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Distant relations:The Palatine family, propaganda and print in early Stuart Britain
Get PDFThe marriage of Elizabeth Stuart to Frederick V, Elector Palatine, in February 1613, offered a lavish counter-attraction to the solemn mourning which had accompanied the death of her brother, Henry, Prince of Wales, just a few months before. Amidst dynastic rupture, the match fortuitously shifted focus onto the future of the line, rather than its recent loss. Indeed, following the birth of Prince Frederick Henry in 1614, and for the next sixteen years, until the birth of a healthy son to King Charles I and his consort, Queen Henrietta Maria, the ever-growing Palatine brood represented the next stage in the Stuart succession. Separated by distance from their potential subjects, it was important that the electoral couple continued to cultivate ties with the British people and, as their family grew, a familiarity with the prospective royal line was encouraged.A steady stream of printed images was enlisted in this campaign, designed to introduce the public back home to the Palatine family. Tracing the development of their visual portrayal in line with the shifting fortunes of the royal couple and their children, this article explores how these representations sought to naturalise a foreign royal family, forging cross-European dynastic loyalties. Whether portrayed as lineal standby or even alternative, images of Elizabeth’s family might endorse or, indeed, complicate dynastic rhetorics. As I shall argue, the affective bonds encouraged in word and image actually risked splitting Stuart allegiances
Towards a deep learning approach for short-term data-driven spatiotemporal seismicity rate forecasting
Get PDFRecent advances in earthquake monitoring have led to the development of methods for the automatic generation of high-resolution catalogues. These catalogues are created at considerably reduced processing times and contain significantly larger volumes of data concerning seismic activity compared to standard catalogues created by human analysts. Disciplinary statistics and physics-based earthquake forecasting models have shown improved performance when rich catalogues are used. The use of high-resolution catalogues paired with machine learning algorithms, which have recently evolved due to the rise in the availability of data and computational power, is therefore a promising approach to uncovering underlying patterns and hidden laws within earthquake sequences. This study focuses on the development of short-term data-driven spatiotemporal seismicity forecasting models with the help of deep learning and tests the hypothesis that deep neural networks can uncover complex patterns within earthquake catalogues. The performance of the forecasting models is assessed using metrics from the data science and earthquake forecasting communities. The results show that deep learning algorithms are a promising solution for generating short-term seismicity forecasts, provided that they are trained on a representative dataset that accurately captures the properties of earthquake sequences. Comparisons of machine learning-based forecasting models with an epidemic-type aftershock sequence benchmark show that both types of models outperform the persistence null hypothesis commonly used as a benchmark in forecasting the behaviour of other types of non-linear systems. Machine learning forecasting models achieve similar performance to that of an epidemic-type aftershock sequence benchmark on the Southern California and Italy test datasets at significantly reduced processing times - a major advantage in applications to short-term operational earthquake forecasting
Pairwise evaluation of accent similarity in speech synthesis
Get PDFMotivated by the social and moral imperative for more inclusive speech technology, the community has witnessed a growing interest in systems capable of generating high-fidelity accent, across various speech generation tasks. In ZeroShot Text-to-Speech (ZS-TTS), accent hallucination/mismatch, where the generated speech deviates from the reference speech in accent, is reported and addressed in [1]. In Accented TTS, numerous attempts have been made to generate high-fidelity accent based on pre-defined accent variety labels or intensity levels [2, 3]. In Accent Conversion (AC), numerous attempts have been made to map speech from foreign to native accent, preserving content and speaker information while removing the foreign accent in the source [4]. However, how to evaluate accent similarity in speech is under-researched and lacks consensus
Toward a typology of boundaries in crisis management
Get PDFWhy do so many organizations fail to respond to crises effectively and what can be done to improve their crisis response? We aim to address these questions by developing a typology of boundaries that can help us understand and better prepare for future crises. Building on the existing literature on crisis management and boundaries, our typology seeks to address the critique of current crisis management models, such as these being too prescriptive and putting too much emphasis on assumptions that only the decisions made by the crisis management team (i.e., centralized decisions) will impact the crisis response. We address this critique by elaborating four categories of boundaries that are relevant to manage crisis response, such as physical, mental, social, and temporal. Examples of these boundaries could include access to properties (physical), interpreting whether a crisis is happening (mental), shifting relationship dynamics (social), and the urgency of response time to a crisis (temporal). Our typology offers a lens to consider crisis management by taking into consideration the complexities and nuances that linear crisis management models have not yet adequately addressed. We argue that by identifying, defining, and understanding different configurations of boundaries one can better manage crises towards a desired outcome
Preliminary investigation of equine veterinary hospital staff attitudes towards pain assessment in a single centre:Staff attitudes toward equine pain assessment
Get PDFDespite the availability of several equine pain assessment tools, their use in equine veterinary practice appears limited compared to small animal practice. This study explores potential barriers to equine pain assessment, as reported by staff at a single UK equine teaching hospital. MethodsNine hospital staff were interviewed using semi-structured interviews. Key themes were identified through reflexive thematic analysis.ResultsParticipants acknowledged the importance of pain assessment yet highlighted limitations in current methods and their inconsistent use. Key challenges included limited observer confidence, subjective interpretations and discrepancies between staff and owner perceptions. Variability in horse temperament and pain presentation further complicated assessment. Staff expressed a desire for improvements in pain assessment tools and clearer protocols.LimitationsThe study was limited by its single-hospital design, short interview duration and small sample size. ConclusionThe study highlights the complexity of equine pain assessment in clinical practice, including tool limitations, knowledge gaps and contextual barriers. Despite valuing pain assessment, staff reported difficulties applying currently available methods. Findings point to a need for improved tools, training and institutional support.<br/
