Into the Magic Cirlce. If all is cozy, what isn’t?:Some conceptual problems regarding cozy games

The emergence of ‘cozy games’ as a distinct game category gives rise to some fundamental questions about this category’s formal qualifications regarding ludic and narrative elements (including ludo-narrative dissonance), players’ experiences, developers’ intentions, and definitions of other constituent characteristics. Interpreting video games as digitally mediated ludo-narrative texts and employing a communication-oriented methodology, the two authors analyze, as case studies for their reflections on the problematic nature of the category, two well-known (if not unproblematic in their identification) ‘cozy games’ – Gris (Nomada Studio 2018) and Spiritfarer (Thunder Lotus Games 2021): both games are identified as ‘cozy’ by critics and players, but – quite paradoxically – deal with not-so-cozy subject matters like death, dying, and grief

A split-cavity design for the incorporation of a DC bias in a 3D microwave cavity

We report on a technique for applying a DC bias in a 3D microwave cavity. We achieve this by isolating the two halves of the cavity with a dielectric and directly using them as DC electrodes. As a proof of concept, we embed a variable capacitance diode in the cavity and tune the resonant frequency with a DC voltage, demonstrating the incorporation of a DC bias into the 3D cavity with no measurable change in its quality factor at room temperature. We also characterize the architecture at millikelvin temperatures and show that the split cavity design maintains a quality factor $Q_\text{i} \sim 8.8 \times 10^5$, making it promising for future quantum applications

Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models

Algorithmic music composition is a way of composing musical pieces with minimal to no human intervention. While recurrent neural networks are traditionally applied to many sequence-to-sequence prediction tasks, including successful implementations of music composition, their standard supervised learning approach based on input-to-output mapping leads to a lack of note variety. These models can therefore be seen as potentially unsuitable for tasks such as music generation. Generative adversarial networks learn the generative distribution of data and lead to varied samples. This work implements and compares adversarial and non-adversarial training of recurrent neural network music composers on MIDI data. The resulting music samples are evaluated by human listeners, their preferences recorded. The evaluation indicates that adversarial training produces more aesthetically pleasing music.Comment: Submitted to a 2023 conference, 20 pages, 13 figure

Model-based electron density profile estimation and control, applied to ITER

In contemporary magnetic confinement devices, the density distribution is sensed with interferometers and actuated with feedback controlled gas injection and open-loop pellet injection. This is at variance with the density control for ITER and DEMO, that will depend mainly on pellet injection as an actuator in feed-back control. This paper presents recent developments in state estimation and control of the electron density profile for ITER using relevant sensors and actuators. As a first step, Thomson scattering is included in an existing dynamic state observer. Second, model predictive control is developed as a strategy to regulate the density profile while avoiding limits associated with the total density (Greenwald limit) or gradients in the density distribution (e.g. neo-classical impurity transport). Simulations show that high quality density profile estimation can be achieved with Thomson Scattering and that the controller is capable of regulating the distribution as desired

In Search of Optimal Linkage Trees

Linkage-learning Evolutionary Algorithms (EAs) use linkage learning to construct a linkage model, which is exploited to solve problems efficiently by taking into account important linkages, i.e. dependencies between problem variables, during variation. It has been shown that when this linkage model is aligned correctly with the structure of the problem, these EAs are capable of solving problems efficiently by performing variation based on this linkage model [2]. The Linkage Tree Genetic Algorithm (LTGA) uses a Linkage Tree (LT) as a linkage model to identify the problem's structure hierarchically, enabling it to solve various problems very efficiently. Understanding the reasons for LTGA's excellent performance is highly valuable as LTGA is also able to efficiently solve problems for which a tree-like linkage model seems inappropriate. This brings us to ask what in fact makes a linkage model ideal for LTGA to be used

Evolving as a Digital Scholar

"What does it take to become a digitally agile scholar? This manual explains how academics can comfortably navigate the digital world of today and tomorrow. It foregrounds three key domains of digital agility: getting involved in research, education and (community) service, mobilising (digital) skills on various levels, and acting in multiple roles, both individually and interlinked with others. After an introduction that outlines the foundations of the three-dimensional framework, the chapters focus on different roles and skills associated with evolving as a digital scholar. There is the author, who writes highly specialised texts for expert peers; the storyteller, who crafts accessible narratives to a broader audience in the form of blogs or podcasts; the creator, who uses graphics, audio, and video to motivate audiences to delve deeper into the material; the integrator, who develops and curates multimedia artefacts, disseminating them through channels such as websites, webinars, and open source repositories; and finally the networker, who actively triggers interaction via social media applications and online learning communities. Additionally, the final chapters offer a blueprint for the future digital scholar as a professional learner and as a “change agent” who is open to and actively pursues innovation. Informed by the authors’ broad and diverse personal experience, Evolving as a Digital Scholar offers insight, inspiration, and practical advice. It equips a broad readership with the skills and the mindset to harness new digital developments and navigate the ever-evolving digital age. It will inspire academic teachers and researchers with different backgrounds and levels of knowledge that wish to enhance their digital academic profile.