Fréchet Distance in Unweighted Planar Graphs.

The Fréchet distance is a distance measure between trajectories in ℝ^d or walks in a graph G. Given constant-time shortest path queries, the Discrete Fréchet distance D_G(P, Q) between two walks P and Q can be computed in O(|P|⋅|Q|) time using a dynamic program. Driemel, van der Hoog, and Rotenberg [SoCG'22] show that for weighted planar graphs this approach is likely tight, as there can be no strongly-subquadratic algorithm to compute a 1.01-approximation of D_G(P, Q) unless the Orthogonal Vector Hypothesis (OVH) fails.Such quadratic-time conditional lower bounds are common to many Fréchet distance variants. However, they can be circumvented by assuming that the input comes from some well-behaved class: There exist (1+ε)-approximations, both in weighted graphs and in ℝ^d, that take near-linear time for c-packed or κ-straight walks in the graph. In ℝ^d there also exists a near-linear time algorithm to compute the Fréchet distance whenever all input edges are long compared to the distance. We consider computing the Fréchet distance in unweighted planar graphs. We show that there exist no strongly-subquadratic 1.25-approximations of the discrete Fréchet distance between two disjoint simple paths in an unweighted planar graph in strongly subquadratic time, unless OVH fails. This improves the previous lower bound, both in terms of generality and approximation factor. We subsequently show that adding graph structure circumvents this lower bound: If the graph is a regular tiling with unit-weighted edges, then there exists an Õ((|P|+|Q|)^{1.5})-time algorithm to compute D_G(P, Q). Our result has natural implications in the plane, as it allows us to define a new class of well-behaved curves that facilitate (1+ε)-approximations of their discrete Fréchet distance in subquadratic time

Industrial Excess:Data Storage, Energy and Utility Planning Before, During and After Digital Industrialisation

Excess is usually understood in research as the point at which materiality gets too much. This article shows instead that materiality is always already excessive. The energy utility workers in our study convey that any product making industry also makes excess. In their view, excess as an energetic form is impossible to eliminate from industrial operations. Excess can be reduced, but complete elimination is only possible if industrial operation did not exist. This concretised state of excess becomes apparent when studying the plans facilitating digital industries’ expansion projects. We focus on an implemented utility infrastructure plan for connecting a ‘big tech’ hyperscale datacentre to a public energy system and the classification work it involves. This particular plan leads us to the analytical object of industrial excess. Despite the high impact on public infrastructures and energy consumption, utility plans and these connections with industrialisation projects have been overlooked within scholarship on the digital economy and datacentres. We call this process of connection - making digital industrialisation. Our ethnography with utility workers in Odense, Denmark, shows three analytical entries of boundaries, scales, and admission points into the practices of planning for, with and against excess in connecting expanding industries to publicly owned, non-profitable utility infrastructure. The utility plan shields the energy system against high pollution impacts of digital industrialisation at a municipal scale but exposes its climatic consequences at a transnational scale. The notion of industrial excess devise how forms of industrial product-making and consumptions of industrial products are infrastructurally normalized, and which are not, ultimately giving insight into the radical potential of the non-profitable utility as a figure for ecological transformatio

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

DaKultur: Evaluating the Cultural Awareness of Language Models for Danish with Native Speakers

Large Language Models (LLMs) have seen widespread societal adoption. However, while they are able to interact with users in languages beyond English, they have been shown to lack cultural awareness, providing anglocentric or inappropriate responses for underrepresented language communities. To investigate this gap and disentangle linguistic versus cultural proficiency, we conduct the first cultural evaluation study for the mid-resource language of Danish, in which native speakers prompt different models to solve tasks requiring cultural awareness. Our analysis of the resulting 1,038 interactions from 63 demographically diverse participants highlights open challenges to cultural adaptation: Particularly, how currently employed automatically translated data are insufficient to train or measure cultural adaptation, and how training on native-speaker data can more than double response acceptance rates. We release our study data as DaKultur - the first native Danish cultural awareness dataset

