Tootfinder

🇺🇦 #NowPlaying on KEXP's #SeekAndDestroy
Mercyful Fate:
🎵 Evil
#MercyfulFate
https://mercyfulfate.bandcamp.com/album/evil
https://open.spotify.com/track/5pH1BecbNktYDbYw0XCEWn

Bayesian inference for spatio-temporal hidden Markov models using the exchange algorithm
Daniele Tancini, Riccardo Rastelli, Francesco Bartolucci
https://arxiv.org/abs/2509.15164

Bayesian inference for spatio-temporal hidden Markov models using the exchange algorithm
Spatio-temporal hidden Markov models are extremely difficult to estimate because their latent joint distributions are available only in trivial cases. In the estimation phase, these latent distributions are usually substituted with pseudo-distributions, which could affect the estimation results, in particular in the presence of strong dependence between the latent variables. In this work, we propose a spatio-temporal hidden Markov model where the latent process is an extension of the autologist…

The US FTC sues Key Investment Group, operator of sites like TotalTickets.com, for skirting ticket limits to buy up and resell live event tickets at a markup (Jody Godoy/Reuters)
https://www.reuters.com/sustainability/boa…

heise | Wie Sie gratis KI-Stimmen klonen – für mehrsprachige Podcasts und Hörbücher
Mit dem KI-Tool Chatterbox werden synthetische Stimmen lebendig. Wir zeigen, wie Sie gratis und lokal mehrsprachige Podcasts oder Hörbücher generieren.

Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
Shengbo Wang, Nian Si
https://arxiv.org/abs/2509.14203 https://a…

Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
Learning and optimal control under robust Markov decision processes (MDPs) have received increasing attention, yet most existing theory, algorithms, and applications focus on finite-horizon or discounted models. The average-reward formulation, while natural in many operations research and management contexts, remains underexplored. This is primarily because the dynamic programming foundations are technically challenging and only partially understood, with several fundamental questions remaining…

A pedestrian's approach to large deviations in semi-Markov processes with an application to entropy production
Alexander M. Maier, Jonas H. Fritz, Udo Seifert
https://arxiv.org/abs/2509.15077

A pedestrian's approach to large deviations in semi-Markov processes with an application to entropy production
Semi-Markov processes play an important role in the effective description of partially accessible systems in stochastic thermodynamics. They occur, for instance, in coarse-graining procedures such as state lumping and when analyzing waiting times between few visible Markovian events. The finite-time measurement of any coarse-grained observable in a stochastic system depends on the specific realization of the underlying trajectory. Moreover, the fluctuations of such observables are encoded in th…

MAJIC: Markovian Adaptive Jailbreaking via Iterative Composition of Diverse Innovative Strategies
Weiwei Qi, Shuo Shao, Wei Gu, Tianhang Zheng, Puning Zhao, Zhan Qin, Kui Ren
https://arxiv.org/abs/2508.13048

MAJIC: Markovian Adaptive Jailbreaking via Iterative Composition of Diverse Innovative Strategies
Large Language Models (LLMs) have exhibited remarkable capabilities but remain vulnerable to jailbreaking attacks, which can elicit harmful content from the models by manipulating the input prompts. Existing black-box jailbreaking techniques primarily rely on static prompts crafted with a single, non-adaptive strategy, or employ rigid combinations of several underperforming attack methods, which limits their adaptability and generalization. To address these limitations, we propose MAJIC, a Mark…

🇺🇦 Auf radioeins läuft...
Morcheeba:
🎵 The Sea
#NowPlaying #Morcheeba
https://duodomusica.bandcamp.com/track/the-sea-morcheeba-cover
https://open.spotify.com/track/0xjkgYSzHjBZNvyUaC9cXX

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Mercyful Fate:
🎵 Evil
#MercyfulFate
https://mercyfulfate.bandcamp.com/album/evil
https://open.spotify.com/track/5pH1BecbNktYDbYw0XCEWn

Environmental Risk Assessment via Nonhomogeneous Hidden Semi-Markov Models with Penalized Vector Auto-Regression
Marco Mingione, Pierfrancesco Alaimo Di Loro, Francesco Lagona, Antonello Maruotti
https://arxiv.org/abs/2509.14387

Environmental Risk Assessment via Nonhomogeneous Hidden Semi-Markov Models with Penalized Vector Auto-Regression
Motivated by the study of pollution trends in the city of Bergen, we introduce a flexible statistical framework for modeling multivariate air pollution data via a nonhomogeneous Hidden Semi-Markov Vector Auto-Regression. The hidden process captures unobserved environmental conditions, while the vector autoregressive structure accounts for temporal autocorrelation and cross-pollutant dependencies. The model further allows time-varying environmental conditions to influence both the average levels…