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Children with life-limiting and disabling conditions are surviving longer than previously, and many require palliative and supportive care, usually at home. Home-based care can put family life under considerable strain, as parents care for their child's complex, often unpredictable, continuing care needs. Rainbow Trust Children's Charity aims to bridge gaps in services for children with life-threatening or terminal conditions by providing family support workers (FSWs). The study used a range of methods (surveys, interviews and ethnographic observation) approach to explore key aspects of the work of the FSWs. The target population for the surveys was families with a child having complex, life-threatening or terminal conditions receiving care from FSWs. The participants included 55 families (12 bereaved) and 39 children aged 2-18 years. Thematic analysis revealed how the FSWs became a presence in families' lives in three main ways: (1) encompassing and embracing families through supporting needs and promoting resilience; (2) befriending and bonding through developing knowledge, trusting relationships and a sense of closeness; and (3) accompanying and enduring by 'being with' families in different settings, situations and crises and by enduring alongside the families. The study demonstrated the fundamental importance of workers who are able to provide aspects of support that is usually not provided by other services.

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Generative Adversarial Networks (GANs) have been used with great success to generate images. They have also been applied to the task of Procedural Content Generation (PCG) in games, particularly for level generation, with various approaches taken to solving the problem of training data. One of those approaches, TOAD-GAN (Token-Based One-Shot Arbitrary Dimension Generative Adversarial Network) (Awiszus, Schubert, and Rosenhahn 2020), can generate levels based on a single training example and has been able to closely reproduce token patterns found in the training sample. While TOAD-GAN is an impressive achievement, questions remain about what exactly it has learned. Can the generator be made to produce levels that are substantially different from the level it has been trained on? Can it reproduce specific level segments? How different are the generated levels? We investigate these questions and others by using the CMA-ES algorithm for Latent Space Evolution. To make the search space feasible, we use a random projection in latent space. We propose the investigation undertaken here as a paradigm for studies into what machine-learned generators have actually learned, and also as a test of a new method for projecting from a smaller search space to a larger latent space.

We present a method for generating arrangements of indoor furniture from human-designed furniture layout data. Our method creates arrangements that target specified diversity, such as the total price of all furniture in the room and the number of pieces placed. To generate realistic furniture arrangement, we train a generative adversarial network (GAN) on human-designed layouts. To target specific diversity in the arrangements, we optimize the latent space of the GAN via a quality diversity algorithm to generate a diverse arrangement collection. Experiments show our approach discovers a set of arrangements that are similar to human-designed layouts but varies in price and number of furniture pieces.