Tootfinder

Efficient Approximate Temporal Triangle Counting in Streaming with Predictions
Giorgio Venturin, Ilie Sarpe, Fabio Vandin
https://arxiv.org/abs/2506.13173 …

Efficient Approximate Temporal Triangle Counting in Streaming with Predictions
Triangle counting is a fundamental and widely studied problem on static graphs, and recently on temporal graphs, where edges carry information on the timings of the associated events. Streaming processing and resource efficiency are crucial requirements for counting triangles in modern massive temporal graphs, with millions of nodes and up to billions of temporal edges. However, current exact and approximate algorithms are unable to handle large-scale temporal graphs. To fill such a gap, we int…

Machine Learning Acceleration of Neutron Star Pulse Profile Modeling
Preston G. Waldrop, Dimitrios Psaltis, Tong Zhao
https://arxiv.org/abs/2506.11194 http…

Machine Learning Acceleration of Neutron Star Pulse Profile Modeling
Ray tracing algorithms that compute pulse profiles from rotating neutron stars are essential tools for constraining neutron-star properties with data from missions such as NICER. However, the high computational cost of these simulations presents a significant bottleneck for inference algorithms that require millions of evaluations, such as Markov Chain Monte Carlo methods. In this work, we develop a residual neural network model that accelerates this calculation by predicting the observed flux …

This https://arxiv.org/abs/2503.21224 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Efficient Learning for Entropy-Regularized Markov Decision Processes via Multilevel Monte Carlo
Designing efficient learning algorithms with complexity guarantees for Markov decision processes (MDPs) with large or continuous state and action spaces remains a fundamental challenge. We address this challenge for entropy-regularized MDPs with Polish state and action spaces, assuming access to a generative model of the environment. We propose a novel family of multilevel Monte Carlo (MLMC) algorithms that integrate fixed-point iteration with MLMC techniques and a generic stochastic approximat…

Evaluating the Contextual Integrity of False Positives in Algorithmic Travel Surveillance
Alina Wernick, Alan Medlar, Sofia S\"oderholm, Dorota G{\l}owacka
https://arxiv.org/abs/2506.00218

Evaluating the Contextual Integrity of False Positives in Algorithmic Travel Surveillance
International air travel is highly surveilled. While surveillance is deemed necessary for law enforcement to prevent and detect terrorism and other serious crimes, even the most accurate algorithmic mass surveillance systems produce high numbers of false positives. Despite the potential impact of false positives on the fundamental rights of millions of passengers, algorithmic travel surveillance is lawful in the EU. However, as the system's processing practices and accuracy are kept secret by l…

The geometric bookkeeping guide to Feynman integral reduction and $\varepsilon$-factorised differential equations
Iris Bree, Federico Gasparotto, Antonela Matija\v{s}i\'c, Pouria Mazloumi, Dmytro Melnichenko, Sebastian P\"ogel, Toni Teschke, Xing Wang, Stefan Weinzierl, Konglong Wu, Xiaofeng Xu
https://arxiv.org/abs/250…

The geometric bookkeeping guide to Feynman integral reduction and $\varepsilon$-factorised differential equations
We report on three improvements in the context of Feynman integral reduction and $\varepsilon$-factorised differential equations: Firstly, we show that with a specific choice of prefactors, we trivialise the $\varepsilon$-dependence of the integration-by-parts identities. Secondly, we observe that with a specific choice of order relation in the Laporta algorithm, we directly obtain a basis of master integrals, whose differential equation on the maximal cut is in Laurent polynomial form with res…