Titlebook: Lyapunov Exponents of LinearCocycles; Continuity via Large Pedro Duarte,Silvius Klein Book 2016 Atlantis Press and the author(s) 2016 Berno

贞洁

2213-3526 ablished for iterates of general quasi-periodic and random cThe aim of this monograph is to present a general method of proving continuity of Lyapunov exponents of linear cocycles. The method uses an inductive procedure based on a general, geometric version of the Avalanche Principle. The main assum

直觉没有

Introduction,e provide a summary of the results in this monograph and review the relevant literature. As a preview of the methods used in this monograph, we present a sketch of a new proof of the multiplicative ergodic theorem.

过份好问

The Oseledets Filtration and Decomposition, of appropriate uniform large deviation type estimates. The same assumptions lead in the previous chapter to an abstract continuity theorem of the Lyapunov exponents. This result and other technical estimates derived there, along with the inductive scheme based on the Avalanche Principle are the main ingredients of the arguments in this chapter.

Notify

CAMEO

acrophobia

间接

Pedro Duarte,Silvius Klein to make normative statements (e.g. regarding the prohibition on indiscriminate weapons). How users of AWS are expected to interpret and use such MoPs, however, is rarely addressed. This is problematic as many indicators, such as accuracy, are measured very differently for AI systems compared to con

新手

Pedro Duarte,Silvius Kleindamage, warnings, and delaying/cancelling) and how their application would be impacted by the unique properties of autonomous weapon systems (AWS). The chapter offers a dual approach: first, an analysis of how AWS-users should apply these obligations under a more strict interpretation of the law, an

fertilizer

Pedro Duarte,Silvius Klein understand why systems fail. To this end, this chapter presents a typology of different reasons that may lead autonomous weapon systems (AWS) to fail in the field: being cognisant of these factors enables AWS-users to predict whether there is increased risk of their systems failing after deployment

Geyser

Pedro Duarte,Silvius Klein understand why systems fail. To this end, this chapter presents a typology of different reasons that may lead autonomous weapon systems (AWS) to fail in the field: being cognisant of these factors enables AWS-users to predict whether there is increased risk of their systems failing after deployment