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Longitudinal data are important in numerous fields, such as healthcare, sociology and seismology, but real-world datasets present notable challenges for practitioners because they can be high-dimensional, contain structured missingness…

Machine Learning · Computer Science 2024-07-01 Maksim Sinelnikov , Manuel Haussmann , Harri Lähdesmäki

We present a general construction for dependent random measures based on thinning Poisson processes on an augmented space. The framework is not restricted to dependent versions of a specific nonparametric model, but can be applied to all…

Machine Learning · Statistics 2012-11-21 Nicholas J. Foti , Joseph D. Futoma , Daniel N. Rockmore , Sinead Williamson

Based on our previous study [IS3] on the stationary scattering theory for the Schrodinger operator on a manifold possessing an escape function we complete our investigation by doing the time-dependent counterpart. A particular class of…

Differential Geometry · Mathematics 2019-05-09 Kenichi Ito , Erik Skibsted

Time series modelling is essential for solving tasks such as predictive maintenance, quality control and optimisation. Deep learning is widely used for solving such problems. When managing complex manufacturing process with neural networks,…

Machine Learning · Computer Science 2020-11-17 Alexey Kurochkin

We prove an existence result for the time-dependent generalized Nash equilibrium problem under generalized convexity using a fixed point theorem. Furthermore, an application to the dynamic abstract economy is considered.

Optimization and Control · Mathematics 2017-09-20 John Cotrina , Javier Zúñiga

In this paper, we discuss the generalizations of exact supersymmetries present in the supersymmetrized sigma models. These generalizations are made by making the supersymmetric transformation parameter field-dependent. Remarkably, the…

High Energy Physics - Theory · Physics 2014-06-23 Rabin Banerjee , Sudhaker Upadhyay

We present a new method to describe the kinetics of driven lattice gases with particle-particle interactions beyond hard-core exclusions. The method is based on the time-dependent density functional theory for lattice systems and allows one…

Statistical Mechanics · Physics 2015-05-20 Marcel Dierl , Philipp Maass , Mario Einax

Explicit expressions for one point moments corresponding to stochastic Verhulst model driven by Markovian coloured dichotomous noise are presented. It is shown that the moments are the given functions of a decreasing exponent. The…

Chaotic Dynamics · Physics 2008-01-08 V. M. Loginov

In this paper, we introduce a new class of confluent hypergeometric functions of many variables, study their properties, and determine a system of partial differential equations that this function satisfies. It turns out that all the…

Analysis of PDEs · Mathematics 2019-08-21 Tuhtasin Ergashev

Motivated by the study of systems of higher order boundary value problems with functional boundary conditions, we discuss, by topological methods, the solvability of a fairly general class of systems of perturbed Hammerstein integral…

Classical Analysis and ODEs · Mathematics 2021-02-09 Gennaro Infante

The functional linear model extends the notion of linear regression to the case where the response and covariates are iid elements of an infinite dimensional Hilbert space. The unknown to be estimated is a Hilbert-Schmidt operator, whose…

Statistics Theory · Mathematics 2016-12-22 Tung Pham , Victor Panaretos

The aim of this paper is to establish the $H^1$ global well-posedness for Kirchhoff systems. The new approach to the construction of solutions is based on the asymptotic integrations for strictly hyperbolic systems with time-dependent…

Analysis of PDEs · Mathematics 2014-01-14 Tokio Matsuyama , Michael Ruzhansky

We provide exact analytical solutions for a two dimensional explicitly time-dependent non-Hermitian quantum system. While the time-independent variant of the model studied is in the broken PT-symmetric phase for the entire range of the…

Quantum Physics · Physics 2019-06-05 Andreas Fring , Thomas Frith

Designing effective models for learning time series representations is foundational for time series analysis. Many previous works have explored time series representation modeling approaches and have made progress in this area. Despite…

Machine Learning · Computer Science 2024-12-17 Mingyue Cheng , Jiqian Yang , Tingyue Pan , Qi Liu , Zhi Li

We present a unified framework for the analysis of space-time methods based on Galerkin-type time discretizations for parabolic and hyperbolic problems. Crucially, the stability analysis relies on a suitable choice of test functions to…

Numerical Analysis · Mathematics 2026-01-28 Sergio Gómez

Contact geometry allows to describe some thermodynamic and dissipative systems. In this paper we introduce a new geometric structure in order to describe time-dependent contact systems: cocontact manifolds. Within this setting we develop…

Mathematical Physics · Physics 2023-01-27 Manuel de León , Jordi Gaset , Xavier Gràcia , Miguel Carlos Muñoz-Lecanda , Xavier Rivas

Generalized linear models (GLMs) have been used quite effectively in the modeling of a mean response under nonstandard conditions, where discrete as well as continuous data distributions can be accommodated. The choice of design for a GLM…

Statistics Theory · Mathematics 2016-08-14 André I. Khuri , Bhramar Mukherjee , Bikas K. Sinha , Malay Ghosh

We derive global analytic representations of fundamental solutions for a class of linear parabolic systems with full coupling of first order derivative terms where coefficient may depend on space and time. Pointwise convergence of the…

Analysis of PDEs · Mathematics 2009-07-17 Joerg Kampen

We consider the model equation arising in the theory of viscoelasticity $$\partial_{tt} u-h_t(0)\Delta u -\int_{0}^\infty h_t'(s)\Delta u(t-s)d s+ f(u) = g.$$ Here, the main feature is that the memory kernel $h_t(\cdot)$ depends on time,…

Dynamical Systems · Mathematics 2016-03-24 Monica Conti , Valeria Danese , Claudio Giorgi , Vittorino Pata

We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular samples, missing data, or unaligned measurements from multiple…