of the Virasoro algebra. The generators LmL_m, m1m\geq -1 of the Lie algebra act by linear differential operators onto the tau function of the hierarchy. We also prove that the tau fu…

2003-08-15abs ↗pdf ↗

Modeling uncertainty in deep neural networks, despite recent important advances, is still an open problem. Bayesian neural networks are a powerful solution, where the prior over network weights is a design choice, often a normal distribution or other distribution encouraging sparsity. However, this prior is agnostic to…

2019-05-30abs ↗pdf ↗

Paper addresses limitations of traditional hierarchical clustering methods.

problem Traditional hierarchical clustering methods face limitations in binary trees and ultrametrics.
method Introduces the notion of a valid hierarchy and a two-step algorithm to construct a binary tree and prune it to enforce validity.
result Proposes a method to recover the finest valid hierarchy, which is not constrained to binary structures.

We present the Lax pair formalism for certain extension of the continuous limit of the classical Toda lattice hierarchy, provide a well defined notion of tau function for its solutions, and give an explicit formulation of the relationship between the CP1CP^1 topological sigma model and the extended Toda hierarchy. We al…

2003-06-29abs ↗pdf ↗

Proposes a neural network for accurate and reconciled hierarchical time series forecasting.

problem Forecasting and reconciling hierarchical time series data.
method Uses a deep neural network to directly produce accurate and reconciled forecasts, minimizing a customized loss function at training time.
result Our approach outperforms state-of-the-art competitors in hierarchical forecasting on real-world datasets.