EDeformNet:Estimating fishing net deformations from sparse observations
Get PDFThis paper introduces EDeformNet, a novel method for real-time 3D reconstruction of fishing nets using sparse positional measurements. Currently, net deployment during large-scale fishing operations is challenging as the submerged lattice deformations that occur in response to the various environmental factors are not visible to the vessel operator. EDeformNet extends Embedded Deformation Graphs (EDGs), a commonly used technique in template-based nonrigid 3D reconstruction that allows control of embedded spaces through sparse control point correspondences. These can be suitably derived from acoustic tracking beacons attached to the net. EDeformNet enhances the standard EDG optimization scheme by including constraints that preserve surface normals at control points and guard distances between vertices in the template mesh. These improvements are proven to enable an accurate representation of the complex deformations and movements typical in purse seine nets, the fishing technique where the algorithm has been tested, which standard EDG is unable to attain. Moreover, EDeformNet also proposes a tailored strategy that dynamically adjusts the net template according to the known length of the deployed portion of the fishing net. This approach reconstructs exclusively the submerged portion of the fishing net, avoiding extraneous data from above-water sections and enhancing accuracy under realistic fishing conditions. The proposed method is validated using realistic 3D physics simulations in Blender, where quantifiable comparisons demonstrate that EDeformNet effectively captures the spatial dynamics of purse-seining. Compared to standard EDG, EDeformNet achieves superior performance, resulting in at least a 25% improvement across the array of challenging temporal scenarios studied
PL-VIWO:A lightweight and robust point-line monocular visual inertial wheel odometry
Get PDFThis paper presents a novel tightly coupled Filter-based monocular visual-inertial-wheel odometry (VIWO) system for ground robots, designed to deliver accurate and robust localization in long-term complex outdoor navigation scenarios. As an external sensor, the camera enhances localization performance by introducing visual constraints. However, obtaining a sufficient number of effective visual features is often challenging, particularly in dynamic or low-texture environments. To address this issue, we incorporate the line features for additional geometric constraints. Unlike traditional approaches that treat point and line features independently, our method exploits the geometric relationships between points and lines in 2D images, enabling fast and robust line matching and triangulation. Additionally, we introduce Motion Consistency Check (MCC) to filter out potential dynamic points, ensuring the effectiveness of point feature updates. The proposed system was evaluated on publicly available datasets and benchmarked against state-of-the-art methods. Experimental results demonstrate superior performance in terms of accuracy, robustness, and efficiency. The source code is publicly available at: https://github.com/Happy-ZZX/PL-VIWO
Self-supervised 3D reconstruction of tibia and fibula from biplanar X-Rays
Get PDFWith the growing number of patients experiencing knee-related conditions, total knee arthroplasty (TKA) has become a common procedure, where a 3D visualisation of the patient’s tibia and fibula is essential for preoperative planning. Traditional imaging techniques, such as computed tomography (CT), often expose patients to high levels of radiation or impose significant financial costs. As an alternative, this paper proposes a novel approach that reconstructs a 3D model of the tibia and fibula using only two X-ray images (taken from the coronal and sagittal planes) and a general template, significantly reducing radiation exposure and financial burden. Our algorithm of 3D reconstruction for patient-specific anatomies combines point-based deformation with deep learning techniques. Initially, the general model undergoes a preliminary deformation to match the patient tibia and fibula dimensions. This pre-deformed model then serves as a template, followed by a fine deformation process via a self-supervised graph convolutional network (GCN), whose parameters are trained iteratively by comparing the template projection and the X-ray measurements. Following tests in simulations, cadaver experiments, and in-vivo experiments, our proposed algorithm demonstrates state-of-the-art accuracy and exceptional robustness across different evaluation metrics. Our code is available at https://github.com/DrKaiPan/tfDeform_GCN.gi
Adaptive Vessel Navigation for Purse Seine Fishing Net Deployment
Get PDFThis paper presents an innovative framework for adaptive navigation of purse-seine vessels during the deployment of fishing nets. Firstly, the framework leverages geometry based real-time fish net shape estimation using sparse acoustic and GPS positioning sensors embedded within the net. This shape estimation is then utilized to predict the optimal path for vessel navigation, ensuring efficient and precise net deployment. By integrating real-time observations and graphical optimization techniques, the proposed method addresses practical challenges in uncertain marine environments such as adapting the navigation path plan to the inherent variability in fish school behaviour and ocean currents. The approach is validated through simulated fishing net deformation scenarios with Blender software, demonstrating its capability to maintain the operational efficiency and adaptation to environmental uncertainties
LLMs reproduce stereotypes of sexual and gender minorities
Get PDFA large body of research has found substantial gender bias in NLP systems. Most of this research takes a binary, essentialist view of gender: limiting its variation to the categories men and women, conflating gender with sex, and ignoring different sexual identities. But gender and sexuality exist on a spectrum, so in this paper we study the biases of large language models (LLMs) towards sexual and gender minorities beyond binary categories. Grounding our study in a widely used social psychology model—the Stereotype Content Model—we demonstrate that English-language survey questions about social perceptions elicit more negative stereotypes of sexual and gender minorities from both humans and LLMs. We then extend this framework to a more realistic use case: text generation. Our analysis shows that LLMs generate stereotyped representations of sexual and gender minorities in this setting, showing that they amplify representational harms in creative writing, a widely advertised use for LLMs
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