Tootfinder

🇺🇦 #NowPlaying on KEXP's #SonicReducer
Split System:
🎵 On The Edge
#SplitSystem
https://splitsystem.bandcamp.com/album/on-the-edge-on-the-loose
https://open.spotify.com/track/5UipxubiGX8H0y2Idb744r

Uncertainty Matters in Dynamic Gaussian Splatting for Monocular 4D Reconstruction
Fengzhi Guo, Chih-Chuan Hsu, Sihao Ding, Cheng Zhang
https://arxiv.org/abs/2510.12768 https://

Uncertainty Matters in Dynamic Gaussian Splatting for Monocular 4D Reconstruction
Reconstructing dynamic 3D scenes from monocular input is fundamentally under-constrained, with ambiguities arising from occlusion and extreme novel views. While dynamic Gaussian Splatting offers an efficient representation, vanilla models optimize all Gaussian primitives uniformly, ignoring whether they are well or poorly observed. This limitation leads to motion drifts under occlusion and degraded synthesis when extrapolating to unseen views. We argue that uncertainty matters: Gaussians with r…

PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture
Yi Liu, Yang Liu, Leqian Zheng, Jue Hong, Junjie Shi, Qingyou Yang, Ye Wu, Cong Wang
https://arxiv.org/abs/2510.12494

PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture
With the rapid advancement of the digital economy, data collaboration between organizations has become a well-established business model, driving the growth of various industries. However, privacy concerns make direct data sharing impractical. To address this, Two-Party Split Learning (a.k.a. Vertical Federated Learning (VFL)) has emerged as a promising solution for secure collaborative learning. Despite its advantages, this architecture still suffers from low computational resource utilization…

Monday jam: Medeski Scofield Martin & Wood | Little Walter Rides Again | #jazz

Little Walter Rides Again by Medeski Scofield Martin & Wood, Medeski, Martin & Wood
Listen now on your favorite streaming service. Powered by Songlink/Odesli, an on-demand, customizable smart link service to help you share songs, albums, podcasts and more.

🇺🇦 Auf radioeins läuft...
Sepalot:
🎵 Never Give Up
#NowPlaying #Sepalot
https://sepalot.bandcamp.com/track/never-give-up
https://open.spotify.com/track/6WQKv8HGRnIdzdgPvd5CWF

'Tennet speelt stroom vrij in Zeeland, tientallen bedrijven uit de problemen'
https://nos.nl/artikel/2590062-tennet-speelt-stroom-vrij-in-zeeland-tientallen-bedrijven-uit-de-problemen
Goed dat hier wél gelukt …

Tennet speelt stroom vrij in Zeeland, tientallen bedrijven uit de problemen
Grootverbruiker Air Liquide gaat zijn energieverbruik spreiden, waardoor extra stroom beschikbaar komt. 

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.
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Google: KI-Agent Sima 2 trainiert in Videospielen für die echte Welt
Google hat eine neue Version seines spielenden KI-Agenten Sima vorgestellt, die komplexere Aktionen beherrscht. Sima trainiert in Spielen für die echte Welt.

🇺🇦 Auf radioeins läuft...
Sepalot:
🎵 My Own Way
#NowPlaying #Sepalot
https://sepalot.bandcamp.com/track/my-own-way
https://open.spotify.com/track/1fZWCdpLnT98Lf5DeGkMSB

heise | Supermodel: Die schönsten Spielautomaten-Klassiker der 90er im Emulator spielen
Traumhafte Grafik, sofortiger Spaß: So emulieren Sie Model-3-Klassiker wie Daytona USA 2 oder Virtua Fighter 3 heute noch ansehnlich.