Basic reinforcement learning techniques to control the intensity of a seeded free-electron laser

Optimal tuning of particle accelerators is a challenging task. Many different approaches have been proposed in the past to solve two main problems\u2014attainment of an optimal working point and performance recovery after machine drifts. The most classical model-free techniques (e.g., Gradient Ascent or Extremum Seeking algorithms) have some intrinsic limitations. To overcome those limitations, Machine Learning tools, in particular Reinforcement Learning (RL), are attracting more and more attention in the particle accelerator community. We investigate the feasibility of RL model-free approaches to align the seed laser, as well as other service lasers, at FERMI, the free-electron laser facility at Elettra Sincrotrone Trieste. We apply two different techniques\u2014the first, based on the episodic Q-learning with linear function approximation, for performance optimization; the second, based on the continuous Natural Policy Gradient REINFORCE algorithm, for performance recovery. Despite the simplicity of these approaches, we report satisfactory preliminary results, that represent the first step toward a new fully automatic procedure for the alignment of the seed laser to the electron beam. Such an alignment is, at present, performed manually

Toward the Application of Reinforcement Learning to the Intensity Control of a Seeded Free-Electron Laser

The optimization of particle accelerators is a challenging task, and many different approaches have been proposed in years, to obtain an optimal tuning of the plant and to keep it optimally tuned despite drifts or disturbances. Indeed, the classical model-free approaches (such as Gradient Ascent or Extremum Seeking algorithms) have intrinsic limitations. To overcome those limitations, Machine Learning techniques, in particular, the Reinforcement Learning, are attracting more and more attention in the particle accelerator community. The purpose of this paper is to apply a Reinforcement Learning model-free approach to the alignment of a seed laser, based on a rather general target function depending on the laser trajectory. The study focuses on the alignment of the lasers at FERMI, the free-electron laser facility at Elettra Sincrotrone Trieste. In particular, we employ Q-learning with linear function approximation and report experimental results obtained in two setups, which are the actual setups where the final application has to be deployed. Despite the simplicity of the approach, we report satisfactory preliminary results, that represent the first step toward a fully automatic procedure for seed laser to the electron beam. Such a superimposition is, at present, performed manually

Economy of Ragusa, 1300-1800: the tiger of medieval mediterranean

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Emergence of a Stage-Dependent Human Liver Disease Signature with Directed Differentiation of Alpha-1 Antitrypsin-Deficient iPS Cells

Induced pluripotent stem cells (iPSCs) provide an inexhaustible source of cells for modeling disease and testing drugs. Here we develop a bioinformatic approach to detect differences between the genomic programs of iPSCs derived from diseased versus normal human cohorts as they emerge during in vitro directed differentiation. Using iPSCs generated from a cohort carrying mutations (PiZZ) in the gene responsible for alpha-1 antitrypsin (AAT) deficiency, we find that the global transcriptomes of PiZZ iPSCs diverge from normal controls upon differentiation to hepatic cells. Expression of 135 genes distinguishes PiZZ iPSC-hepatic cells, providing potential clues to liver disease pathogenesis. The disease-specific cells display intracellular accumulation of mutant AAT protein, resulting in increased autophagic flux. Furthermore, we detect beneficial responses to the drug carbamazepine, which further augments autophagic flux, but adverse responses to known hepatotoxic drugs. Our findings support the utility of iPSCs as tools for drug development or prediction of toxicity

Optical-EUV Pump and Probe Experiments With Variable Polarization on the Newly Open LDM Beamline of FERMI@Elettra

Two color experiments are now available to users at the low-density matter beamline (LDM) operating at the Free Electron Laser (FEL) source FERMI@Elettra [1]. The seeded FEL method used at FERMI allows generation of high power, coherent pulses in the femtosecond regime, with a high level of shot-to-shot stability. Variable polarization is also available. LDM is dedicated to atomic, molecular and cluster physics. The LDM endstation,equipped with a velocity map imaging and a time-of-flight detector [2], is an ideal tool to characterize fast multiphoton processes. LDM was open to users in December 2012 and in February 2013 performed its firstpump and probe experiment on photoionization of atomic He and generation of spectral sidebands. The FERMI FEL-1 source, delivered EUV photons with several tens of microjoule per pulse (about 100 fs wide) in atunable wavelength range from 65 to 20 nm, while the 780 nm, optical pulses were from the same Ti:sapphire laser used to form the FEL seed pulse. This paper gives details about the pump and probe experimental setupand shows the straightforward use of the pump and probe data to measure the FEL pulse width