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The paper considers causal smoothing of the real sequences, i.e.,discrete time processes in a deterministic setting. A family of causal linear time-invariant filters is suggested. These filters approximate the gain decay for some non-causal…

Optimization and Control · Mathematics 2015-07-07 Nikolai Dokuchaev

Identification of causal structures and quantification of direct information flows in complex systems is a challenging yet important task, with practical applications in many fields. Data generated by dynamical processes or large-scale…

Data Analysis, Statistics and Probability · Physics 2015-08-06 Carlo Cafaro , Warren M. Lord , Jie Sun , Erik M. Bollt

The aim of this paper is to discuss a recent result which shows that probabilistic inference in the presence of (unknown) causal mechanisms can be tractable for models that have traditionally been viewed as intractable. This result was…

Artificial Intelligence · Computer Science 2022-02-08 Adnan Darwiche

In this paper, C1-conforming element methods are analyzed for the stream function formulation of a single layer non-stationary quasi-geostrophic equation in the ocean circulation model. In its first part, some new regularity results are…

Numerical Analysis · Mathematics 2024-11-19 Dohyun Kim , Amiya K. Pani , Eun-Jae Park

We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, i.e., models where the evaluation of the likelihood function is intractable or too computationally expensive, but where one can…

Machine Learning · Computer Science 2025-05-28 Simon Dirmeier , Antonietta Mira

A finite element methodology for large classes of variational boundary value problems is defined which involves discretizing two linear operators: (1) the differential operator defining the spatial boundary value problem; and (2) a Riesz…

Numerical Analysis · Mathematics 2017-12-08 Brendan Keith , Socratis Petrides , Federico Fuentes , Leszek Demkowicz

Causal inference is central to statistics and scientific discovery, enabling researchers to identify cause-and-effect relationships beyond associations. While traditionally studied within Euclidean spaces, contemporary applications…

Methodology · Statistics 2025-07-01 Satarupa Bhattacharjee , Bing Li , Xiao Wu , Lingzhou Xue

We present a categorical framework for relating causal models that represent the same system at different levels of abstraction. We define a causal abstraction as natural transformations between appropriate Markov functors, which concisely…

Machine Learning · Statistics 2025-10-07 Markus Englberger , Devendra Singh Dhami

We consider optimization problems that are formulated and solved in the framework of tropical mathematics. The problems consist in minimizing or maximizing functionals defined on vectors of finite-dimensional semimodules over idempotent…

Optimization and Control · Mathematics 2014-08-05 Nikolai Krivulin

We investigate infinitary wellfounded systems for linear logic with fixed points, with transfinite branching rules indexed by some closure ordinal $\alpha$ for fixed points. Our main result is that provability in the system for some…

Logic · Mathematics 2026-02-24 Anupam Das , Tikhon Pshenitsyn

As model problem we consider the prototype for flow and transport of a concentration in porous media in an interior domain and couple it with a diffusion process in the corresponding unbounded exterior domain. To solve the problem we…

Numerical Analysis · Mathematics 2018-10-22 Christoph Erath , Günther Of , Francisco-Javier Sayas

We classify certain sofic shifts (the irreducible Point Extension Type, or PET, sofic shifts) up to flow equivalence, using invariants of the canonical Fischer cover. There are two main ingredients: (1) An extension theorem, for extending…

Dynamical Systems · Mathematics 2018-10-08 Mike Boyle , Toke Meier Carlsen , Søren Eilers

Clustering is a fundamental tool for analyzing large data sets. A rich body of work has been devoted to designing data-stream algorithms for the relevant optimization problems such as $k$-center, $k$-median, and $k$-means. Such algorithms…

Data Structures and Algorithms · Computer Science 2018-12-06 Kook Jin Ahn , Graham Cormode , Sudipto Guha , Andrew McGregor , Anthony Wirth

A finite element model was developed to compute the fluid flow inside a sessile evaporating droplet on hydrophilic substrate in ambient conditions. The evaporation is assumed as quasi-steady and the flow is considered as axisymmetric with a…

Fluid Dynamics · Physics 2020-09-08 Manish Kumar , Rajneesh Bhardwaj

Beginning with the projectively invariant method for linear programming, interior point methods have led to powerful algorithms for many difficult computing problems, in combinatorial optimization, logic, number theory and non-convex…

Numerical Analysis · Computer Science 2014-12-11 Narendra Karmarkar

Computation models such as circuits describe sequences of computation steps that are carried out one after the other. In other words, algorithm design is traditionally subject to the restriction imposed by a fixed causal order. We address a…

Quantum Physics · Physics 2017-07-04 Ämin Baumeler , Stefan Wolf

Causal reasoning is essential for understanding decision-making about the behaviour of complex `ecosystems' of systems that underpin modern society, with security -- including issues around correctness, safety, resilience, etc. -- typically…

Logic in Computer Science · Computer Science 2025-08-05 Pinaki Chakraborty , Tristan Caulfield , David Pym

The third-order Jeffery-Hamel ODE governing the flow of an incompressible fluid in a two-dimensional wedge is briefly derived, and a C^1 finite element formulation of the equation is developed. This formulation has several advantages,…

Numerical Analysis · Mathematics 2016-12-20 John W. Peterson , Roy H. Stogner

Temporal stream logic (TSL) extends LTL with updates and predicates over arbitrary function terms. This allows for specifying data-intensive systems for which LTL is not expressive enough. In the semantics of TSL, functions and predicates…

Logic in Computer Science · Computer Science 2022-01-26 Bernd Finkbeiner , Philippe Heim , Noemi Passing

Structural Equation Models (SEM) are the standard approach to representing causal dependencies between variables in causal models. In this paper we propose a new interpretation of SEMs when reasoning about Actual Causality, in which SEMs…

Artificial Intelligence · Computer Science 2025-12-23 Maksim Gladyshev , Natasha Alechina , Mehdi Dastani , Dragan Doder , Brian Logan