Radegund of Poitiers (520–587) was a princess of the Thuringian kingdom, wife to the Merovingian king Clothar I, and ultimately domina of the abbey of Sainte-Croix in Poitiers. The literary persona of Saint Radegund, as constructed by the poet-hagiographer Venantius Fortunatus and, a few years later, by the nun Baudonivia, underpins the historical figure. The saint exerted a significant cultural influence across Frankish territories, and over the ages her image has been continuously received, reinterpreted, and expanded. The purpose of this study is to provide a survey of the critical reception of Radegund’s character, in order to explore how modern scholarship has interpreted and reimagined her persona over time.
Abstract Higher Education-Based Teacher Educators (TEs) are responsible for the preparation of future teachers across the continuum of education. However, despite their significant role in the education ecosystem, their well-being and professional satisfaction often remain overlooked in research and policy. For example, while burnout among academics is extensively studied, it remains under-researched, particularly among TEs. Even less attention is paid to rustout, a phenomenon characterised by professional underutilisation, intellectual stagnation and unfulfillment. Rustout is not a universal experience. However, its presence acknowledges that occupational stress is non-linear and nuanced and that it can vary depending on organisational and personal resources. Like its better-known counterpart, burnout, untreated rustout can have individual and organisational consequences, such as poor mental health, career dissatisfaction and accelerated employee turnover. Through an analysis of surveys and interviews with TEs across Ireland and the United Kingdom (UK), we explore the factors that may contribute to rustout. Guided by rustout literature and validated through collaborative reflection, this paper reveals three core themes: (1) administrative overload and erosion of autonomy, (2) misalignment between professional aspirations and job tasks and (3) systemic barriers to professional growth. Some participants reported being ‘prevented from thriving’, while others actively sought ways to mitigate rustout through new challenges or external opportunities. More broadly, the study shines a light on the ‘silence’ surrounding rustout in academia. The findings also highlight the detrimental effects of rustout on individual well-being and suggest that it is not merely a pre-retirement phenomenon but can emerge at various stages of a TE’s career. Practical implications emphasise the need for Higher Education (HE) sectors and leaders to put ‘rustout’ on the mental health literacy agenda, to balance job demands with resources and to acknowledge the trade-off that can occur when operational efficiency is prioritised over professional well-being.
History of scholarship and learning. The humanities, Social Sciences
Alleviating rural poverty plays a critical role in achieving comprehensive rural revitalization. This study selects typical villages in karst rocky desertification poverty-stricken mountainous areas as the research objects and applies fuzzy-set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA). Using an element–structure–function framework, this paper explores the driving mechanism of the multi-factor linkage of rural regional systems and the revitalization of poverty-stricken villages. The results show that single antecedent conditions are not necessary conditions for the rural revitalization of poverty-stricken villages. There are seven distinct pathways to achieve the revitalization of poverty-stricken villages, which are categorized into function–structure driven types, function–element driven types, and function–structure–element driven types. Each type has different configurations of influencing factors. This study uncovers the complex interaction mechanism among multiple factors behind the rural revitalization of poverty-stricken villages from a systematic perspective, offering insights to guide targeted local policy development.
History of scholarship and learning. The humanities, Social Sciences
Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been used to predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air quality monitoring stations (AQMSs) have less observed data in quantity, and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pre-trained neural networks have been employed to assist AQMSs with less observed data to build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12-year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledge-based system. As shown by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorter training time than randomly initialized recurrent neural networks.
On a centennial timescale, solar activity was quantified based on records of instrumental sunspot observations. This article briefly discusses several aspects of the recent archival investigations of historical sunspot records in the 17th to 18th centuries. This article also reviews the recent updates for the active day fraction and positions of the reported sunspot groups of the Maunder Minimum to show their significance within the observational history. These archival investigations serve as base datasets for reconstructing solar activity.
Canadian author L.M. Montgomery did not set out to write stories about romance. As she indicated in her journals, she wrote character-driven stories of young girls navigating their way through girlhood. However, she understood that the public, and her publishers, expected these girls to experience romance that culminated in marriage, following the societal traditions of the time. Montgomery managed this dichotomy by having many characters experience a suspended romance, delaying the romantic aspect of the relationship for as long as possible. Arts-based practice is a mode of analysis and offers the opportunity to find a new way of understanding and communicating Montgomery’s type of suspended romance. Music is, in many ways, considered romantic, so it is an appropriate medium to communicate Montgomery’s romantic narrative structures. This paper investigates Montgomery’s use of suspended romance in her novels and how this delay provided her characters with time to develop other areas of their lives. An arts-based methodology was used to identify and analyse recurring themes in Montgomery’s work, as the question is not can Montgomery’s theme of romance be musically represented but how. The result of this creative experimentation is a new musical composition that articulates these suspended romances using six different musical devices. This creative work exemplifies the intertextual link that exists between Montgomery’s work and new musical compositions.
