arXiv research

We prove that any totally geodesic hypersurface $N^5$ of a 6-dimensional nearly Kähler manifold $M^6$ is a Sasaki-Einstein manifold, and so it has a hypo structure in the sense of \cite{ConS}. We show that any Sasaki-Einstein 5-manifold defines a nearly Kähler structure on the sin-cone $N^5\times\mathbb R$, and a compa…

This note constructs complex structures on specific isoparametric hypersurfaces.

Study nearly half-flat SU(3)-structures on S³×S³.

The paper extends Marsden-Weinstein reduction to mechanical presymplectic structures for time-dependent Hamiltonian systems.

Researchers found all homogeneous structure tensors on two specific 3D manifolds.

Improved prediction of hierarchical time series using structured regularization.

SCOTCH learns system structure from irregular time series using neural SDEs.

The paper identifies short-term and long-term time scales in stock markets with and without structural breaks.

CSTS benchmarks time series clustering by evaluating correlation structures.

It has been understood that the "local" existence of the Markowitz' optimal portfolio or the solution to the local-risk minimization problem is guaranteed by some specific mathematical structures on the underlying assets price processes known in the literature as "{\it Structure Conditions}". In this paper, we consider…

Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is…

Paper predicts future graph structures using time series methods.

Flexible Cox model for time-dependent covariates with complex sparsity patterns.

This work proposes a method to learn graph structure for multivariate time series forecasting.

We show that if a manifold M admits a contact structure, then so does M\times S^2. Our proof relies on surgery theory, a theorem of Eliashberg on contact surgery and a theorem of Bourgeois showing that if M admits a contact structure then so does M\times T^2.

Proposes a new model to analyze mortgage delinquency transitions.

Proposes DCNAR for dynamic causal inference from neural time series.

Study establishes time functions in Lorentzian spaces without requiring manifold structure.

Proves and tests methods for learning time-series with breaks.

Study coclosed G2-structures on SU(2)²-invariant manifolds.

The paper studies a heat flow for almost complex structures and proves convergence under certain conditions.

An approach is proposed to determine structural shift in time-series assuming non-linear dependence of lagged values of dependent variable. Copulas are used to model non-linear dependence of time series components.

In this note we show that Lorentzian Concircular Structure manifolds (LCS)_n coincide with Generalized Robertson-Walker space-times.

Classically time is kept fixed for infinitesimal variations in problems in mechanics. Apparently, there appears to be no mathematical justification in the literature for this standard procedure. This can be explained canonically by unveiling the intrinsic mathematical structure of time in Lagrangian mechanics. Moreover…

We introduce a flow of $G_2$-structures defining the same underlying Riemannian metric, whose stationary points are those structures with divergence-free torsion. We show short-time existence and uniqueness of the solution.

Dynamic Structural Causal Models handle time-dependent systems with cycles and latent confounding.

We find a normal form for two-input flat discrete-time systems.

New method identifies nonstationary causal structures in time series data.

No left-invariant hypercomplex structures found on compact Lie groups.

The paper studies geometric quantization and coherent state transforms on Lie groups.

We construct an infinite family of mutually non-diffeomorphic irreducible smooth structures on the topological 4-manifold $S^2 \times S^2$.

SNS-GAN integrates class labels into generative models for images and time series.

We study the geometry of Engel structures, which are 2-plane fields on 4-manifolds satisfying a generic condition, that are compatible with other geometric structures. A complex Engel structure is an Engel 2-plane field on a complex surface for which the 2-planes are complex lines. We solve the equivalence problems for…

We consider Courant and Courant-Jacobi brackets on the stable tangent bundle $TM\times\mathds{R}^h$ of a differentiable manifold and corresponding Dirac, Dirac-Jacobi and generalized complex structures. We prove that Dirac and Dirac-Jacobi structures on $TM\times\mathds{R}^h$ can be prolonged to $TM\times\mathds{R}^k$,…

New method discovers accurate time series models using SMC and MCMC.

TADA detects anomalies in time series using topological data analysis.

Lightlike hypersurfaces in cone structures minimize time.

Constructs deformed G2-instantons on specific G2-manifolds.

New method learns CTBN structures from incomplete data.

Methods for detecting structural changes, or change points, in time series data are widely used in many fields of science and engineering. This chapter sketches some basic methods for the analysis of structural changes in time series data. The exposition is confined to retrospective methods for univariate time series. …

This paper provides a topological method for filling contact structures on the connected sums of $S^2\times S^3$. Examples of nonsymplectomorphic strong fillings of homotopy equivalent contact structures with vanishing first Chern class on $\#_k S^2\times S^3$ $(k\geq2)$ are produced.

Tackles network structure inference from time series data using GNN.

Structured prediction is a powerful framework for coping with joint prediction of interacting outputs. A central difficulty in using this framework is that often the correct label dependence structure is unknown. At the same time, we would like to avoid an overly complex structure that will lead to intractable predicti…

There exist non-degenerate 3-form $dω_I$, $ω_I(X,Y)=g(IX,Y)$, for each leftinvariant almost Hermitian structure $(g,I)$, where $g$ is Killing-Cartan metric on the $M=S^3\times S^3=SU(2)\times SU(2)$. Known \cite{H1}, that arbitrary non-degenerate 3-form on the 6-dimensional manifold, with some additional properties def…

A $(TE)$-structure $\nabla$ over a complex manifold $M$ is a meromorphic connection defined on a holomorphic vector bundle over $\mathbb{C}\times M$, with poles of Poincaré rank one along $\{ 0 \} \times M.$ Under a mild additional condition (the so called unfolding condition), $\nabla$ induces a multiplication on $TM$…

Graph neural networks detect structural perturbations from time series data.

Paper encodes textile structures and classifies them up to complexity five.

Analyzing multivariate time series data is important to predict future events and changes of complex systems in finance, manufacturing, and administrative decisions. The expressiveness power of Gaussian Process (GP) regression methods has been significantly improved by compositional covariance structures. In this paper…