Abstract
As already noticed in other telescopes, the presence of large telescope spiders and of a segmented deformable mirror in an Adaptive Optics system leads to pupil fragmentation and may create phase discontinuities. On the ELT telescope, a typical effect is the differential piston, where all disconnected areas of the pupil create their own piston, unseen locally but drastically degrading the final image quality. The poor sensitivity of the Pyramid WFS to differential piston will lead to these modes been badly seen and therefore badly controlled by the adaptive optics (AO) loop. In close loop operation, differential pistons between segments will start to appear and settle around integer values of the average sensing wavelength. These additional differential pistons are artificially injected by the adaptive optics control loop but do not have any real physical origin, contrary to the Low Wind Effect. In an attempt to reduce the impact of unwanted differential pistons that are injected by the AO loop, we propose a novel approach based on the pair-wise coupling of the actuators sitting on the edges of the deformable mirror segments. In this paper, we present the correction principle, its performance in nominal seeing condition, and its robustness relative to changing seeing conditions, wind speed and natural guide star magnitude. We show that the edge actuator coupling is a simple and robust solution and that the additional quadratic error relative to the reference case (i.e. no spiders) is of only 40 nm RMS, well within the requirements for HARMONI.

Abstract
The optimized operation of a building energy management system (BEMS) is of great significance to its operation security, economy, and efficiency. This paper proposes a day-ahead multiobjective optimization model for the BEMS under time-of-use price-based demand response (DR), which integrates building integrated photovoltaic with other generations to optimize the economy and occupants' comfort by the synergetic dispatch of source-load-storage. The occupants' comfort contains three aspects of the indoor environment: visual comfort; thermal comfort; and indoor air quality comfort. With the consideration of controllable load that could participate in DR programs, the balances among different energy styles, electric, thermal, and cooling loads are guaranteed during the optimized operation. YALMIP toolbox in MATLAB was applied to solve the optimization problem. Finally, a case study was conducted to validate the effectiveness and adaptability of the proposed model.

Abstract
A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.

Abstract
The SPHERE instrument, dedicated to high contrast imaging on VLT, has been routinely operated for more than 3 years, over a large range of conditions and producing observations from visible to NIR. A central part of the instrument is the high order adaptive optics system, named SAXO, designed to deliver high Strehl image quality with a balanced performance budget for bright stars up to magnitude R=9. We take benefit now from the very large set of observations to revisit the assumptions and analysis made at the time of the design phase: We compare the actual AO behavior as a function of expectations. The data set consists of the science detector data, for both coronagraphic images and non-coronagraphic PSF calibrations, but also of AO internal data from the high frequency sensors and statistics computations from the real-time computer which are systematically archived, and finally of environmental data, monitored at VLT level. This work is supported and made possible by the SPHERE Data Center infrastructure hosted at Grenoble which provides an efficient access and the capability for the homogeneous analysis of this large and statistically-relevant data set. We review in a statistical manner the actual AO performance as a function of external conditions for different regimes and we discuss the possible performance metrics, either derived from AO internal data or directly from the high contrast images. We quantify the dependency of the actual performance on the most relevant environmental parameters. By comparison to earlier expectations, we conclude on the reliability of the usual AO modeling. We propose some practical criteria to optimize the queue scheduling and the expression of observer requirements; finally, we revisit what could be the most important AO specifications for future high contrast imagers as a function of the primary science goals, the targets and the turbulence properties.

Abstract
Optimization problems that are motivated by real-world settings are often complex to solve. Bridging the gap between theory and practice in this field starts by understanding the causes of complexity of each problem and measuring its impact in order to make better decisions on approaches and methods. The Job-Shop Scheduling Problem (JSSP) is a well-known complex combinatorial problem with several industrial applications. This problem is used to analyse what makes some instances difficult to solve for a commonly used solution approach - Mathematical Integer Programming (MIP) - and to compare the power of an alternative approach: Constraint Programming (CP). The causes of complexity are analysed and compared for both approaches and a measure of MIP complexity is proposed, based on the concept of load per machine. Also, the impact of problem-specific global constraints in CP modelling is analysed, making proof of the industrial practical interest of commercially available CP models for the JSSP.

Abstract
Large corpus of scientific research papers have been available for a long time. However, most of those corpus store only the title and the abstract of the paper. For some domains this information may not be enough to achieve high performance in text mining tasks. This problem has been recently reduced by the growing availability of full text scientific research papers. A full text version provides more detailed information but, on the other hand, a large amount of data needs to be processed. A priori, it is difficult to know if the extra work of the full text analysis has a significant impact in the performance of text mining tasks, or if the effect depends on the scientific domain or the specific corpus under analysis. The goal of this paper is to show a framework for full text analysis, called LearnSec, which incorporates domain specific knowledge and information about the content of the document sections to improve the classification process with propositional and relational learning. To demonstrate the usefulness of the tool, we process a scientific corpus based on OSHUMED, generating an attribute/value dataset in Weka format and a First Order Logic dataset in Inductive Logic Programming (ILP) format. Results show a successful assessment of the framework.