On the notion of phase in mechanics

The notion of phase plays an esential role in both classical and quantum mechanics.But what is a phase? We show that if we define the notion of phase in phase (!) space one can very easily and naturally recover the Heisenberg-Weyl formalism; this is achieved using the properties of the Poincare-Cartan invariant, and without making any quantum assumption

An end-to-end data-driven optimization framework for constrained trajectories

Abstract Many real-world problems require to optimize trajectories under constraints. Classical approaches are often based on optimal control methods but require an exact knowledge of the underlying dynamics and constraints, which could be challenging or even out of reach. In view of this, we leverage data-driven approaches to design a new end-to-end framework which is dynamics-free for optimized and realistic trajectories. Trajectories are here decomposed on function basis, trading the initial infinite dimension problem on a multivariate functional space for a parameter optimization problem. Then a maximum a posteriori approach which incorporates information from data is used to obtain a new penalized optimization problem. The penalized term narrows the search on a region centered on data and includes estimated features of the problem. We apply our data-driven approach to two settings in aeronautics and sailing routes optimization. The developed approach is implemented in the Python library PyRotor

Antimicrobial Stewardship from Policy to Practice: Experiences from UK Antimicrobial Pharmacists

Antimicrobial stewardship in the UK has evolved dramatically in the last 15 years. Factors driving this include initial central funding for specialist pharmacists and mandatory reductions in healthcare-associated infections (particularly Clostridium difficile infection). More recently, the introduction of national stewardship guidelines, and an increased focus on stewardship as part of the UK five-year antimicrobial resistance strategy, have accelerated and embedded developments. Antimicrobial pharmacists have been instrumental in effecting changes at an organizational and national level. This article describes the evolution of the antimicrobial pharmacist role, its impact, the progress toward the actions listed in the five-year resistance strategy, and novel emerging areas in stewardship in the UK

An end-to-end data-driven optimisation framework for constrained trajectories

28 pagesMany real-world problems require to optimise trajectories under constraints. Classical approaches are based on optimal control methods but require an exact knowledge of the underlying dynamics, which could be challenging or even out of reach. In this paper, we leverage data-driven approaches to design a new end-to-end framework which is dynamics-free for optimised and realistic trajectories. We first decompose the trajectories on function basis, trading the initial infinite dimension problem on a multivariate functional space for a parameter optimisation problem. A maximum \emph{a posteriori} approach which incorporates information from data is used to obtain a new optimisation problem which is regularised. The penalised term focuses the search on a region centered on data and includes estimated linear constraints in the problem. We apply our data-driven approach to two settings in aeronautics and sailing routes optimisation, yielding commanding results. The developed approach has been implemented in the Python library PyRotor

Relationship between DNA acid-solubility and frequency of single-strand breaks near apurinic sites.

peer reviewedUsing [32P]DNA alkylated with [3H]methyl methanesulfonate, depurinated by heating at 50 degrees C for various periods, then treated with sodium hydroxide, a table was constructed giving the DNA fraction soluble in 5% perchloric acid at 0 degree C as a function of the frequency of strand breaks. The alkaline treatment placed a break near each apurinic site; the apurinic sites were counted in two ways which gave consonant results: by the loss of [3H]methyl groups and by reaction with [14C]methoxyamine. The 32P label of DNA was used to measure the acid-solubility

Exploring applications of deep reinforcement learning for real-world autonomous driving systems

Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind’s AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye’s path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthetic car simulator environments such as TORCS and CARLA. In general, DRL is still at its infancy in terms of usability in real-world applications. Our goal in this paper is to encourage real-world deployment of DRL in various autonomous driving (AD) applications. We first provide an overview of the tasks in autonomous driving systems, reinforcement learning algorithms and applications of DRL to AD systems. We then discuss the challenges which must be addressed to enable further progress towards real-world deployment.ye