Tootfinder

FOGMACHINE -- Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents
Lars Ohnemus, Nils Hantke, Max Wei{\ss}er, Kai Furmans
https://arxiv.org/abs/2510.09483

FOGMACHINE -- Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents
Dynamic Scene Graphs (DSGs) provide a structured representation of hierarchical, interconnected environments, but current approaches struggle to capture stochastic dynamics, partial observability, and multi-agent activity. These aspects are critical for embodied AI, where agents must act under uncertainty and delayed perception. We introduce FOGMACHINE , an open-source framework that fuses DSGs with discrete-event simulation to model object dynamics, agent observations, and interactions at scal…

Timeline Problems in Temporal Graphs: Vertex Cover vs. Dominating Set
Anton Herrmann, Christian Komusiewicz, Nils Morawietz, Frank Sommer
https://arxiv.org/abs/2510.08124 https:…

Timeline Problems in Temporal Graphs: Vertex Cover vs. Dominating Set
A temporal graph is a finite sequence of graphs, called snapshots, over the same vertex set. Many temporal graph problems turn out to be much more difficult than their static counterparts. One such problem is \textsc{Timeline Vertex Cover} (also known as \textsc{MinTimeline$_\infty$}), a temporal analogue to the classical \textsc{Vertex Cover} problem. In this problem, one is given a temporal graph $\mathcal{G}$ and two integers $k$ and $\ell$, and the goal is to cover each edge of each snapsho…

Wat in ieder geval weer blijkt is dat de peilingen voortaan beter achterwege gelaten kunnen worden.
In geen enkele peiling is voor GL-PvdA minder dan 23 zetels voorspeld.
Zelfs met onzekerheidsmarge erbij komt het nauwelijks in de buurt van de 20.

Restrictions of PCBNs for integration-free computations
Alexis Derumigny, Niels Horsman, Dorota Kurowicka
https://arxiv.org/abs/2510.03518 https://arxiv.or…

Restrictions of PCBNs for integration-free computations
The pair-copula Bayesian Networks (PCBN) are graphical models composed of a directed acyclic graph (DAG) that represents (conditional) independence in a joint distribution. The nodes of the DAG are associated with marginal densities, and arcs are assigned with bivariate (conditional) copulas following a prescribed collection of parental orders. The choice of marginal densities and copulas is unconstrained. However, the simulation and inference of a PCBN model may necessitate possibly high-dimen…