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The visual challenges of short-range navigation in teleost fish
No full textTo understand how fish use vision to navigate, we must first understand what they see. This review explores how visually guided navigation in teleost fishes is shaped by the structure of their visual systems, the cognitive processes that interpret sensory input, and the dynamic environments they inhabit. With broad variation in habitat, ecology, and visual capabilities, fish provide a powerful system for examining how sensory conditions influence navigation. We focus on short-range navigation and review core strategies—beaconing, pilotage, path integration, and spatial mapping—alongside the visual and cognitive demands each entails. To assess which strategies are available to different species, we examine the visual processing pathway, from eye and retinal anatomy to behavioural evidence from cognition studies. These reveal that fish process visual information in a variety of ways to perform a diverse range of visual functions, including motion perception, object recognition, and generalisation across viewpoint or lighting changes. We consider how sensory limitations and visual noise may constrain navigational accuracy, and how context or visual ability might shape which strategies are used. Environmental changes, such as turbidity, light pollution, or habitat degradation or shifts, can further degrade cue availability and reliability, affecting navigational performance. Understanding how visual information is received, processed, and applied is therefore essential not only for interpreting observed behaviours, but also for predicting how fish may respond to changing environments. By linking sensory input with spatial behaviour, we propose a framework that integrates perception, cognition, and movement, offering new insight into how diverse visual systems shape navigation across species
Development of patient initiated follow up resources for UK rheumatology departments: a co-design study with patient and clinician collaboration
No full textData for Homogenized optoelectronic properties in perovskites: achieving high-efficiency solar cells with common chloride additives
No full textRaw data for 'Homogenized optoelectronic properties in perovskites: achieving high-efficiency solar cells with common chloride additives
Disaggregated economic accounts
No full textWe develop a system of disaggregated economic accounts. The system breaks down national accounting positions into bilateral flows between consistently defined groups of consumers (“consumer cells”), groups of producers (“producer cells”), the government, and the rest of the world. We disaggregate the full circular flow of money, including consumer spending, labor compensation, firm profits, trade in intermediates, foreign trade, and government transactions, while satisfying all national accounting identities. We implement the disaggregated system for small region-by-industry cells in Denmark and present stylized facts, including variation in domestic spending shares, local and urban bias in consumer spending, and a pattern of “triangular flows” across regions. Cell-level measures of “spending intensity” capture how much spending by a cell contributes to the income of cells experiencing unemployment after a shock. Using a general equilibrium model, we show that fiscal transfers are more effective in stimulating aggregate GDP when they target cells with high spending intensity on unemployed cells. Knowledge of the disaggregated economic accounts helps governments select more effective policies
Coverage with self-induced obstacles on grids
Full text linkModelling environments as grids is a common approach in robotics, particularly for coverage path planning tasks, where the goal is to fully traverse an area or visit key points. In this paper, we introduce a new variant of coverage planning, inspired by applications such as open-pit mining, harvesting, and painting, where the robot’s own actions modify the environment. Specifically, self-induced obstacles arise as a result of task execution, e.g. piles of rubble from drilling, and must be avoided in all future motion planning. We formally model these constraints by assuming that once a vertex is visited, it becomes non-traversable, and define an obstacle as self-induced if its existence depends on the set of previously visited vertices. This situation has not been addressed in existing formulations, which assume static obstacle placement. We provide a formal analysis of how the existence of solutions is affected by geometric constraints, such as vertex distances and robot turning radius, as defined by the Dubins vehicle. We also propose modelling the problem using self-deleting graphs based on the line graph of the grid. This provides a sufficient representation that captures the problem’s dynamics and enables the use of general graph search algorithms. Our experimental evaluation demonstrates that our approach outperforms classical coverage path algorithms in terms of computation time and solution quality
Neuro-symbolic federated learning over heterogeneous data-views: a structured approach to distributive EHR modelling
No full textFederated learning (FL) enables privacy-preserving model training across distributed Electronic Health Records (EHRs), but its deployment remains limited by data-view heterogeneity, where institutions maintain incompatible local schemas. Most existing methods address this by enforcing flat, aligned data views, which require extensive cross-site preprocessing and manual harmonisation that often discards clientspecific features, or by projecting inputs into a shared latent space, which sacrifices interpretability. We propose a modelling shift from conventional FL with vectorised inputs to a symbolic, relation-centric framework, where each client organises its EHR data as a structured, type-aware relational graph. This enables client-specific inference without requiring schema alignment and supports FL across heterogeneous data views. To model over these symbolic structures, we introduce an architecture that combines relation-aware message passing with a learnable feature relevance mechanism, jointly enabling accurate local predictions and client-specific interpretability while supporting parameter sharing across clients. Beyond strong performance on three real-world EHR datasets exhibiting data-view heterogeneity, we further show that our framework supports multimodal FL under modalitylevel heterogeneity. Using MC-MED, a publicly available multimodal emergency department dataset, we demonstrate that our method accommodates clients with partially missing modalities, highlighting its robustness and scalability in realworld clinical settings
Beyond the book: recycling print in early modern England
Full text linkThis essay considers the status of recycled pages of print in early modern English culture. The wide scope of this culture is noted (including the practice of re-using pages in book-bindings, and in the repair of musical instruments), and then the essay focuses in particular on the use of recycled pages of print to line boxes. A number of case studies are detailed to raise questions about the status of these texts as both waste papers drained of literary content, and as literary texts that continue to signify. The essay considers the broad implications of these case studies, in terms of the dominance of the idea of the book in literary studies, and in terms of the ways the study of material texts intersects with literary interpretation. [A.S.
Mismatching Expressions: Spatiotemporal and Kinematic Differences in Autistic and Non‐Autistic Facial Expressions
Full text linkLay Summary: This study compared the facial expressions produced by autistic and non‐autistic people. Our findings demonstrate that autistic and non‐autistic adults produce different angry, happy, and sad facial expressions, even after accounting for other interfering factors. This mismatch in facial expressions could explain why autistic people find it difficult to recognize non‐autistic expressions, and vice versa; autistic and non‐autistic faces may be essentially “speaking a different language” when it comes to conveying emotion. As such, what have previously been thought of as intrinsic emotion recognition “deficits” for autistic people may be more accurately described as difficulties resulting from cross‐neurotype interactions (i.e., interactions between autistic and non‐autistic people, as opposed to interactions between two autistic people). Further research is needed to test the impact of expressive differences on emotion recognition for autistic and non‐autistic people
