Tootfinder

Sheffield Pattern Club meetup at gut level on Thursday! I'll be doing an intro to South Indian konnakol rhythms
https://gutlevel.co.uk/whatson/0x2ojm8z3khi0h62aleh6ds7176qg2-5maeh-nds8e

J'ai découvert la nouvelle section "Packages" du site officiel de #Svelte
https://notes.sklein.xyz/2025-10-11_1027/

A breakthrough in green hydrogen just happened in South Australia. Sparc Hydrogen achieved sustained hydrogen production using only sunlight and water—no electrolyzers, no electricity grid needed.
Their photocatalytic process uses concentrated solar energy to split water directly. With green hydrogen markets projected to hit $1.4 trillion annually by 2050, this could be the scalability breakthrough the industry needs.

Sparc hits production milestone at hydrogen pilot plant in Australia
Fortescue-backed startup Sparc Hydrogen has begun producing green hydrogen directly from water using concentrated solar at what it describes as a “first-of-its-kind” pilot plant in South Australia.

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

'pigz' is a fast parallel compressor - it splits the normal compressors over however many threads you want; it's really great when you have a HUGE file that needs to be less huge.
https://zlib.net/pigz/

each month, my building posts a neighborhood events calendar
inexplicably, this month it's Very Incorrect AI Slop that:
- Has droopy typos
- Has times with no event name
- Starts on the wrong day
- Doesn't have a single correct event date
- Really likes Dallas
how did they not notice ???

Train Stochastic Non Linear Coupled ODEs to Classify and Generate
Stefano Gagliani, Feliciano Giuseppe Pacifico, Lorenzo Chicchi, Duccio Fanelli, Diego Febbe, Lorenzo Buffoni, Raffaele Marino
https://arxiv.org/abs/2510.12286

Train Stochastic Non Linear Coupled ODEs to Classify and Generate
A general class of dynamical systems which can be trained to operate in classification and generation modes are introduced. A procedure is proposed to plant asymptotic stationary attractors of the deterministic model. Optimizing the dynamical system amounts to shaping the architecture of inter-nodes connection to steer the evolution towards the assigned equilibrium, as a function of the class to which the item - supplied as an initial condition - belongs to. Under the stochastic perspective, po…