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🇺🇦 #NowPlaying on KEXP's #VarietyMix
Eaves Wilder:
🎵 Everybody Talks
#EavesWilder
https://eaveswilder.bandcamp.com/track/everybody-talks
https://open.spotify.com/track/7mqTdRDE6VeMJBKAiHyef2

Notarzt im Einsatz - Böller-Experiment: Explosion in Einfamilienhaus #News #Nachrichten

Characterizing Agent-Based Model Dynamics via $\epsilon$-Machines and Kolmogorov-Style Complexity
Roberto Garrone (University of Milano-Bicocca)
https://arxiv.org/abs/2510.12729

Characterizing Agent-Based Model Dynamics via $ε$-Machines and Kolmogorov-Style Complexity
We propose a two-level information-theoretic framework for characterizing the informational organization of Agent-Based Model (ABM) dynamics within the broader paradigm of Complex Adaptive Systems (CAS). At the macro level, a pooled $ε$-machine is reconstructed as a reference model that summarizes the system-wide informational regime. At the micro level, $ε$-machines are reconstructed for each caregiver-elder dyad and variable, and are complemented with algorithm-agnostic Kolmogorov-style mea…

On the Complexity of Stationary Nash Equilibria in Discounted Perfect Information Stochastic Games
Kristoffer Arnsfelt Hansen, Xinhao Nie
https://arxiv.org/abs/2510.11550 https:…

On the Complexity of Stationary Nash Equilibria in Discounted Perfect Information Stochastic Games
We study the problem of computing stationary Nash equilibria in discounted perfect information stochastic games from the viewpoint of computational complexity. For two-player games we prove the problem to be in PPAD, which together with a previous PPAD-hardness result precisely classifies the problem as PPAD-complete. In addition to this we give an improved and simpler PPAD-hardness proof for computing a stationary epsilon-Nash equilibrium. For 3-player games we construct games showing that rat…

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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Prioritizing Latency with Profit: A DRL-Based Admission Control for 5G Network Slices
Proggya Chakraborty, Aaquib Asrar, Jayasree Sengupta, Sipra Das Bit
https://arxiv.org/abs/2510.08769

Prioritizing Latency with Profit: A DRL-Based Admission Control for 5G Network Slices
5G networks enable diverse services such as eMBB, URLLC, and mMTC through network slicing, necessitating intelligent admission control and resource allocation to meet stringent QoS requirements while maximizing Network Service Provider (NSP) profits. However, existing Deep Reinforcement Learning (DRL) frameworks focus primarily on profit optimization without explicitly accounting for service delay, potentially leading to QoS violations for latency-sensitive slices. Moreover, commonly used epsil…

Hausdorff dimension of the singular set for Griffith almost-minimizers in the plane
Manuel Friedrich, Camille Labourie, Kerrek Stinson
https://arxiv.org/abs/2510.08670 https://

Hausdorff dimension of the singular set for Griffith almost-minimizers in the plane
We consider regularity of the crack set associated to a minimizer of the Griffith fracture energy, often used in modeling brittle materials. We show that the crack is uniformly rectifiable which in conjunction with our previous epsilon-regularity result allows us to prove that the singular set has dimension strictly less than $1$. This size estimate also applies to almost-minimizers. As a byproduct, we prove higher integrability for the gradient of local minimizers of the Griffith energy, provi…

🇺🇦 #NowPlaying on KEXP's #DriveTime
Eaves Wilder:
🎵 Everybody Talks
#EavesWilder
#newRelease 🆕 single
https://eaveswilder.bandcamp.com/track/everybody-talks
https://open.spotify.com/track/7mqTdRDE6VeMJBKAiHyef2

No paywall. One of the folks I read when I can to stay sane. Always on the cutting edge of The Moment.
Welcome to The Continental. We Do Hope You Enjoy Your Stay. - Epsilon Theory https://new.epsilontheory.com/a/welcome-to-the-continental-we-do-hope…

Welcome to The Continental. We Do Hope You Enjoy Your Stay.
Our autonomy of mind is our birthright. It is not ladled out to us from some central pot, and it cannot be taken away from us. But we can give it away, sometimes in pieces and sometimes all at once, and that’s what the semantic system of world-building and script-injection encourages us to do. Enough. Today we begin to reclaim our minds.

🇺🇦 #NowPlaying on #BBC6Music's #NewMusicFix
Tyler, The Creator:
🎵 Don't Tap That Glass
#Tyler #TheCreator
https://mrfantastik2.bandcamp.com/track/dont-tap-that-glass-tweakin
https://open.spotify.com/track/5DRS7YEe1bwGJLDGviT3CD