Introduction: Carbon and Culture Change

This introduction and the volume as a whole discuss the transformational role that carbon has come to play in social and cultural life. As a proxy for greenhouse gas emissions, carbon has become a phenomenon that can no longer be accounted for solely within the technoscientific vocabulary of climate scientists or as an economic externality to human modes of production. As a new value form, carbon has entered individual and collective imaginaries across the globe. We contend that this entrance is by no means uniform. The introduction thus attends to carbon as a range of diverse phenomena in human lives and, second, to the way that it can be approached as culture. It continues with a discussion of carbon's potential for generating or promoting change, before turning to the different contributions to this volume, and how they provide unique perspectives on the topic of carbon as a cultural phenomenon. The chapters are thus framed through a focus on the diverse meanings ascribed to carbon in different cultural contexts. In sum, in the introduction, together with the volume as a whole, we demonstrate how paying attention to carbon as a cultural phenomenon allows for a more profound appreciation of when, how and why carbon enables (or sometimes disables) change in the form of green transitions or transformations

Summon a demon and bind it: A grounded theory of LLM red teaming

Engaging in the deliberate generation of abnormal outputs from Large Language Models (LLMs) by attacking them is a novel human activity. This paper presents a thorough exposition of how and why people perform such attacks, defining LLM red-teaming based on extensive and diverse evidence. Using a formal qualitative methodology, we interviewed dozens of practitioners from a broad range of backgrounds, all contributors to this novel work of attempting to cause LLMs to fail. We focused on the research questions of defining LLM red teaming, uncovering the motivations and goals for performing the activity, and characterizing the strategies people use when attacking LLMs. Based on the data, LLM red teaming is defined as a limit-seeking, non-malicious, manual activity, which depends highly on a team-effort and an alchemist mindset. It is highly intrinsically motivated by curiosity, fun, and to some degrees by concerns for various harms of deploying LLMs. We identify a taxonomy of 12 strategies and 35 different techniques of attacking LLMs. These findings are presented as a comprehensive grounded theory of how and why people attack large language models: LLM red teaming

Modelling Recursion and Probabilistic Choice in Guarded Type Theory

Constructive type theory combines logic and programming in one language. This is useful both for reasoning about programs written in type theory, as well as for reasoning about other programming languages inside type theory. It is well-known that it is challenging to extend these applications to languages with recursion and computational effects such as probabilistic choice, because these features are not easily represented in constructive type theory. We show how to define and reason about a programming language with probabilistic choice and recursive types, in guarded type theory. We use higher inductive types to represent finite distributions and guarded recursion to model recursion. We define both operational and denotational semantics, as well as a relation between the two. The relation can be used to prove adequacy, but we also show how to use it to reason about programs up to contextual equivalence

Illegal loot box advertising on social media? An empirical study using the Meta and TikTok ad transparency repositories

Loot boxes are gambling-like products inside video games that can be bought with real-world money to obtain random rewards. They are widely available to children, and stakeholders are concerned about potential harms, e.g., overspending. UK advertising must disclose, if relevant, that a game contains (i) any in-game purchases and (ii) loot boxes specifically. An empirical examination of relevant adverts on Meta-owned platforms (i.e., Facebook, Instagram, and Messenger) and TikTok revealed that only about 7 % disclosed loot box presence. The vast majority of social media advertising (93 %) was therefore non-compliant with UK advertising regulations and also EU consumer protection law. In the UK alone, the 93 most viewed TikTok adverts failing to disclose loot box presence were watched 292,641,000 times total or approximately 11 impressions per active user. Many people have therefore been repeatedly exposed to prohibited and socially irresponsible advertising that failed to provide important and mandated information. Implementation deficiencies with ad repositories, which must comply with transparency obligations imposed by the EU Digital Services Act, are also highlighted, e.g., not disclosing the beneficiary. How data access empowered by law can and should be used by researchers is practically demonstrated. Policymakers should consider enabling more such opportunities for the public benefit

