Skateboarders see urban environments differently from non-skaters. Where the non-skater sees a bench that affords sitting, the skater sees more. They see a ledge that affords the possibility of executing a number of potential maneuvers. The general disposition to see surfaces as affording possibilities for skating and seeing possible sequences of maneuvers or ways to approach something (what skaters refer to as 'lines') has been christened 'the skater's eye'. Like other skilled ways of seeing, the skater's eye is a skilled trait. In this paper, the authors sketch an account of the skater's eye and consider the etiology of programming the skater's eye, focusing on the essential role of risk in acquiring the skater's eye. Specifically, acquiring the skater's eye requires that one actually experience skateboarding, taking risks to learn maneuvers, and executing them on surfaces that not only potentiate the execution of tricks but also potentiate bodily harm.
This paper considers sovereign attributions to skateboarding by various public intellectuals and scholars characterized as 'heroic', 'aristocratic', and of a 'higher style of play'. They argue that such attributions are indicative of a form of internal excellence (aret & eacute;) that manifests externally in a continuum from criminal vandal to Olympic athlete not unlike similar attributions in Archaic and Ancient Greece as well as the Edo period of Japan. They argue further that skateboarding's sovereign excellence includes subversive elements that present a ritualized reworking of the meaning and value of the city, tacitly redeeming it from a merely pecuniary role. While toying with sovereignty, its value for understanding excellence within the sport enclave, the authors also propose an epistemology that takes seriously the mythic poetics of skateboarding.
Composite materials have experienced continued growth in engineering applications due to their low weight, high strength, and customizable properties. However, their complex internal structure presents challenges for evaluating structural integrity and detecting damage, especially under mechanical loads. Non-destructive testing methods are essential for monitoring the structural health of composites without introducing damage to the material. Among various non-destructive testing techniques, Digital Image Correlation has emerged as an effective optical technique for measuring the deformation of composite materials without any contact. This study demonstrates the use of Augmented Lagrangian Digital Image Correlation to track strain evolution and predict failure location in composite specimens. In this study, a carbon fiber reinforced epoxy specimen was subjected to tensile testing, and its surface deformation was recorded at the same time using a Forward-Looking Infrared (FLIR) thermal camera. The camera captured detailed thermal images that reflected temperature changes associated with mechanical stress concentrations. The Augmented Lagrangian Digital Image Correlation technique was employed to analyze these images and quantify the evolving strain distributions across the specimen’s surface throughout the entire loading process. The results confirm that the method used can provide valuable insights into the deterioration of the composite specimen, offering a reliable and non-destructive approach to map strain progression and predict failure location.
Intracranial Hemorrhage (ICH) remains a critical life-threatening condition where timely and accurate diagnosis using non-contrast Computed Tomography (CT) scans is vital to reduce mortality and long-term disability. Deep learning methods have shown strong potential for automated hemorrhage detection, yet most existing approaches lack confidence quantification and clinical interpretability, which limits their adoption in high-stakes care. This study presents X-HEM, an explainable hemorrhage ensemble model for reliable detection of Intracranial Hemorrhage (ICH) on non-contrast head CT scans. The aim is to improve diagnostic accuracy, interpretability, and confidence for real-time clinical decision support. X-HEM integrates three convolutional backbones (VGG16, ResNet50, DenseNet121) through soft voting. Bayesian uncertainty is estimated using Monte Carlo Dropout, while Grad-CAM++ and SHAP provide spatial and global interpretability. Training and validation were conducted on the RSNA ICH dataset, with external testing on CQ500. The model achieved AUCs of 0.96 (RSNA) and 0.94 (CQ500), demonstrated well-calibrated confidence (low Brier/ECE), and provided explanations that aligned with radiologist-marked regions. The integration of ensemble learning, Bayesian uncertainty, and dual explainability enables X-HEM to deliver confidence-aware, interpretable ICH predictions suitable for clinical use.
