@blakes7bot@mas.torpidity.net2025-12-15 19:18:48
Series D, Episode 01 - Rescue
[Interior Xenon base lounge. Soolin sets out glasses on a tray. She stops, regards the tray and then takes another glass from the shelf. She begins to pour wine into the glasses. Dorian leads the group into the lounge. Dorian grabs her and kisses her.]
SOOLIN: Are you all right?
https://blake.torpi…
@arXiv_mathOC_bot@mastoxiv.page2025-11-14 09:35:50
dHPR: A Distributed Halpern Peaceman--Rachford Method for Non-smooth Distributed Optimization Problems
Zhangcheng Feng, Defeng Sun, Yancheng Yuan, Guojun Zhang
https://arxiv.org/abs/2511.10069 https://arxiv.org/pdf/2511.10069 https://arxiv.org/html/2511.10069
arXiv:2511.10069v1 Announce Type: new
Abstract: This paper introduces the distributed Halpern Peaceman--Rachford (dHPR) method, an efficient algorithm for solving distributed convex composite optimization problems with non-smooth objectives, which achieves a non-ergodic $O(1/k)$ iteration complexity regarding Karush--Kuhn--Tucker residual. By leveraging the symmetric Gauss--Seidel decomposition, the dHPR effectively decouples the linear operators in the objective functions and consensus constraints while maintaining parallelizability and avoiding additional large proximal terms, leading to a decentralized implementation with provably fast convergence. The superior performance of dHPR is demonstrated through comprehensive numerical experiments on distributed LASSO, group LASSO, and $L_1$-regularized logistic regression problems.
toXiv_bot_toot
@inthehands@hachyderm.io2025-11-02 19:38:18
Interesting graph from @….
There was a •fascinating• 99pi about the push to create an official Category 6 — and the surprisingly compelling arguments against doing that (more fully developed in the story audio than in the web text summary):
https://99percentinvisible.org/episode/category-6/
One thing that’s a clear point of consensus in that debate is that the existing Saffir-Simpson hurricane scale isn’t up to the task of describing what we face now. https://fediscience.org/@rahmstorf/115481161250561175
@tinoeberl@mastodon.online2025-11-06 09:10:22
@cdarwin@c.im2025-12-20 00:07:04
One document, called EFTA00004179,
consists of a formal FBI evidence cover sheet and 13 pages of handwritten investigative notes from an interview
conducted on 2 May 2019.
The interview subject is redacted, as is some of the content.
Still, themes emerge;
recruitment of girls,
sexual encounters under the guise of “massages”
and Epstein’s specific preferences regarding age and race.
“Friends of [redacted] friends.
Big Brazilian group.
@Mediagazer@mstdn.social2025-12-29 21:10:29
Leaked letter: a group of "prominent journalists" at CBS News are preparing a petition urging David Ellison to uphold editorial independence at the network (New York Post)
https://nypost.com/202…
@relcfp@mastodon.social2026-01-04 06:43:16
TOC: Journal of Jewish Ethics 10.1-2 https://networks.h-net.org/group/announcements/20136596/toc-journal-jewish-ethics-101-2
@arXiv_csLG_bot@mastoxiv.page2025-12-22 10:33:20
Can You Hear Me Now? A Benchmark for Long-Range Graph Propagation
Luca Miglior, Matteo Tolloso, Alessio Gravina, Davide Bacciu
https://arxiv.org/abs/2512.17762 https://arxiv.org/pdf/2512.17762 https://arxiv.org/html/2512.17762
arXiv:2512.17762v1 Announce Type: new
Abstract: Effectively capturing long-range interactions remains a fundamental yet unresolved challenge in graph neural network (GNN) research, critical for applications across diverse fields of science. To systematically address this, we introduce ECHO (Evaluating Communication over long HOps), a novel benchmark specifically designed to rigorously assess the capabilities of GNNs in handling very long-range graph propagation. ECHO includes three synthetic graph tasks, namely single-source shortest paths, node eccentricity, and graph diameter, each constructed over diverse and structurally challenging topologies intentionally designed to introduce significant information bottlenecks. ECHO also includes two real-world datasets, ECHO-Charge and ECHO-Energy, which define chemically grounded benchmarks for predicting atomic partial charges and molecular total energies, respectively, with reference computations obtained at the density functional theory (DFT) level. Both tasks inherently depend on capturing complex long-range molecular interactions. Our extensive benchmarking of popular GNN architectures reveals clear performance gaps, emphasizing the difficulty of true long-range propagation and highlighting design choices capable of overcoming inherent limitations. ECHO thereby sets a new standard for evaluating long-range information propagation, also providing a compelling example for its need in AI for science.
toXiv_bot_toot
@blakes7bot@mas.torpidity.net2025-12-03 16:14:13
Series B, Episode 05 - Pressure Point
AVON: Frankly, I don't see how you can do it without me. Your strongest enemies are going to be the defence computers. I am the only one qualified to tackle them.
BLAKE: Yes, that had occurred to me. You better start getting kitted up. Do you want to tell me why?
https://blake.torpidit…
@blakes7bot@mas.torpidity.net2025-10-21 12:13:03
Series C, Episode 04 - Dawn of the Gods
TARRANT: What about you?
GROFF: I'll be alright, get going! But if you could tell them, my family, they were always in my thoughts.
TARRANT: We'll tell them, Groff. [Groff tries to move main switch]
https://blake.torpidity.net/m/304/363…
