Abstract

Abstract
The interaction between code and language shapes emergence and innovation in computational systems, turning them not merely into a series of connected structures but into narrative spaces. Interactive Digital Narratives (IDNs) are characterized by a tension between the control exerted by the system to engage readers and the autonomy that readers desire over the narrative's direction. This results in a ludic paradox, where the role of the narrative system is to enable and facilitate play while simultaneously being capable of communicating the outcomes of the readers' actions. On the other hand, the reader must be able to participate actively by playing along the system's rules. Based on the notion of interpassivity, which refers to the delegation of the cognitive activity to the object, thus transforming the reader into a passive observer of the system's interactions, this paper aims to explore the interplay between interpassivity and interactivity. As we navigate IDNs, we engage with narratives that challenge and empower readers, that create immersive and enriching experiences, and transform their relationships with the computational system. This contributes to understanding the pleasure of playing and the reader's role. Based on the premise that readers can derive pleasure from automation but also yearn for control over the narrative, we can investigate the playful interaction between humans and machines. This paper will analyze Emissaries (2015-2017), defined by its creator, Ian Cheng, as a video game that plays itself, and where the reader can seemingly only visualize the work. In this case study, we will look for narrative mechanics and the specificity of the medium in which the IDN is instantiated. We will discuss how the computational system actively shapes the narrative without direct reader input and consequently propose a reconceptualization of the concept of interpassivity and its relationship with interactivity.

Abstract
The increasing penetration of renewable energy sources in power systems has heightened the importance of grid-forming (GFM) converters, which emulate the dynamic behavior of synchronous machines and are crucial for ensuring stability in converter-dominated grids. However, the complexity of modern grids calls for innovative control mechanisms to unlock the full potential of GFM technology. This work presents a novel automated framework for control design in power systems. Simulated annealing is used to evolve the structural design of control systems represented as graph-based models. The method achieves greater flexibility by using control graphs instead of traditional tree-based representations, supporting complex feedback loop configurations. A simplification process is also included to reduce complexity and improve interpretability, ensuring practical applicability. Validation on a two-generator power system with one GFM converter demonstrates the method's ability to design robust controllers that enhance system stability, achieving better performance metrics, such as smoother frequency responses with significantly reduced frequency deviations compared to benchmark configurations. The improved frequency response arises from differing terminal angle profiles, enabling faster, stronger power responses that quickly arrest frequency deviations during disturbances.

Abstract
As in school, one teacher to cover all subjects is insufficient to distill equally robust information to a student. Hence, each subject is taught by a highly specialised teacher. Following a similar philosophy, we propose a multiple specialized teacher framework to distill knowledge to a student network. In our approach, directed at face recognition use cases, we train four teachers on one specific ethnicity, leading to four highly specialized and biased teachers. Our strategy learns a project of these four teachers into a common space and distill that information to a student network. Our results highlighted increased performance and reduced bias for all our experiments. In addition, we further show that having biased/specialized teachers is crucial by showing that our approach achieves better results than when knowledge is distilled from four teachers trained on balanced datasets. Our approach represents a step forward to the understanding of the importance of ethnicity-specific features.

Abstract
Recent verification tools aim to make formal verification more accessible for software engineers by automating most of the verification process. However, the manual work and expertise required to write verification helper code, such as loop invariants and auxiliary lemmas and assertions, remains a barrier. This paper explores the use of Large Language Models (LLMs) to automate the generation of loop invariants for programs in Dafny. We tested the approach on a curated dataset of 100 programs in Dafny involving arrays, strings, and numeric types. Using a multimodel approach that combines GPT-4o and Claude 3.5 Sonnet, correct loop invariants (passing the Dafny verifier) were generated at the first attempt for 92% of the programs, and in at most five attempts for 95% of the programs. Additionally, we developed an extension to the Dafny plugin for Visual Studio Code to incorporate automatic loop invariant generation into the IDE. Our work stands out from related approaches by handling a broader class of problems and offering IDE integration.

Abstract
The electrification of transportation, driven by the widespread adoption of electric vehicles and increased integration of renewable energy, is critical to decarbonizing mobility and society. Demand response strategies, such as dynamic pricing, enable indirect control of charging processes, but their success relies on accurately estimating consumer responses to tariff changes. Observational data can provide insights into consumer behavior, but the presence of confounding variables motivates the use of causal inference techniques for a reliable elasticity estimation. This study proposes a data-driven framework for optimizing dayahead charging tariffs, leveraging causal discovery and inference algorithms validated on a synthetically generated dataset. A sensitivity analysis explores the impact of data availability on elasticity estimation and the performance of the resulting demand response strategy. The findings highlight the potential of causal machine learning to characterize consumers and, ultimately, the need for regular characterization to improve the efficiency of demand-side management.