Yusro Edy Nugroho, Sahid Teguh Widodo, Wasino
et al.
Colonialism has turned the gender relations in Java into something considerably complex. Through marriage, women were positioned to promote the harmonization of strategic politics of kingdoms. Such a condition results in many studies on several Javanese literary works of Wulang Putri. This research investigates the socio- historical background of the writing of Wulang Putri in the context of the hegemony of Javanese kingdom power. A sociological, literary work in the Gramscian hegemony theory was applied in this study. All data comprised nine literary works of Wulang Putri written in the nineteenth century. The result showed the effect of the literary works in instilling the political influence of the author through a cultural discourse. Such is seen in the post-Java War demilitarization (1825–1830) to maintain the integrity of the kingdom. In addition, there are still traces of efforts to unite the Mataram dynasty through marriages between princes and princesses in four palaces.
The processes and supports within an institution can, I've noticed, demand a bit of effort to change. When we speak of open scholarship or open science, many aspects tie-in or lead out from those concepts, which makes the whole prospect of institutional change quite complex.
The ITExpo conference was held in Fort Lauderdale, Florida in February 2023. The IoT Evolution program is part of the overall ITExpo conference and focuses on emerging trends and opportunities within the Internet of Things (IoT) environment. IoT Evolution offers expert sessions on practical applications and use cases of IoT. These include automation, security, and healthcare (https://www.iotevolutionexpo.com/east/). One of the 2023 conference sessions was entitled “Medical Internet of Things (MIoT) – Opportunities for Managed Solutions Providers (MSPs). This article is from the presenter’s perspective and addresses the topics covered in the doctoral dissertation research completed in 2022. The focus of the session was to provide information to organizations that offer managed solutions services to clients or customers, including healthcare. One of the observations derived from the doctoral research was a lack of awareness within the healthcare community regarding the security and privacy issues associated with remote implantable or wearable medical devices. Doctors presumed that these issues were addressed by the device manufacturers, HIPAA, the FDA, or others. Research indicated that this was not correct and that there was a gap in this area. This gap represented an opportunity for organizations like MSPs that provide consulting and advisory services to healthcare organizations regarding overall security and privacy. The article elaborates on the composition of the attendees, questions that arose during the session, and summarizes the information that was provided. The linkage between academic research and practical field application were key elements of this session.
History of scholarship and learning. The humanities
Abstract This study examines whether and how Sharia compliance and national governance affect the value of corporate cash holding (cash) in Organization of Islamic Cooperation (OIC) countries. Study results indicate that cash can enhance firm value and such cash value is higher for Sharia-compliant firms than for Sharia non-compliant firms. In addition, cash is particularly valuable when national governance is strong. Furthermore, the positive effect of Sharia compliance on cash value is more pronounced when national governance is strong. Results suggest that internal governance (i.e., Sharia compliance) and external governance (i.e., national governance) should be in sync to maximize cash value.
History of scholarship and learning. The humanities, Social Sciences
Abstract News media plays a vital role in communicating scientific evidence to the public during the COVID-19 pandemic. Such communication is important for convincing the public to follow social distancing guidelines and to respond to health campaigns such as vaccination programmes. However, newspapers were criticised that they focus on the socio-political perspective of science, without explaining the nature of scientific works behind the government’s decisions. This paper examines the connections of the nature of science categories in the COVID-19 era by four local newspapers in the United Kingdom between November 2021 to February 2022. Nature of science refers to different aspects of how science works such as aims, values, methods and social institutions of science. Considering the news media may mediate public information and perception of scientific stories, it is relevant to ask how the various British newspapers covered aspects of science during the pandemic. In the period explored, Omicron variant was initially a variant of concern, and an increasing number of scientific evidence showed that the less severity of this variant might move the country from pandemic to endemic. We explored how news articles communicate public health information by addressing how science works during the period when Omicron variants surge. A novel discourse analysis approach, epistemic network analysis is used to characterise the frequency of connections of categories of the nature of science. The connection between political factors and the professional activities of scientists, as well as that with scientific practices are more apparent in left-populated and centralist outlets than in right-populated news outlets. Among four news outlets across the political spectrum, a left-populated newspaper, the Guardian, is not consistent in representing relations of different aspects of the nature of scientific works across different stages of the public health crisis. Inconsistency of addressing aspects of scientific works and a downplay of the cognitive-epistemic nature of scientific works likely lead to failure in trust and consumption of scientific knowledge by the public in the healthcare crisis.
