Tootfinder

Reinforced sequential Monte Carlo for amortised sampling
Sanghyeok Choi, Sarthak Mittal, V\'ictor Elvira, Jinkyoo Park, Nikolay Malkin
https://arxiv.org/abs/2510.11711 https…

Reinforced sequential Monte Carlo for amortised sampling
This paper proposes a synergy of amortised and particle-based methods for sampling from distributions defined by unnormalised density functions. We state a connection between sequential Monte Carlo (SMC) and neural sequential samplers trained by maximum-entropy reinforcement learning (MaxEnt RL), wherein learnt sampling policies and value functions define proposal kernels and twist functions. Exploiting this connection, we introduce an off-policy RL training procedure for the sampler that uses …

ICE assaulting a pregnant woman.
Covering their license plates with fake spray-on snow to hide the numbers.
Getting pelted with snowballs by the crowd and responding with pepper spray — which only increased the snowballs.
Just this afternoon’s local news.
Video of the snowballs (CW: person being assaulted, dragged)
(UPDATE: video attached in case FB blocks; source URL is https://www.facebook.com/share/r/1ZaUBtkGCZ/)

Wijers moest wel sneuvelen want hij was een obstakel in het plan van ondernemers voor een rechts kabinet - Joop - BNNVARA
#NLpol #Wijers

One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
Zaid Khan, Archiki Prasad, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal
https://arxiv.org/abs/2510.12088

One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
Symbolic world modeling requires inferring and representing an environment's transitional dynamics as an executable program. Prior work has focused on largely deterministic environments with abundant interaction data, simple mechanics, and human guidance. We address a more realistic and challenging setting, learning in a complex, stochastic environment where the agent has only "one life" to explore a hostile environment without human guidance. We introduce OneLife, a framework that models world…

Ryser's Theorem for Simple Multi-Latin Rectangle
Amin Bahmanian
https://arxiv.org/abs/2509.11471 https://arxiv.org/pdf/2509.11471

Ryser's Theorem for Simple Multi-Latin Rectangle
We prove a general result on completing objects similar to Latin rectangles in which the number of occurrences of each symbol is prescribed, each cell contains multiple symbols, and no cell contains repeated symbols. This generalizes several results in the literature, and leads to confirming a conjecture of Cavenagh, Hämäläinen, Lefevre, and Stones. An $r\times s$ {\it $λ$-Latin rectangle} $L$ is an $r\times s$ array in which each cell contains a multiset of $λ$ elements from the set $\{…

heute nach vielzuvielen jahren endlich mal wieder einen #erstehilfe-kurs gemacht. danke an olaf von den johannitern für den abwechslungsreichen methoden-mix im kurs und die gute laune, an die kolleg:innen für die angenehme zusammenarbeit, und an unsere uni, dass sie solche betrieblichen kurse anbietet. 🙏
eine übersichtliche broschüre der johanniter gibts hier:

From Seinfeld to Shawshank, Rob Reiner changed Hollywood for ever https://www.theguardian.com/film/2025/dec/15/rob-reiner-director-this-is-spinal-tap-when-harry-met-sally?CMP=Share_iOSApp_Other

twitter: Twitter followers (2010)
A directed network of following relationships from Twitter, from a snowball sample crawl across "quality" users in 2009. A directed edge (i, j) indicates that user i follows user j.
This network has 465017 nodes and 834797 edges.
Tags: Social, Online, Unweighted
https://network…

"Frå kristenrett til kyrkjerett": Ny bok om norsk kirkehistorie, oppfŸlger til fjorårets kulturhistorie "Arven etter Moster: kristenrettens betydning i norsk kulturarv"

Structure-Aware Spectral Sparsification via Uniform Edge Sampling
Kaiwen He, Petros Drineas, Rajiv Khanna
https://arxiv.org/abs/2510.12669 https://arxiv.or…

Structure-Aware Spectral Sparsification via Uniform Edge Sampling
Spectral clustering is a fundamental method for graph partitioning, but its reliance on eigenvector computation limits scalability to massive graphs. Classical sparsification methods preserve spectral properties by sampling edges proportionally to their effective resistances, but require expensive preprocessing to estimate these resistances. We study whether uniform edge sampling-a simple, structure-agnostic strategy-can suffice for spectral clustering. Our main result shows that for graphs adm…