Tootfinder

Deciding Termination of Simple Randomized Loops
\'El\'eanore Meyer, J\"urgen Giesl
https://arxiv.org/abs/2506.18541 https://

Deciding Termination of Simple Randomized Loops
We show that universal positive almost sure termination (UPAST) is decidable for a class of simple randomized programs, i.e., it is decidable whether the expected runtime of such a program is finite for all inputs. Our class contains all programs that consist of a single loop, with a linear loop guard and a loop body composed of two linear commuting and diagonalizable updates. In each iteration of the loop, the update to be carried out is picked at random, according to a fixed probability. We s…

Learning graphons from data: Random walks, transfer operators, and spectral clustering
Stefan Klus, Jason J. Bramburger
https://arxiv.org/abs/2507.18147 https://

Learning graphons from data: Random walks, transfer operators, and spectral clustering
Many signals evolve in time as a stochastic process, randomly switching between states over discretely sampled time points. Here we make an explicit link between the underlying stochastic process of a signal that can take on a bounded continuum of values and a random walk process on a graphon. Graphons are infinite-dimensional objects that represent the limit of convergent sequences of graphs whose size tends to infinity. We introduce transfer operators, such as the Koopman and Perron--Frobeniu…

🇺🇦 #NowPlaying on KEXP's #DriveTime
Kevin Morby:
🎵 A Random Act of Kindness
#KevinMorby
https://kevinmorby.bandcamp.com/track/a-random-act-of-kindness
https://open.spotify.com/track/5GepgBdAV6eI3RoijuHZua

Large-scale entity resolution via microclustering Ewens--Pitman random partitions
Mario Beraha, Stefano Favaro
https://arxiv.org/abs/2507.18101 https://arx…

Large-scale entity resolution via microclustering Ewens--Pitman random partitions
We introduce the microclustering Ewens--Pitman model for random partitions, obtained by scaling the strength parameter of the Ewens--Pitman model linearly with the sample size. The resulting random partition is shown to have the microclustering property, namely: the size of the largest cluster grows sub-linearly with the sample size, while the number of clusters grows linearly. By leveraging the interplay between the Ewens--Pitman random partition with the Pitman--Yor process, we develop effici…

Quenched scaling limit for biased random walks on random, heavy tailed conductances: low dimensions
Umberto De Ambroggio, Carlo Scali
https://arxiv.org/abs/2507.17583 https://…

Quenched scaling limit for biased random walks on random, heavy tailed conductances: low dimensions
We consider a random walk amongst positive random conductances on $\mathbb{Z}^d, d \ge 2$, with directional bias. When the conductances have a stable distribution with parameter $γ\in (0, 1)$, the walk is sub-ballistic. In this regime Fribergh and Kious (Ann. Prob. 2018) derived an annealed scaling limit for the appropriately rescaled walk towards the Fractional Kinetics process. We prove the quenched version of this result for all $d \ge 2$.

This headline tells you where the US is heading, fascism. Also it tells us to avoid any travel to the US if possible.
"SCOTUS allows Trump admin to deport people to random countries with no notice"
https://www.lawdork.com/p/breaking-scotus-allows-trump-admin

SCOTUS allows Trump admin to deport people to random countries with no notice
Justice Sotomayor called the order a "gross ... abuse" of the court's authority. Also: Law Dork in the media.

I'm the kind of friend that you sometimes don't hear from for months or even years, then randomly DM's you something I knew you liked back then (like a video of a specific kind of bird I randomly spotted in my feed).

ZORMS-LfD: Learning from Demonstrations with Zeroth-Order Random Matrix Search
Olivia Dry, Timothy L. Molloy, Wanxin Jin, Iman Shames
https://arxiv.org/abs/2507.17096

ZORMS-LfD: Learning from Demonstrations with Zeroth-Order Random Matrix Search
We propose Zeroth-Order Random Matrix Search for Learning from Demonstrations (ZORMS-LfD). ZORMS-LfD enables the costs, constraints, and dynamics of constrained optimal control problems, in both continuous and discrete time, to be learned from expert demonstrations without requiring smoothness of the learning-loss landscape. In contrast, existing state-of-the-art first-order methods require the existence and computation of gradients of the costs, constraints, dynamics, and learning loss with re…

The Bivariate regular variation of randomly weighted sums revisited in the presence of interdependence
Dimitrios G. Konstantinides, Charalampos D. Passalidis
https://arxiv.org/abs/2506.17895

The Bivariate regular variation of randomly weighted sums revisited in the presence of interdependence
We study the closure properties of the class of Bivariate Regular Variation, symbolically BRV , in standard and nonstandard cases, with respect to the randomly weighted sums. However, we take into consideration a weak dependence structure among the random weights and the main random variables, providing more flexibility in applications. In the first main result, under the condition that the main random variables belong to BRV , we provide a dependent bivariate extension of Breiman's theorem, un…

Random walk reflected off of infinity, with applications to uniform spanning forests and supercritical Liouville quantum gravity
Ewain Gwynne, Jinwoo Sung
https://arxiv.org/abs/2506.18827

Random walk reflected off of infinity, with applications to uniform spanning forests and supercritical Liouville quantum gravity
Let $\mathcal G$ be an infinite graph -- not necessarily one-ended -- on which the simple random walk is transient. We define a variant of the continuous-time random walk on $\mathcal G$ which reaches $\infty$ in finite time and "reflects off of $\infty$" infinitely many times. We show that the Aldous-Broder algorithm for the random walk reflected off of $\infty$ gives the free uniform spanning forest (FUSF) on $\mathcal G$. Furthermore, Wilson's algorithm for the random walk reflected off of…