Abstract
The estimation of atmospheric turbulence parameters is of relevance for the following: (a) site evaluation and characterization; (b) prediction of the point spread function; (c) live assessment of error budgets and optimization of adaptive optics performance; (d) optimization of fringe trackers for long baseline optical interferometry. The ubiquitous deployment of Shack-Hartmann wavefront sensors in large telescopes makes them central for atmospheric turbulence parameter estimation via adaptive optics telemetry. Several methods for the estimation of the Fried parameter and outer scale have been developed, most of which are based on the fitting of Zernike polynomial coefficient variances reconstructed from the telemetry. The non-orthogonality of Zernike polynomial derivatives introduces modal cross coupling, which affects the variances. Furthermore, the finite resolution of the sensor introduces aliasing. In this article the impact of these effects on atmospheric turbulence parameter estimation is addressed with simulations. It is found that cross-coupling is the dominant bias. An iterative algorithm to overcome it is presented. Simulations are conducted for typical ranges of the outer scale (4-32 m), Fried parameter (10 cm) and noise in the variances (signal-to-noise ratio of 10 and above). It is found that, using the algorithm, both parameters are recovered with sub-per cent accuracy.

Abstract
The performance of biometric systems is known to decay over time, eventually rendering them ineffective. Focused on ECG-based biometrics, this work aims to study the permanence of these signals for biometric identification in state-of-the-art methods, and measure the effect of template update on their long-term performance. Ensuring realistic testing settings, four literature methods based on autocorrelation, autoencoders, and discrete wavelet and cosine transforms, were evaluated with and without template update, using Holter signals from THEW’s E-HOL 24 h database. The results reveal ECG signals are unreliable for long-term biometric applications, and template update techniques offer considerable improvements over the state-of-the-art results. Nevertheless, further efforts are required to ensure long-term effectiveness in real applications. © 2019, Springer Nature Switzerland AG.

Abstract

Abstract
This paper provides a thorough second-best welfare analysis of the standard two-stage model of R&D/product market competition with R&D spillovers. The planner's solution is compared to the standard non-cooperative scenario, the R&D cartel, and the cartelized research joint venture (or joint lab). We introduce the notion of a social joint lab, as a way for the planner to avoid wasteful R&D duplication. With no spillovers, the non-cooperative scenario, the joint lab, and the second-best planner's solutions coincide. However, with spillovers, all three scenarios yield R&D investments that fall short of the socially optimal level. To shed light on the role of the spillover level on these comparisons, we observe that the gaps between the market outcomes and the planners solutions widen as the spillover parameter increases. Finally, we establish that a social planner and a social joint lab solutions may be achieved starting from any of the three scenarios by offering firms respective suitably weighted quadratic R&D subsidization schedules.

Abstract
This work demonstrates a system that provides decision support to players in electricity market negotiations. This contribution is provided by ALBidS (Adaptive Learning strategic Bidding System), a decision support system that includes a large number of distinct market negotiation strategies, and learns which should be used in each context in order to provide the best expected response. The learning process on the best negotiation strategies to use at each moment is developed by means of several integrated reinforcement learning algorithms. ALBidS is integrated with MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), which enables the simulation of realistic market scenarios using real data.

Abstract
Laser Guide Star [LGS] wave-front sensing is a key element of the Laser Tomographic AO system and mainly drives the final performance of any ground based high resolution instrument. In that framework, HARMONI the first light spectro-imager of the ELT [1,2], will use 6 Laser focused around 90km(@Zenith) with a circular geometry in order to sense, reconstruct and correct for the turbulence volume located above the telescope. LGS wave-front sensing suffers from several well-known limitations [3] which are exacerbated by the giant size of the Extremely Large Telescopes. In that context, the presentation is threefold: (1) we will describe, quantify and analyse the various effects (bias and noise) induced by the LGS WFS in the context of ELT. Among other points, we will focus on the spurious low order signal generated by the spatially and temporally variable sodium layer. (2) we will propose a global design trade-off for the LGS WFS and Tomographic reconstruction process in the HARMONI context. We will show that, under strong technical constraints (especially concerning the detectors characteristics), a mix of opto-mechanic and numerical optimisations will allow to get rid of WFS bias induce by spot elongation without degrading the ultimate system performance (3) beyond HARMONI baseline, we will briefly present alternative strategies (from components, concepts and algorithms point of view) that could solve the LGS spot elongation issues at lower costs and better robustness.