Vision-based classification for underwater safety critical applications

Computer vision is crucial for ensuring a safe future for the quickly growing underwater infrastructure monitoring industry, which currently relies primarily on labour-intensive and costly manual methods. Over the last twelve years, deep learning models have revolutionized the field of computer vision and have been applied across various domains. These models could potentially assist in developing underwater surveys, for example, by analyzing videos and image data from equipment inspection and environment monitoring, thereby automating the process and reducing the time spent on visual inspections. Despite the success of deep learning models, underwater images impose challenges not faced with in-air images. Underwater images are generally of poor quality with issues such as blur, haziness, non-uniform illumination, and color degradation. These factors raise concerns about the performance of well-known deep learning models in underwater applications.As a subset of machine learning, deep learning models are datadriven, and the quality of the training data impacts the model’s performance. Furthermore, as a consequence of the large number of parameters, they are data-greedy, requiring large datasets for effective training. However, large underwater datasets are difficult to generate and not widely available. Collecting underwater data relies on the availability of underwater vehicles, specialists to perform the survey, and favorable weather and water conditions. Once collected, the data needs to be labeled. Data annotation is an extremely laborious task, prone to human errors, which reduces the quality of the final dataset. Furthermore, a well-known problem in deep learning is the overconfidence of the models, which can predict outputs with high probability even for inputs out of distribution (OOD) of the training data. This dissertation leverages this overconfidence by employing predictive uncertainty to identify the most important images for labelling, addressing limited financial or human resources for data annotation.An extensive review of the state-of-the-art of deep learning models applied to underwater images concluded that predictive uncertainty is rarely used in this field. The literature review also revealed the scarcity of large underwater datasets and the researchers’ efforts to develop models and training strategies to overcome this limitation. This lack of publicly available datasets is even more pronounced for industrial applications. To address this gap, this thesis supported the development and release of three publicly available underwater RGB datasets: MIMIR (synthetic), SubPipe, and MarinaPipe (real-world datasets). The contribution for the MIMIR project consisted of evaluating the dataset usability in the context of pipeline segmentation. In the SubPipe and MarinaPipe projects, the work consisted on annotating and evaluating the datasets for image segmentation. MarinaPipe was recorded in very shallow waters, resulting in images with visible sunlight rays, and contains pipelines occluded by the sediments from the marina floor. SubPipe was recorded in deeper waters, leading to darker images, with many parts of the pipeline covered by sand. The links for downloading these three datasets are available in the REMARO GitHub (https://github.com/remaro-network).The overconfidence in deep learning models is an overwhelming concern. However, it is possible to leverage this overconfidence by calculating the predictive uncertainty to assess the models’ lack of knowledge and use this information to reduce the effort required to generate labeled datasets. This thesis investigated the hypotheses that, given a model trained on synthetic data, the predictive uncertainty enables the selection of real-world images about which the model demonstrates little to no knowledge, for fine-tuning the model and bridging the synthetic-to-reality gap that exists even for photorealistic images. Selecting images based on uncertainty, calculated with Monte Carlo dropout, resulted in a model with better performance compared to randomly selecting the same amount of images.Additionally, this research explored using predictive uncertainty calculated with Monte Carlo dropout for active learning in the underwater domain. When training with real underwater pipeline images and using uncertainty-driven active learning for selecting images, at least 15.9% fewer annotations were needed to achieve the same performance as a model trained on randomly selected images. In addition, this PhD research trained a generalized few-shot segmentation model and used predictive uncertainty to evaluate the reliability of the predictions using mutual information and entropy. The experiments performed indicate that predictive uncertainty both increases the reliability of deep learning models and optimizes the use of human and financial resources available for data annotation by selecting the most informative data samples

Learning and Combinatorial Optimization for Efficient Container Vessel Stowage Planning

Container shipping plays an essential role in the global transportation of interna-tionally traded goods, making it a crucial component of the world economy. Dueto the large volume of cargo moved by each vessel, container shipping is also oneof the most environmentally friendly modes of transport, resulting in significantlylower emissions per tonne of cargo per kilometer compared to alternatives.A key operational challenge in container shipping is deciding how to efficientlyplace containers onto vessels, a task known as stowage planning. This task is criticalyet challenging, involving many factors and constraints that interact in combina-tionally difficult ways. Due to its complexity, stowage planning is usually split intotwo phases: (1) the master planning problem (MPP), which broadly determines howcontainers are grouped and placed onboard, and (2) the slot planning problem (SPP),which assigns individual containers to specific slots.This PhD research explores how advanced techniques from combinatorial optimiza-tion (CO) and machine learning (ML) can accelerate decision-making for stowageplanning, especially at the master planning level. The goal is to develop efficient andpractical solutions that bridge the gap between theoretical models and real-world in-dustry needs, leading to faster and better planning decisions that result in reliableand efficient supply chains with implications for global trade and environmentalsustainability.The research comprises four main contributions: (1) a comprehensive literature re-view, (2) novel problem formulations of the MPP, (3) scalable CO and ML-basedsolutions methods, (4) theoretical analysis on computational complexity and math-ematical soundness.First, a literature review classifies the single-port and multi-port container stowageplanning problem (CSPP), highlighting key issues such as oversimplified problemformulations and limited industrial validation. A research agenda is proposed toaddress challenges, such as the need for scalable algorithms to solve realistic prob-lem definitions on benchmark instances, with particular attention to the MPP.Second, building on these insights, novel problem formulations are provided in theform of a 0-1 integer program (IP) model that searches in the space of valid pairedblock stowage and a Markov decision process and its extension that both decomposethe decision process into sequential steps. Furthermore, this thesis includes pairedblock stowage patterns and demand uncertainty in the MPP, which are features toconsider in the MPP.Third, the findings indicate that the 0-1 IP model outperforms a traditional mixed-integer programming (MIP) model in terms of optimality and runtime. Regardless,larger problem instances require more than 10 minutes to solve, which is consideredintractable given the dynamic nature of stowage planning. In contrast, the MDPs ad-dressed by deep reinforcement learning (DRL) can construct solutions for the MPPwithin this timeframe. However, the MDPs do not offer guarantees on optimalityand feasibility, which need to be learned through extensive training. Especially onfeasibility, it is shown that differentiable projection layers are needed to ensure fea-sibility, while alternatives as reward scaling and feasibility regularization can workbut are hard to balance with other objectives. In the case of specific non-convex con-straints, action masking in combination with feasibility projection can be applied.Fourth, this thesis shows that searching in the space of valid block stowage patternis an NP-hard task but also demonstrates how a differentiable projection layer basedon violation gradients can minimize the violations of convex inequality constraints.This research advances both theory and practice in stowage planning by introduc-ing scalable optimization techniques. It highlights the value of improved problemformulations and learning-based heuristics for real-world planning problems. Thesecontributions show how decision-support systems can be enhanced, paving the wayfor more resilient and efficient container shipping