Tootfinder

I have been an “end Canada’s connection to the Monarchy”* guy for awhile now. I heard Canadian constitutional expert Phillipe Lagasse say once that it would be a very big lift for Canada to do so because of the requirement to get all provinces to agree on a constitutional amendment, etc etc. In his opinion, the most likely way for it to happen, then, would be for the UK population to end the Royal family’s power themselves.
Seems that is starting to become more likely.
*I avoid using the term Republican because I don’t want to be confused with any notion of advocating for anything like the USA. A Canadian republic can look and function exactly the same as it does today while ending the royal head of state. Bermuda being the most recent example.
#epstein #uk #royalfamily #monarchy #republic #AbolishTheMonarchy
https://mastodon.scot/@ScottishGreens/115440697972592005

Ende des Liveblogs
Wir schließen den Liveblog für heute. Vielen Dank für Ihr Interesse.
📑 https://www.tagesschau.de/newsticker/liveblog-ukraine-mittwoch-532.html?at_medium=mastodon&at_campai…

Ukraine-Liveblog: ++ Stromabschaltungen in vielen Teilen der Ukraine ++
Nach erneuten russischen Angriffe auf Energieanlagen hat die Ukraine in vielen Landesteilen die Stromversorgung massiv eingeschränkt. Die Zahl junge Ukrainer, die als Schutzsuchende nach Deutschland kommen, ist im Sommer gestiegen.

S-D-RSM: Stochastic Distributed Regularized Splitting Method for Large-Scale Convex Optimization Problems
Maoran Wang, Xingju Cai, Yongxin Chen
https://arxiv.org/abs/2511.10133 https://arxiv.org/pdf/2511.10133 https://arxiv.org/html/2511.10133
arXiv:2511.10133v1 Announce Type: new
Abstract: This paper investigates the problems large-scale distributed composite convex optimization, with motivations from a broad range of applications, including multi-agent systems, federated learning, smart grids, wireless sensor networks, compressed sensing, and so on. Stochastic gradient descent (SGD) and its variants are commonly employed to solve such problems. However, existing algorithms often rely on vanishing step sizes, strong convexity assumptions, or entail substantial computational overhead to ensure convergence or obtain favorable complexity. To bridge the gap between theory and practice, we integrate consensus optimization and operator splitting techniques (see Problem Reformulation) to develop a novel stochastic splitting algorithm, termed the \emph{stochastic distributed regularized splitting method} (S-D-RSM). In practice, S-D-RSM performs parallel updates of proximal mappings and gradient information for only a randomly selected subset of agents at each iteration. By introducing regularization terms, it effectively mitigates consensus discrepancies among distributed nodes. In contrast to conventional stochastic methods, our theoretical analysis establishes that S-D-RSM achieves global convergence without requiring diminishing step sizes or strong convexity assumptions. Furthermore, it achieves an iteration complexity of $\mathcal{O}(1/\epsilon)$ with respect to both the objective function value and the consensus error. Numerical experiments show that S-D-RSM achieves up to 2--3$\times$ speedup compared to state-of-the-art baselines, while maintaining comparable or better accuracy. These results not only validate the algorithm's theoretical guarantees but also demonstrate its effectiveness in practical tasks such as compressed sensing and empirical risk minimization.
toXiv_bot_toot

Wow, just noticed #ThingUmbrella reached 3700 stars on GitHub — I'm celebrating... 🤩🫠
Heartfelt thanks to all of you who've been helping along the way (in any shape & form) and been supporting this work for all these years and across different programming languages/camps! Merci beaucoup!!! Esp. big Thank You's to fellow fediverse people/supporters from various stages…

thi.ng/umbrella

Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation
Bogdan Butyrin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Qi-Man Shao, Zhuo-Song Zhang
https://arxiv.org/abs/2510.12375

Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation
In this paper, we refine the Berry-Esseen bounds for the multivariate normal approximation of Polyak-Ruppert averaged iterates arising from the linear stochastic approximation (LSA) algorithm with decreasing step size. We consider the normal approximation by the Gaussian distribution with covariance matrix predicted by the Polyak-Juditsky central limit theorem and establish the rate up to order $n^{-1/3}$ in convex distance, where $n$ is the number of samples used in the algorithm. We also prov…

Metropolitana VI - Asymmetry ✅
城 VI - 非对称 ✅
📷 Pentax MX
🎞️ Ilford Pan 100
#filmphotography #Photography #blackandwhite

Inside the aftermath of FTX's collapse, law firm Sullivan & Cromwell's role, and an interview with SBF, who is preparing to appeal his conviction in November (Mother Jones)
https://www.motherjones.com/politics/2025/10/…

Sam Bankman-Fried and the multibillion-dollar drama over FTX’s ruins
In exclusive prison interviews, the convicted crypto king is still fighting to control the narrative ahead of a high-stakes appeal.

Quantum walks through generalized graph composition
Arjan Cornelissen
https://arxiv.org/abs/2510.04973 https://arxiv.org/pdf/2510.04973

Quantum walks through generalized graph composition
In this work, we generalize the recently-introduced graph composition framework to the non-boolean setting. A quantum algorithm in this framework is represented by a hypergraph, where each hyperedge is adjacent to multiple vertices. The input and output to the quantum algorithm is represented by a set of boundary vertices, and the hyperedges act like switches, connecting the input vertex to the output that the algorithm computes. Apart from generalizing the graph composition framework, our ne…

Accelerated stochastic first-order method for convex optimization under heavy-tailed noise
Chuan He, Zhaosong Lu
https://arxiv.org/abs/2510.11676 https://a…

Accelerated stochastic first-order method for convex optimization under heavy-tailed noise
We study convex composite optimization problems, where the objective function is given by the sum of a prox-friendly function and a convex function whose subgradients are estimated under heavy-tailed noise. Existing work often employs gradient clipping or normalization techniques in stochastic first-order methods to address heavy-tailed noise. In this paper, we demonstrate that a vanilla stochastic algorithm -- without additional modifications such as clipping or normalization -- can achieve op…

Russische Angriffe auf Stromversorgung in der Ukraine
Russland hat in der vergangenen Nacht die ukrainische Energieinfrastruktur mit Drohnen angegriffen und dadurch die Stromversorgung in mehreren Gebieten unterbrochen. Betroffen sei auch die zentrale Region Dnipropetrowsk, teilt das ukrainische Energieministerium mit. Der Stromnetzbetreiber Ukrenergo erklärt, in sieben Regionen und vor allem im Osten sei die Stromversorgung …
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Ukraine-Liveblog: ++ NATO und EU arbeiten an "Drohnenschutzwall" ++
Laut NATO-Generalsekretär Rutte arbeitet das Verteidigungsbündnis gemeinsam mit der EU an einem Abwehrwall gegen Drohnen. Die Zahl junge Ukrainer, die als Schutzsuchende nach Deutschland kommen, ist im Sommer gestiegen.