CoolWalks for active mobility in urban street networks

Walking is the most sustainable form of urban mobility, but is compromised by uncomfortable or unhealthy sun exposure, which is an increasing problem due to global warming. Shade from buildings can provide cooling and protection for pedestrians, but the extent of this potential benefit is unknown. Here we explore the potential for shaded walking, using building footprints and street networks from both synthetic and real cities. We introduce a route choice model with a sun avoidance parameter α and define the CoolWalkability metric to measure opportunities for walking in shade. We derive analytically that on a regular grid with constant building heights, CoolWalkability is independent of α, and that the grid provides no CoolWalkability benefit for shade-seeking individuals compared to the shortest path. However, variations in street geometry and building heights create such benefits. We further uncover that the potential for shaded routing differs between grid-like and irregular street networks, forms local clusters, and is sensitive to the mapped network geometry. Our research identifies the limitations and potential of shade for cool, active travel, and is a first step towards a rigorous understanding of shade provision for sustainable mobility in cities

How to Value Open Source Contributions? An Institutional Perspective from CERN

Supplementary material for the paper "How to Value Open Source Contributions? An Institutional Perspective from CERN" submitted to the Software Engineering in Practice (SEIP) track of ICSE 2026. This contains the analysis code and the interview questions used that were used in this work

Synthetic emotions and Gamification: Exploring Engagement Strategies for Small Social Robots in different age-groups

This paper investigates engagement strategies for pocket-sized social robots designed to aid children in the longer term with anxiety disorders. We explore engagement strategies for a pocket-sized tactile robot designed to support children with anxiety through daily interaction. The robot delivers either synthetic emotional feedback or gamified point rewards to encourage user participation. We evaluated these strategies through two studies conducted in contrasting age groups and settings: a school-based preference study with young children, and a full-day behavioral study with university students in naturalistic environments. The study with school children (aged 6-8, n=16) indicated a preference for emotional engagement over points-based approaches. The follow up study with university students (n=14) across a full day of interactions revealed contrasting results: points-based systems produced significantly higher task accuracy (p < 0.05) and sustained performance over time. Points-based participants also rated their robot as more human-like and likable, challenging assumptions about emotional expressiveness being necessary for positive robot perception. These findings across different age groups suggest that stated preferences may not predict behavioral effectiveness, with structured reward mechanisms demonstrating superior performance outcomes despite lower user preference ratings in younger populations. This work contributes insights into age-related differences in engagement strategy effectiveness in human-robot interaction design, highlighting the importance of behavioral validation alongside preference assessment in therapeutic robotics

The AI Gap: How Socioeconomic Status Affects Language Technology Interactions

Socioeconomic status (SES) fundamentally influences how people interact with each other and, more recently, with digital technologies like large language models (LLMs). While previous research has highlighted the interaction between SES and language technology, it was limited by reliance on proxy metrics and synthetic data. We survey 1,000 individuals from ‘diverse socioeconomic backgrounds’ about their use of language technologies and generative AI, and collect 6,482 prompts from their previous interactions with LLMs. We find systematic differences across SES groups in language technology usage (i.e., frequency, performed tasks), interaction styles, and topics. Higher SES entail a higher level of abstraction, convey requests more concisely, and topics like ‘inclusivity’ and ‘travel’. Lower SES correlates with higher anthropomorphization of LLMs (using ”hello” and ”thank you”) and more concrete language. Our findings suggest that while generative language technologies are becoming more accessible to everyone, socioeconomic linguistic differences still stratify their use to create a digital divide. These differences underscore the importance of considering SES in developing language technologies to accommodate varying linguistic needs rooted in socioeconomic factors and limit the AI Gap across SES groups

data2lang2vec: Data Driven Typological Features Completion

Language typology databases enhance multilingual Natural Language Processing (NLP) by improving model adaptability to diverse linguistic structures. The widely-used lang2vec toolkit integrates several such databases, but its coverage remains limited at 28.9%. Previous work on automatically increasing coverage predicts missing values based on features from other languages or focuses on single features; we propose to use textual data for better-informed feature prediction. To this end, we introduce a multi-lingual Part-of-Speech (POS) tagger, achieving over 70% accuracy across 1,749 languages, and experiment with external statistical features and a variety of machine learning algorithms. We also introduce a more realistic evaluation setup, focusing on likely to be missing typology features, and show that our approach outperforms previous work in both setups

