In this chapter we discuss the effects of losses on quantum optical systems. We discuss quantum jumps and master equations. We introduce the notion of using fictitious beam splitters to model losses. We introduce the decoherence of pure quantum mechanical states into a statistical mixture.

The goal of this chapter is to present complete examples of the design and implementation of machine learning methods in large-scale data analytics. In particular, we choose three distinct topics: semi-supervised learning, ensemble learning, and how to deploy deep learning models at scale. Each of them is introduced, motivating why parallelization to deal with big data is needed, determining the main bottlenecks, designing and coding Spark-based solutions, and discussing further work required to improve the code. In semi-supervised learning, we focus on the simplest self-labeling approach called self-training, and a global solution for it. Likewise, in ensemble learning, we design a global approach for bagging and boosting. Lastly, we show an example with deep learning. Rather than parallelizing the training of a model, which is typically easier on GPUs, we deploy the inference step for a case study in semantic image segmentation.

The story of how medieval polyphony and song have been approached in more recent times is the subject of our final chapter. We look at how the appearance of modern editions of medieval poetry allowed composers in the first half of the twentieth century, such as Carl Orff and Benjamin Britten, to set medieval song-texts to new music. It would take until after World War II, however, for the music of medieval polyphony and song to reach wide audiences, and we chart the history of the recordings, concerts, and festivals that have brought this music to listeners from 1950 onwards. We examine the approaches of the pioneering ensembles and directors who first performed and recorded this music in the 1950s and 1960s, looking at their influences and attitudes towards the many unknowns regarding its original performance practice. Situating the re-awakening of medieval music in the wider context of the early music revival and the ‘authentic’ (later, ‘historically-informed’) performance movements, we then move on to consider ‘medievalist’ music, especially in film, television, and video games with medieval settings.

Dynamical downscaling uses high-resolution regional climate models (RCMs) to bias-correct and downscale global climate model output.This chapter discusses the models and methods used in dynamical downscaling. It provides an overview of the basic physics used in RCMs, and how this is similar to and differs from that used in global models. It also discusses the methods and metrics used to evaluate RCMs, and how projections from RCMs can be used to assess climate impacts at the regional scale

What lies in the future of regional downscaling, and how will future developments advance the information available for impacts and policymaking analyses? Models and methods are constantly under development; what might users expect to become available over the next decades as computing resources reach and extend beyond exascale (over 100 times faster than the current fastest computers), and global climate model resolutions reach regional scales? What uncertainties will remain to be resolved, even with these advances? By its nature, much of this discussion is somewhat philosophical and this chapter does not include real-world case studies. Instead, it highlights specific ways assessments may be affected by future advances and discusses key areas of future development.

Do you remember the first time you heard the music of Igor Stravinsky? Or modernist music? My own teenage introduction to both was Ragtime (1917–18), our music teacher helping us join the dots between its particular strand of twentieth-century classical music and Scott Joplin’s evergreen rag, ‘The Entertainer’ (1902), which the pianists among us would struggle to play.1 Looking back, the muffled giggling which Ragtime provoked was due as much to the jolting introduction of its faint and weird-sounding cimbalom as to the relentless discontinuities that shape its phrasing, melody and timbre. To hear Stravinsky repeatedly is to understand how these innovations relate to one another, but the shock of having to process his music for the first time was real and literally physical. Here were strange folk- and jazz-inspired sounds, far removed from the Classical and Romantic orchestras that had framed our expectations of so-called classical music until that point. Ragtime’s sound was, and remains, quite alien.