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We generalize the clockwork theory in several directions. First, we consider beyond nearest neighbors interactions. Considering such interactions keeps a larger subgroup of the original $U(1)^{N+1}$ unbroken and can allow for different…

High Energy Physics - Phenomenology · Physics 2019-07-11 Ido Ben-Dayan

Given a cohomology theory, there is a well-known abstract way to define the dual homology theory using the theory of spectra. In [4] the author provides a more geometric construction of the homology theory, using a generalization of the…

Algebraic Topology · Mathematics 2013-01-25 Fabio Ferrari Ruffino

To any graph with external half-edges and internal masses, we associate canonical integrals which depend non-trivially on particle masses and momenta, and are always finite. They are generalised Feynman integrals which satisfy graphical…

Mathematical Physics · Physics 2023-11-23 Francis Brown

Time dependent signals in experimental techniques such as Nuclear Magnetic Resonance (NMR) and Muon Spin Relaxation (muSR) are often the result of an ensemble average over many microscopical dynamical processes. While there are a number of…

Quantum Physics · Physics 2021-05-07 Simone Sturniolo

We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding to each structural equation model, there is a directed…

Machine Learning · Statistics 2014-06-03 Jonas Peters , Peter Bühlmann

We formulate a model of gravitationally induced decoherence for photons starting from Maxwell theory coupled to linearised gravity, expressed in terms of Ashtekar-Barbero variables and treated as an open quantum field theoretic system. In…

General Relativity and Quantum Cosmology · Physics 2026-02-10 Max Joseph Fahn , Kristina Giesel , Roman Kemper

A brief introduction to Topological Quantum Field Theory as well as a description of recent progress made in the field is presented. I concentrate mainly on the connection between Chern-Simons gauge theory and Vassiliev invariants, and…

High Energy Physics - Theory · Physics 2008-02-03 J. M. F. Labastida

Chain graphs combine directed and undirected graphs and their underlying mathematics combines properties of the two. This paper gives a simplified definition of chain graphs based on a hierarchical combination of Bayesian (directed) and…

Artificial Intelligence · Computer Science 2013-02-21 Wray L. Buntine

We prove generalizations of the first and second Ray-Knight theorems, for a large class of non-symmetric strong Markov processes. These results link the local times of the Markov process with the squares of associated Gaussian processes.…

Probability · Mathematics 2026-02-20 P. J. Fitzsimmons , Jay Rosen

We show how to construct a topological Markov map of the interval whose invariant probability measure is the stationary law of a given stochastic chain of infinite order. In particular we caracterize the maps corresponding to stochastic…

Probability · Mathematics 2015-06-05 Pierre Collet , Antonio Galves

The Darwin field model addresses an approximation to Maxwell's equations where radiation effects are neglected. It allows to describe general quasistatic electromagnetic field phenomena including inductive, resistive and capacitive effects.…

Computational Engineering, Finance, and Science · Computer Science 2020-03-13 Markus Clemens , Bernhard Kähne , Sebastian Schöps

The method of constructing of extended phase space for singular theories which permits the consideration of covariant gauges without the introducing of a ghost fields, is proposed. The extension of the phase space is carried out by the…

High Energy Physics - Theory · Physics 2009-10-28 S. A. Gogilidze , A. M. Khvedelidze , V. N. Pervushin

We prove the periodicity conjecture for pairs of Dynkin diagrams using Fomin-Zelevinsky's cluster algebras and their (additive) categorification via triangulated categories.

Representation Theory · Mathematics 2012-04-17 Bernhard Keller

Gaussian process models are flexible, Bayesian non-parametric approaches to regression. Properties of multivariate Gaussians mean that they can be combined linearly in the manner of additive models and via a link function (like in…

Machine Learning · Statistics 2016-04-19 Alan D. Saul , James Hensman , Aki Vehtari , Neil D. Lawrence

Global Markov properties in mixed graphs are usually formulated in terms of the path-oriented m-separation or by use of augmented graphs (similar to moral graphs in the case of directed acyclic graphs). We provide an alternative…

Methodology · Statistics 2011-11-17 Michael Eichler

Using the renewal approach we prove Bernstein-like inequalities for additive functionals of geometrically ergodic Markov chains, thus obtaining counterparts of inequalities for sums of independent random variables. The coefficient in the…

Probability · Mathematics 2020-03-18 Michał Lemańczyk

Real causal processes may contain feedback loops and change over time. In this paper, we model cycles and non-stationary distributions using a mixture of directed acyclic graphs (DAGs). We then study the conditional independence (CI)…

Statistics Theory · Mathematics 2019-09-16 Eric V. Strobl

In this note, we realize the half-steps of a general class of Markov chains as alternating projections with respect to the reverse Kullback-Leibler divergence between convex sets of joint probability distributions. Using this…

Probability · Mathematics 2025-04-30 Deven Mithal , Lorenzo Orecchia

The covariant descripion is constructed for the Wick-type symbols on symplectic manifolds by means of the Fedosov procedure. The geometry of the manifolds admitting this symbol is explored. The superextended version of the Wick-type…

High Energy Physics - Theory · Physics 2007-05-23 V. A. Dolgushev , S. L. Lyakhovich , A. A. Sharapov

Link prediction aims to reveal missing edges in a graph. We address this task with a Gaussian process that is transformed using simplified graph convolutions to better leverage the inductive bias of the domain. To scale the Gaussian process…

Machine Learning · Computer Science 2020-02-12 Felix L. Opolka , Pietro Liò