History of scholarship and learning. The humanities, Social Sciences
Sajeda Pervin, Mohammad Nazari Ismail, Abu Hanifa Md Noman
The role played by microfinancing in the empowerment of women has been a subject of debate in recent literature. By employing the case study method, this paper explores the influencing determinants. Data was obtained from a field interview comprised of 20 female microfinance borrowers and their male family members, as well as focus group discussions and participant observation. The economic, social, and political empowerment of the interviewed women were assessed using Kabeer’s empowerment framework, and the results reveal that microfinancing does not influence the economic and political dimensions of female empowerment; instead, microfinancing was observed to empower women socially, particularly with respect to their participation in major decision-making. The results further indicate that the age, family type, educational level, financial literacy, and training of female microfinance clients play important roles in their empowerment.
History of scholarship and learning. The humanities, Social Sciences
Sigrid Passano Hellan, Christopher G. Lucas, Nigel H. Goddard
Transfer learning for Bayesian optimisation has generally assumed a strong similarity between optimisation tasks, with at least a subset having similar optimal inputs. This assumption can reduce computational costs, but it is violated in a wide range of optimisation problems where transfer learning may nonetheless be useful. We replace this assumption with a weaker one only requiring the shape of the optimisation landscape to be similar, and analyse the recent method Prior Learning for Bayesian Optimisation - PLeBO - in this setting. By learning priors for the hyperparameters of the Gaussian process surrogate model we can better approximate the underlying function, especially for few function evaluations. We validate the learned priors and compare to a breadth of transfer learning approaches, using synthetic data and a recent air pollution optimisation problem as benchmarks. We show that PLeBO and prior transfer find good inputs in fewer evaluations.
Estudos sobre o processamento da leitura bilíngue/multilíngue têm evidenciado de forma robusta que o acesso lexical é não seletivo, havendo uma busca paralela em todos os subsistemas linguísticos. Esse efeito tem sido replicado nas pesquisas mais recentes nas quais são apresentadas sentenças aos participantes. O contexto de sentença também mostra que pode haver coativação sintática, de modo que as representações sintáticas de uma língua possam facilitar o processamento de estruturas semelhantes na outra língua. De forma geral, tais as investigações incluem línguas hegemônicas e majoritárias. Diante disso, o objetivo deste estudo foi investigar o acesso lexical durante o processamento de sentenças em alemão padrão por falantes de hunsriqueano, língua de imigração alemã falada no Brasil. Foi aplicada uma Tarefa de compreensão de sentenças em alemão. Dois grupos participaram da pesquisa: um composto por falantes de hunsriqueano, o outro por não falantes de qualquer língua minoritária de origem alemã. Todos os participantes estudam alemão como língua estrangeira. Os resultados mostram um efeito do compartilhamento de representações semânticas, fonológicas (acesso lexical não seletivo) e sintáticas (coativação sintática) entre hunsriqueano e alemão no processamento de sentenças em alemão. Por meio dos resultados, visamos contribuir com a pesquisa em línguas minoritárias e processamento da leitura, uma relação nem sempre trivial.
History of scholarship and learning. The humanities, Philology. Linguistics
Jonas Rothfuss, Christopher Koenig, Alisa Rupenyan
et al.
In robotics, optimizing controller parameters under safety constraints is an important challenge. Safe Bayesian optimization (BO) quantifies uncertainty in the objective and constraints to safely guide exploration in such settings. Hand-designing a suitable probabilistic model can be challenging, however. In the presence of unknown safety constraints, it is crucial to choose reliable model hyper-parameters to avoid safety violations. Here, we propose a data-driven approach to this problem by meta-learning priors for safe BO from offline data. We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner via empirical uncertainty metrics and a frontier search algorithm. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches while maintaining safety.
In this position paper, we study interactive learning for structured output spaces, with a focus on active learning, in which labels are unknown and must be acquired, and on skeptical learning, in which the labels are noisy and may need relabeling. These scenarios require expressive models that guarantee reliable and efficient computation of probabilistic quantities to measure uncertainty. We identify conditions under which a class of probabilistic models -- which we denote CRISPs -- meet all of these conditions, thus delivering tractable computation of the above quantities while preserving expressiveness. Building on prior work on tractable probabilistic circuits, we illustrate how CRISPs enable robust and efficient active and skeptical learning in large structured output spaces.
