Tootfinder

scheise was ist pasirt
https://github.com/AasishPokhrel/repository/issues/1

Robust output stability and input-to-output stability based on output dissipation
Antoine Chaillet (IUF, L2S), Iasson Karafyllis (NTUA), Yuan Wang
https://arxiv.org/abs/2506.10479

Robust output stability and input-to-output stability based on output dissipation
LaSalle techniques to ensure the convergence of a given output usually fail at guaranteeing uniform convergence time, which induces robustness issues. Recent works have provided extra conditions under which a Lyapunov function that dissipates in terms of only the output guarantees this uniformity. In this paper, we extend these results to systems with inputs in order to establish either robust stability or input-to-output stability. In addition, we show that a recent relaxation of Barb__lat's l…

Mr. Seifert the science teacher addresses the class re: the rumors about him & Ms. Barker the 5th grade teacher.
▶️ Miss Barker Was NOT Ready for This... #shorts #repost #school

Robust copula estimation for one-shot devices with correlated failure modes
E. Castilla, P. J. Chocano
https://arxiv.org/abs/2506.10152 https://

Robust copula estimation for one-shot devices with correlated failure modes
This paper presents a robust method for estimating copula models to evaluate dependence between failure modes in one-shot devices-systems designed for single use and destroyed upon activation. Traditional approaches, such as maximum likelihood estimation (MLE), often produce unreliable results when faced with outliers or model misspecification. To overcome these limitations, we introduce a divergence-based estimation technique that enhances robustness and provides a more reliable characterizati…

Open repos & AI bots
#FederatedMemory
https://coar-repositories.org/news-updates/open-repositories-are-being-profoundly-impacted-by-ai-bots-and-other-crawlers-results-of-a-coar-survey/
and https://codeberg.org/forgejo/discussions/issues/339

EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair
Fangwen Mu, Junjie Wang, Lin Shi, Song Wang, Shoubin Li, Qing Wang
https://arxiv.org/abs/2506.10484

EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair
Automatically repairing software issues remains a fundamental challenge at the intersection of software engineering and AI. Although recent advancements in Large Language Models (LLMs) have demonstrated potential for repository-level repair tasks, current methodologies exhibit two notable limitations: (1) they often address issues in isolation, neglecting to incorporate insights from previously resolved issues, and (2) they rely on static and rigid prompting strategies, which constrain their ab…

New preprint!
User-space library rootkits revisited: Are user-space detection mechanisms futile?
https://arxiv.org/abs/2506.07827

Hooray, parallel downloading of repository metadata was merged for #dnf! 🥳
https://github.com/rpm-software-management/dnf5/issues/307#issuecomment-2…

A Robust Optimization Framework for Flexible Industrial Energy Scheduling: Application to a Cement Plant with Market Participation
Sebasti\'an Rojas-Innocenti, Enrique Baeyens, Alejandro Mart\'in-Crespo, Sergio Saludes-Rodil, Fernando Frechoso Escudero
https://arxiv.org/abs/2506.10824

A Robust Optimization Framework for Flexible Industrial Energy Scheduling: Application to a Cement Plant with Market Participation
This paper presents a scenario based robust optimization framework for short term energy scheduling in electricity intensive industrial plants, explicitly addressing uncertainty in planning decisions. The model is formulated as a two-stage Mixed Integer Linear Program (MILP) and integrates a hybrid scenario generation method capable of representing uncertain inputs such as electricity prices, renewable generation, and internal demand. A convex objective function combining expected and worst cas…

Limiting the Shrinkage for the Exceptional by Objective Robust Bayesian Analysis: the `Clemente Problem'
Luis R. Pericchi, Maria-Eglee Perez
https://arxiv.org/abs/2506.10114

Limiting the Shrinkage for the Exceptional by Objective Robust Bayesian Analysis: the `Clemente Problem'
In `borrowing strength' an important problem of Statistics is to treat exceptional cases in a fundamentally different. This is what has been coined as `the Clemente problem' in honor of R. Clemente (Efron 2010). In this article, we propose to use robust penalties, in the form of losses that penalize more severely huge errors, or (equivalently) priors of heavy tails which make more probable the exceptional. Using heavy tailed priors, we can reproduce in a Bayesian way Efron and Morris `limited t…