Iterative Structured Knowledge Distillation: Optimizing Language Models Through Layer-by-Layer Distillation

Traditional language model compression techniques, like knowledge distillation, require a fixed architecture, limiting flexibility, while structured pruning methods often fail to preserve performance. This paper introduces Iterative Structured Knowledge Distillation (ISKD), which integrates knowledge distillation and structured pruning by progressively replacing transformer blocks with smaller, efficient versions during training. This study validates ISKD on two transformer-based language models: GPT-2 and Phi-1. ISKD outperforms L1 pruning and achieves similar performance to knowledge distillation while offering greater flexibility. ISKD reduces model parameters - 30.68% for GPT-2 and 30.16% for Phi-1 - while maintaining at least four-fifths of performance on both language modeling and commonsense reasoning tasks. These findings suggest that this method offers a promising balance between model efficiency and accuracy

Ghana's 2024 Elections: Ghanaians Vote for Renewal and Accountability

Since Ghana's return to democratic rule in 1992, the West African country has recurrently been heralded as the model for democracy in Africa. Despite multiple controversies challenging core democratic institutions, Ghana's 2024 elections again represent a strong indicator of the country's democratic resilience. Combining our multi-disciplinary perspectives, we identify the key concerns that preoccupied Ghanaian voters in the lead-up to election day on 7 December 2024. We argue that there is a disconnect between campaign promises, such as the transition into a digital economy, and Ghanaians’ existential worries about the future. Concerns about both environmental and economic liveability equally informed the voter migration behind the 2024 election's unusually large margin of victory. Debates around the alignment of both flagbearers with Ghana's major religious groups, alongside Ghanaians’ rejection of the dismantling of democratic institutions, indicate that Ghana's new government will have to live up to voters’ demands for authenticity and accountability

The Calculated Typer (Functional Pearl)

We present a calculational approach to the design of type checkers, showing how they can be derived from behavioural specifications using equational reasoning. We focus on languages whose semantics can be expressed as a fold, and show how the calculations can be simplified using fold fusion. This approach enables the compositional derivation of correct-by-construction type checkers based on solving and composing fusion preconditions. We introduce our approach using a simple expression language, to which we then add support for exception handling and checked exceptions

Three faces of autonomy: Exploring configurations of high autonomy in software project teams

This article seeks to provide deeper insights into the concept of team autonomy within the software industry by investigating the combinations of autonomy and control modes that lead to high perceptions of team autonomy. Additionally, it examines the types of autonomy and control that are most effective for navigating complex environments.The study is grounded in the framework of Complex Adaptive Systems (CAS), integrating interdisciplinary research on autonomy and control to develop a research design. Methodologically, the study employs survey data and qualitative comparative analysis (QCA) to address its research questions.The findings identify three distinct configurations of projects that achieve high team autonomy, demonstrating how the road to high team autonomy can be shaped in various ways in relation to the presence of different modes of control. Using the CAS framework to evaluate these configurations, the third configuration emerges as the most aligned with the framework and empirically the most successful. This configuration is characterized by the absence of control for safeguarding purposes, the presence of control for coordination purposes, and the presence of joint decision-making.The article concludes by discussing the fuzzy and contextual nature of autonomy and its inherent relationship with control. It emphasizes the importance of understanding autonomy within its specific context and highlights the value of applying the CAS framework to grasp the complexity of autonomy-control dynamics. This study contributes to the literature by offering a nuanced perspective on autonomy in teams and its role in addressing the challenges of complexity in projects

Hidden Layer Interaction: A Technique to Explore the Material of Generative AI

This pictorial describes the process of developing an interaction technique for directly engaging with the hidden layers of a generative AI model for image synthesis. First, we give some background to generative AI in HCI, arguing that current interaction techniques prevent us from directly interacting with the material of AI, foreclosing its use in design. Drawing on inspiration from the Computer Science field of feature visualization, we investigate the materiality of our prototype, a GAN model trained to generate fashion imagery, and show how Hidden Layer Interaction offers an alternative to standard prompting. In doing so, we illustrate how this change in approach leads to new forms of interaction with the internal semantics of generative AI, and demonstrate how one might use Hidden Layer Interaction to engage with AI as a material in design