This study aims to explore the harmonisation of scientific specialisation for undergraduate science students using multiple intelligences (MI), their relationship to academic achievement (GPA) and the students’ attitudes towards science. The sample consists of 198 male and female students chosen randomly from different year groups in the departments of physics and chemistry at Al-Qunfudah College at Umm Al-Qura University in Saudi Arabia. The study used a tool to survey MI and a questionnaire to measure the sample’s attitudes towards science. The researcher obtained the students’ GPAs from the college administration department. The results showed that the ranking of intelligences for the sample, respectively, was existential, logical, interpersonal, kinaesthetic, naturalistic, visual, intrapersonal, linguistic and musical. There was consistency between the levels of students’ MI with their science specialisation. There was no significant correlation between the levels of study, GPA variables and attitudes towards science. There was a significant and positive increasing correlation between GPA and each of the following MI: logical, intrapersonal and existential. There was a significant difference between attitudes towards science in favour of chemistry, a significant difference between the medians of existential intelligence in females and a significant and positive increasing correlation between the attitudes towards science and existential intelligence.
History of scholarship and learning. The humanities, Social Sciences
Samantha Biegel, Rafah El-Khatib, Luiz Otavio Vilas Boas Oliveira
et al.
The availability of labelled data is one of the main limitations in machine learning. We can alleviate this using weak supervision: a framework that uses expert-defined rules $\boldsymbolλ$ to estimate probabilistic labels $p(y|\boldsymbolλ)$ for the entire data set. These rules, however, are dependent on what experts know about the problem, and hence may be inaccurate or may fail to capture important parts of the problem-space. To mitigate this, we propose Active WeaSuL: an approach that incorporates active learning into weak supervision. In Active WeaSuL, experts do not only define rules, but they also iteratively provide the true label for a small set of points where the weak supervision model is most likely to be mistaken, which are then used to better estimate the probabilistic labels. In this way, the weak labels provide a warm start, which active learning then improves upon. We make two contributions: 1) a modification of the weak supervision loss function, such that the expert-labelled data inform and improve the combination of weak labels; and 2) the maxKL divergence sampling strategy, which determines for which data points expert labelling is most beneficial. Our experiments show that when the budget for labelling data is limited (e.g. $\leq 60$ data points), Active WeaSuL outperforms weak supervision, active learning, and competing strategies, with only a handful of labelled data points. This makes Active WeaSuL ideal for situations where obtaining labelled data is difficult.
Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi
et al.
We consider the offline reinforcement learning problem, where the aim is to learn a decision making policy from logged data. Offline RL -- particularly when coupled with (value) function approximation to allow for generalization in large or continuous state spaces -- is becoming increasingly relevant in practice, because it avoids costly and time-consuming online data collection and is well suited to safety-critical domains. Existing sample complexity guarantees for offline value function approximation methods typically require both (1) distributional assumptions (i.e., good coverage) and (2) representational assumptions (i.e., ability to represent some or all $Q$-value functions) stronger than what is required for supervised learning. However, the necessity of these conditions and the fundamental limits of offline RL are not well understood in spite of decades of research. This led Chen and Jiang (2019) to conjecture that concentrability (the most standard notion of coverage) and realizability (the weakest representation condition) alone are not sufficient for sample-efficient offline RL. We resolve this conjecture in the positive by proving that in general, even if both concentrability and realizability are satisfied, any algorithm requires sample complexity polynomial in the size of the state space to learn a non-trivial policy. Our results show that sample-efficient offline reinforcement learning requires either restrictive coverage conditions or representation conditions that go beyond supervised learning, and highlight a phenomenon called over-coverage which serves as a fundamental barrier for offline value function approximation methods. A consequence of our results for reinforcement learning with linear function approximation is that the separation between online and offline RL can be arbitrarily large, even in constant dimension.
Many practical applications of reinforcement learning require agents to learn from sparse and delayed rewards. It challenges the ability of agents to attribute their actions to future outcomes. In this paper, we consider the problem formulation of episodic reinforcement learning with trajectory feedback. It refers to an extreme delay of reward signals, in which the agent can only obtain one reward signal at the end of each trajectory. A popular paradigm for this problem setting is learning with a designed auxiliary dense reward function, namely proxy reward, instead of sparse environmental signals. Based on this framework, this paper proposes a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning. We establish a surrogate problem by Monte-Carlo sampling that scales up least-squares-based reward redistribution to long-horizon problems. We analyze our surrogate loss function by connection with existing methods in the literature, which illustrates the algorithmic properties of our approach. In experiments, we extensively evaluate our proposed method on a variety of benchmark tasks with episodic rewards and demonstrate substantial improvement over baseline algorithms.