Faster, Deterministic and Space Efficient Subtrajectory Clustering

Given a trajectory T and a distance Δ, we wish to find a set C of curves of complexity at most , such that we can cover T with subcurves that each are within Fréchet distance Δ to at least one curve in C. We call C an (,Δ)-clustering and aim to find an (,Δ)-clustering of minimum cardinality. This problem variant was introduced by Akitaya et al. (2021) and shown to be NP-complete. The main focus has therefore been on bicriteria approximation algorithms, allowing for the clustering to be an (, Θ(Δ))-clustering of roughly optimal size.We present algorithms that construct (,4Δ)-clusterings of (k log n) size, where k is the size of the optimal (, Δ)-clustering. We use (n³) space and (k n³ log⁴ n) time. Our algorithms significantly improve upon the clustering quality (improving the approximation factor in Δ) and size (whenever ∈ Ω(log n / log k)). We offer deterministic running times improving known expected bounds by a factor near-linear in . Additionally, we match the space usage of prior work, and improve it substantially, by a factor super-linear in n, when compared to deterministic results

MarineLLM-PDDL: Generation of Planning Domains for Marine Vessels Using Past Incident Response Plans

Testing the hardware and software of marine vessels in field trials is a necessity to avoid technical and environmental catastrophes. Conducting tests with large vessels is costly. Multiple realistic domain descriptions based on past missions could increase the value of simulation tests, reducing the need for expensive field tests. In this paper, we generate scenarios from unstructured Incident Response Plan (IRP) documents using Large Language Models (LLMs), converting them to standard structured planning programs. The two synthesized marine test-domain datasets contain approximately 90% parsable, 75% solvable, and 57% correct planning programs

Human excreta recycling in Sweden: a PESTEL-SWOT framework analysis – Review

Source-separating sanitation systems can maximise resource recovery from wastewater and mitigate the environmental impacts of conventional wastewater treatment plants, including eutrophication and climate change. This study conducts a comprehensive review of the literature on source-separating sanitation systems in Sweden, aiming to identify the challenges hindering their diffusion and potential expansion opportunities. Employing a rapid evidence synthesis approach, we extracted data from the Web of Science and supplemented findings through hand-searches in additional electronic databases. Of the 713 studies initially identified, 24 met our stringent inclusion criteria. The analysis was structured around a combined PESTEL (Political, Economic, Technical, Social, Environmental, Legal) and SWOT (Strengths, Weaknesses, Opportunities, Threats) framework to synthesise the existing body of work and discern main patterns. The findings underscore the untapped strengths in these technologies' potential in enhancing nutrient recovery and food security, in addition to reducing eutrophication and greenhouse gas emissions. The studies analysed reported Sweden's strengths in source separation, highlighting organisational diversity, market benefits, social acceptance, technological readiness, and nutrient recovery, all contributing to the SDGs and addressing challenges such as eutrophication and limited sanitation access. The primary challenges were identified as social and cultural taboos towards the recycling of human excreta, disbelief in its quality as a fertiliser, concerns about hazardous substances like pharmaceuticals, and a preference for using it to grow non-food crops. Our article main contribution lies in proposing 12 structured upscaling strategies addressing these barriers and leveraging the opportunities identified including policy measures to incentivise circular practices, building support through stakeholder engagement, updating building codes to require double piping, and enhancing municipal-utility cooperation. While grounded in Sweden, our study contributes to research on the broader shift towards sustainable food systems by leveraging internal strengths and external opportunities in circular wastewater systems

