Problematizing cultural appropriation

Cultural appropriation in games entails the taking of knowledge, artifacts or expression from a culture and recontextualizing it within game structures. While cultural appropriation is a pervasive practice in games, little attention has been given to the ethical issues that emerge from such practices with regards to how culture is portrayed. This paper problematizes cultural appropriation in the context of a serious game for children inspired by Día de los Muertos, a Mexican festival focused on remembrance of the dead. Taking a research through design approach, we demonstrate that recontextualised cultural elements can retain their basic, original meaning. However, we also find that cultural appropriation is inevitable and its ethical implications can be far reaching. In our context, ethical concerns arose as a result of children’s beliefs that death affects prominent others and their destructive ways of coping with death. We argue that revealing emergent ethical concerns is imperative before deciding how and in what way to encourage culturally authentic narratives

Gaussian Markov Random Fields for fusion in information form

© 2016 IEEE. 2.5D maps are preferable for representing the environment owing to their compactness. When noisy observations from multiple diverse sensors at different resolutions are available, the problem of 2.5D mapping turns to how to compound the information in an effective and efficient manner. This paper proposes a generic probabilistic framework for fusing efficiently multiple sources of sensor data to generate amendable, high-resolution 2.5D maps. The key idea is to exploit the sparse structure of the information matrix. Gaussian Markov Random Fields are employed to learn a prior map, which uses the conditional independence property between spatial location to obtain a representation of the state with a sparse information matrix. This prior map encoded in information form can then be updated with other sources of sensor data in constant time. Later, mean state vector and variances can be also efficiently recovered using sparse matrices techniques. The proposed approach allows accurate estimation of 2.5D maps at arbitrary resolution, while incorporating sensor noise and spatial dependency in a statistically sound way. We apply the proposed framework to pipe wall thickness mapping and fuse data from two diverse sensors that have different resolutions. Experimental results are compared with three other methods, showing that, while greatly reducing computation time, the proposed framework is able to capture in large extend the spatial correlation to generate equivalent results to the computationally expensive optimal fusion method in covariance form with a Gaussian Process prior

Kernel-specific Gaussian process for predicting pipe wall thickness maps

Data organised in 2.5D such as elevation and thickness maps has been extensively studied in the fields of robotics and geostatistics. These maps are typically a probabilistic 2D grid that stores an estimated value (height or thickness) for each cell. Modelling the spatial dependencies and making inference on new grid locations is a common task that has been addressed using Gaussian random fields. However, inference faraway from the training areas results quite uncertain, therefore not informative enough for some applications. The objective of this re- search is to model the status of a pipeline based on limited and sparse local assessments, predicting the likely condition on pipes that have not been inspected. A customised kernel for Gaussian Processes (GP) is proposed to capture the spatial correlation of the pipe wall thickness data. An estimate of the likely condition of non-inspected pipes is achieved by con-cretising GP to a multivariate Gaussian distribution and generating realisations from the distribution. The performance of this approach is evaluated on various thickness maps from the same pipeline, where data have been obtained by measuring the actual remaining wall thickness. The output of this work aims to serve as the input of a structural analysis for failure risk estimation

Strategies to Improve Information Transfer for Multitrauma Patients.

The aim of this multiphase mixed-method study was to improve access, flow, and consistency of information transfer for multitrauma patients leaving the Emergency Department. Methods included literature review, focus group interviews, chart audits, staff surveys, and a review of international trauma forms to inform an intervention developed with a researcher-led, clinician stakeholder group. Analysis included descriptive and inferential statistics. Baseline data revealed variability existed in patient-care documentation, showing little standardization. Improvement strategies implemented included a gold standard for information embedded in handover tools, raising staff awareness of complexities for information transfer. Improvement was seen in communication between wards coordinating transfer, improved documentation, decreased information duplication, improved legibility, and increased ease and efficiency in navigating to key information. Improvement in communication at patient transition is essential to continuity of safe, effective care, and is impacted by complex interactions between multiple factors. Difficulty increases for patients with high acuity

Raman-scattering study of the InGaN alloy over the whole composition range

We present Raman-scattering measurements on InxGa1−xN over the entire composition range of the alloy. The frequencies of the A1(LO) and E2 modes are reported and show a good agreement with the one-mode behavior dispersion predicted by the modified random-element isodisplacement model. The A1(LO) mode displays a high intensity relative to the E2 mode due to resonant enhancement. For above band-gap excitation, the A1(LO) peak displays frequency shifts as a function of the excitation energy due to selective excitation of regions with different In contents, and strong multiphonon scattering up to 3LO is observed in outgoing resonance conditions

Coupling conditionally independent submaps for large-scale 2.5D mapping with Gaussian Markov Random Fields

© 2017 IEEE. Building large-scale 2.5D maps when spatial correlations are considered can be quite expensive, but there are clear advantages when fusing data. While optimal submapping strategies have been explored previously in covariance-form using Gaussian Process for large-scale mapping, this paper focuses on transferring such concepts into information form. By exploiting the conditional independence property of the Gaussian Markov Random Field (GMRF) models, we propose a submapping approach to build a nearly optimal global 2.5D map. In the proposed approach data is fused by first fitting a GMRF to one sensor dataset; then conditional independent submaps are inferred using this model and updated individually with new data arrives. Finally, the information is propagated from submap to submap to later recover the fully updated map. This is efficiently achieved by exploiting the inherent structure of the GMRF, fusion and propagation all in information form. The key contribution of this paper is the derivation of the algorithm to optimally propagate information through submaps by only updating the common parts between submaps. Our results show the proposed method reduces the computational complexity of the full mapping process while maintaining the accuracy. The performance is evaluated on synthetic data from the Canadian Digital Elevation Data

Patients’ willingness to access cross-border healthcare

European Union (EU) Member States were required to direct their health practices to ensure implementation of ‘Directive on patients’ rights in cross-border healthcare’ which provides the right for EU citizens to seek treatment abroad. This study recruited Maltese patients, consequently it identified and quantified domains constituting willingness to access cross-border healthcare. Via this analytical approach, the results and recommendations were presented to assist cross-border healthcare policy.peer-reviewe