papers

Publications (9)

math-ph2019

Model-independent comparison between factorization algebras and algebraic quantum field theory on Lorentzian manifolds

Marco Benini, Marco Perin, Alexander Schenkel

This paper investigates the relationship between algebraic quantum field theories and factorization algebras on globally hyperbolic Lorentzian manifolds. Functorial constructions t…

eess.SY2024

Star-shaped Tilted Hexarotor Maneuverability: Analysis of the Role of the Tilt Cant Angles

Marco Perin, Massimiliano Bertoni, Nicolas Viezzer +2

Star-shaped Tilted Hexarotors are rapidly emerging for applications highly demanding in terms of robustness and maneuverability. To ensure improvement in such features, a careful s…

math-ph2021

Categorification of algebraic quantum field theories

Marco Benini, Marco Perin, Alexander Schenkel +1

This paper develops a concept of 2-categorical algebraic quantum field theories (2AQFTs) that assign locally presentable linear categories to spacetimes. It is proven that ordinary…

math.SP2010

On the sharpness of a certain spectral stability estimate for the Dirichlet Laplacian

Pier Domenico Lamberti, Marco Perin

We consider a spectral stability estimate by Burenkov and Lamberti concerning the variation of the eigenvalues of second order uniformly elliptic operators on variable open sets in…

cs.AI2025

AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models

Conor Heins, Toon Van de Maele, Alexander Tschantz +11

Current deep reinforcement learning (DRL) approaches achieve state-of-the-art performance in various domains, but struggle with data efficiency compared to human learning, which le…

math-ph2021

Smooth 1-dimensional algebraic quantum field theories

Marco Benini, Marco Perin, Alexander Schenkel

This paper proposes a refinement of the usual concept of algebraic quantum field theories (AQFTs) to theories that are smooth in the sense that they assign to every smooth family o…

cs.LG2025

Soft Geometric Inductive Bias for Object Centric Dynamics

Hampus Linander, Conor Heins, Alexander Tschantz +2

Equivariance is a powerful prior for learning physical dynamics, yet exact group equivariance can degrade performance if the symmetries are broken. We propose object-centric world…

stat.ML2025

AutoBayes: A Compositional Framework for Generalized Variational Inference

Toby St Clere Smithe, Marco Perin

We introduce a new compositional framework for generalized variational inference, clarifying the different parts of a model, how they interact, and how they compose. We explain tha…

eess.SY2023

Trajectory Tracking for Tilted Hexarotors with Concurrent Attitude Regulation

Marco Perin, Massimiliano Bertoni, Giulia Michieletto +2

Tilted hexarotors embody a technology that remains partially unexploited in terms of its potential, especially concerning precise and concurrent position and attitude control. Focu…