The Digital Leviathan: Prediction, Politics and Police Power in POL-INTEL

In this digital era, police forces across the globe are turning to cutting-edge data analytics for the purpose of enacting more efficient police power through predicting and pre-empting crime. In Denmark, allegedly one of the most digitalized countries in the world, the police have turned to the American firm Palantir Technologies to produce a platform named POL-INTEL to integrate, analyze and visualize mass amounts of data from different data bases. For the Danish national police, this platform was heralded as a “super weapon” with predictive policing capacities that would represent a “quantum leap” into the future of law enforcement. For critics, POL-INTEL has been branded a tool of mass surveillance. With this background in mind, this thesis asks two questions: How is police power imagined and enacted in the digital era? And how is governance over the police materialized in relation to data-driven policing? To answer these questions, this thesis develops a methodological framework that combines ethnographic, historiographic and interventionist approaches. Ethnographically, data is drawn from interviews with police officers as well as a variety of other actors, while following the data of those profiled by the police through the criminal justice system. Furthermore, a variety of documents, ranging from public accounts in the press serving to detail the public debate, to internal police handbooks, state reports, etc. are featured. In terms of theory, this thesis synthesizes concepts from critical theory and Science and Technology Studies in particular, alongside Critical Data Studies, police studies with a particular focus on predictive policing, as well as critical criminology. Together, these produce a useful framework for analyzing the complexities of police power, and the materialization of governance, on multiple different levels. Specifically, the thesis investigates the history of police power, tracing how police power has been imagined from the 17th century to the modern notion of predictive policing. POL-INTEL constitutes a case of digital police technology that is expected and portrayed as if it brings immense efficiency in producing social order through the application of science and technology. Through this investigation, the thesis historically ties the notion of predictive policing to the state in a way that has generally been obscured in earlier literature. Concretely, the thesis argues that predictive techniques and technologies have been a major element in the enactment of police power throughout history and follows how the specific notion of predictive policing has been revised and demarcated in the modern era, which has created conceptual inflation. In contrast, the notion of “prediction in action” is launched as a way of capturing the variety of ways law enforcement attempts to predict across different sites and with different technologies. Moreover, the thesis shows that police power has been imagined through predictive data analytics such as POL-INTEL in ways that conflict with how police power is enacted in practice, where the promised effects and new working methods are rarely fully implemented or successful. Instead, the thesis shows that the ways police power is imagined are ideological and serve to black box the enactment of police power. In turn, this black boxing means that police are able to hide their own biases, practices and politics, as well as how they influence the state itself by strategically navigating those forms of governance materialized to control law enforcement. This discovery reverses classical philosophical schemas of police as subordinate to the state and underlines instead how police power may influence government institutions and elected politicians. Details of the complexities, contradictions, and nuances of how police power is enacted in the digital era and how governance over the police is materialized in relation to data-driven policing are also explored. For instance, this thesis described in depth the internal conflicts and contradictions within the police regarding POL-INTEL as a managerial tool that attempts to curtail, limit or direct police discretion. At the same time, the thesis underlines how police discretionary power is still a significant factor in Danish law enforcement with racially biased police profiling practices feeding biased data into the platform. By utilizing and developing the concept of feedback loops, a multiplicity of feedback loops are also traced that quantitatively or qualitatively affect the lives of individuals profiled by the police, while mechanisms such as ghetto classifications and police predictions are fed into governance. This thesis thereby concretely connects the relation between police and the state in the digital era while also accentuating the contradictions of how police power is imagined and enacted. It specifies and details police predictive practices in action, thereby revealing a process that spans the human, non-human and the imaginary

Re-Imagining the Landscape of Future Farming

This paper explores how participatory design methods can engage future farmers in imagining sustainable agricultural futures in Denmark. Denmark's agricultural sector faces a "crisis of imagination" where dominant paradigms in farming are hard to challenge. Building on Ingold's notion of the landscape as a relational, temporal space for dwelling, we propose a conceptualization of the landscape game that enables farming students to articulate their visions of future farming. The design game creates opportunities for participants to situate their imagined farms within broader social, environmental, and technical contexts while exploring how these might evolve over time. This approach aims to generate new agricultural imaginaries that move beyond current techno-solutionist or radical transformation narratives, while supporting an underrepresented political public in developing their own perspective on contested agricultural futures that are both speculative and grounded in lived experience