Tootfinder

Docs: Oracle generated ~$900M from its Nvidia cloud server rental business, with a $125M gross profit, or a 14% margin, vs. its ~70% overall gross profit margin (The Information)
https://www.theinformation.com/articles/internal-oracl…

Internal Oracle Data Show Financial Challenge of Renting Out Nvidia Chips
Oracle became the best-performing megacap stock of 2025 after its executives said last month that the once-sleepy database firm will generate an astonishing $381 billion in revenue from renting out specialized cloud servers to OpenAI and other artificial intelligence developers over the next ...

A Bug at Social Security Admin Has Been Rerouting Phone Calls to Random Offices
https://gizmodo.com/a-bug-at-social-security-admin-has-been-rerouting-phone-calls-to-random-offices-2000640762

Randomness in Narrative Video Games: Aesthetics of Different Random Systems
https://azhdarchid.com/randomness-in-narrative-video-games-aesthetics-of-different-random-systems/
Some of these considerations & differences als…

Randomness in Narrative Video Games: Aesthetics of Different Random Systems
On virtual dice, cards, and other alternatives.

Using dense graph limit theory to count cocycles of random simplicial complexes
Andr\'as M\'esz\'aros
https://arxiv.org/abs/2509.06559 https://…

Using dense graph limit theory to count cocycles of random simplicial complexes
We develop a limit theory for $1$-cochains of complete graphs with coefficients from a finite abelian group. We prove an analogue of the large deviation principle of Chatterjee and Varadhan for random cochains. We use these new tools to prove results about the homology of random $2$-dimensional simplicial complexes. More specifically, we prove that if $T_n$ is a random $2$-dimensional determinantal hypertree on $n$ vertices and $p$ is any prime, then \[\frac{\dim H_1(T_n,\mathbb{F}_p)}{n^2}\]…

On the entropy growth of sums of iid discrete random variables
Riccardo Castellano, Pavel Sekatski
https://arxiv.org/abs/2508.05348 https://arxiv.org/pdf/2…

On the entropy growth of sums of iid discrete random variables
We derive an asymptotic lower bound on the Shannon entropy $H$ of sums of $N$ arbitrary iid discrete random variables. The derived bound $H \geq \frac{r(X)}{2}\log(N) + {\it cst}$ is given in terms of the incommensurability rank $r(X)$ of the random variable -- a positive integer quantity that we introduce. The derivation does not rely on central limit theorems, but builds upon the known expressions of the asymptotic entropy of the multinomial distribution and sums of iid lattice random variabl…

Sequential Least-Squares Estimators with Fast Randomized Sketching for Linear Statistical Models
Guan-Yu Chen, Xi Yang
https://arxiv.org/abs/2509.06856 https://

Sequential Least-Squares Estimators with Fast Randomized Sketching for Linear Statistical Models
We propose a novel randomized framework for the estimation problem of large-scale linear statistical models, namely Sequential Least-Squares Estimators with Fast Randomized Sketching (SLSE-FRS), which integrates Sketch-and-Solve and Iterative-Sketching methods for the first time. By iteratively constructing and solving sketched least-squares (LS) subproblems with increasing sketch sizes to achieve better precisions, SLSE-FRS gradually refines the estimators of the true parameter vector, ultimat…

Very good essay on the situation in the UK with Palestinian Action and protest bans.
David Renton · Short Cuts: What is the meaning of support?
https://www.lrb.co.uk/the-paper/v47/n14/david-renton/short-cuts

David Renton · Short Cuts: What is the meaning of support?
With the proscription of Palestine Action early in July, the question of what support for a terrorist group means has...

Sharks fear him
#ParkerStevenson
Season 1 Episode 19 "Shark Derby"
#RandomBaywatch #lvdlpx

Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation
Muquan Li, Hang Gou, Dongyang Zhang, Shuang Liang, Xiurui Xie, Deqiang Ouyang, Ke Qin
https://arxiv.org/abs/2510.04838

Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation
The growing demand for efficient deep learning has positioned dataset distillation as a pivotal technique for compressing training dataset while preserving model performance. However, existing inner-loop optimization methods for dataset distillation typically rely on random truncation strategies, which lack flexibility and often yield suboptimal results. In this work, we observe that neural networks exhibit distinct learning dynamics across different training stages-early, middle, and late-maki…

Sharp thresholds for NAC-colourings and stable cuts in random graphs
Katie Clinch, John Haslegrave, Tony Huynh, Anthony Nixon
https://arxiv.org/abs/2510.05838 https://

Sharp thresholds for NAC-colourings and stable cuts in random graphs
NAC-colourings of graphs correspond to flexible quasi-injective realisations in $\mathbb {R} ^2$. A special class of NAC-colourings are those that arise from stable cuts. We give sharp thresholds for the random graph to have no stable cut and to have no NAC-colouring via exact hitting-time results: with high probability, the random graph process gains both properties at the precise time that every vertex is in a triangle. Our thresholds complement recent results on the thresholds for the random…