Memory for repeated auditory textures

Even though memory plays a pervasive role in perception, the nature of the memory traces left by past sounds is still largely mysterious. Here, we probed the memory for natural auditory textures. For such stochastic sounds, two types of representations have been put forward: a representation based on sets of temporally local features, or a representation based on time-averaged summary statistics. We synthesized naturalistic texture exemplars and used them in an implicit memory paradigm based on repetition, previously shown to induce rapid learning for artificial sounds such as white noise. Results were similar for artificial and natural sounds, exhibiting a general trend for a decrease in repetition detection performance with increasing exemplar duration, although with some variation depending on texture type. This trend could be captured by a summary statistics model, but also by a new model based on the random sampling of temporally local features. Moreover, repeated exposure to a same natural texture or artificial noise exemplar systematically induced a performance gain, which was comparable across all sound types and exemplar durations. Thus, natural texture exemplars were amenable to learning when repeated exposure was available. The findings are consistent with two interpretations: the existence of a special processing mode when acoustic repetition is involved, to which natural textures are not immune, or a convergence of the local features versus summary statistics descriptions if a continuum of time scales is considered for auditory representations.</p

GKM-OD: Gaussian knowledge based modelling for outlier detection

Outlier detection is a critical process in data engineering. Leveraging machine learning techniques for outlier detection enables the handling of large-scale, high-dimensional data, enhancing detection accuracy and efficiency. Traditional methods typically model data directly in the data space. However, these approaches often struggle to accurately distinguish inliers from outliers when dealing with complex data distributions. GMM can flexibly fit complex, multi-peak distributions using multiple Gaussian components and effectively identify outliers through probabilistic modelling. We introduce a novel outlier detection approach, which improves detection efficiency by indirectly modelling data in a latent space using a Gaussian Mixture Model (GMM).This approach aligns with a growing trend in AI, notably advocated by Yann LeCun, that emphasizes decision-making and learning in latent representation spaces, instead of depending on raw token or feature spaces. For this, we design an encoder-decoder neural network with a GMM as the decision layer, enabling effective identification of outliers through probabilistic modelling. Our method not only addresses practical needs in anomaly detection but also contributes to this broader trend of latent space modelling as a step toward more autonomous and generalisable learning systems.Extensive evaluations on public and proprietary datasets demonstrate that our method outperforms existing approaches, including DAGMM and ECOD, highlighting its superiority in accuracy.<br/

On the border: searching for cometary activity near the Centaur–JFC Transition Line

Current wide-field surveys discover ∼15 Jupiter-family comets (JFCs) each year, typically identified via visual detection of a dust coma or tail. The same surveys also discover many asteroids that have distant JFC-like orbits, but with no reported activity. We observed asteroids on Jupiter-crossing orbits beyond the depth of typical survey imaging using the 2.5 m Isaac Newton Telescope. We used deep imaging to observe 16 asteroids in this region, plus seven known comets for comparison. Three asteroids (2011 WM183, (669525) 2012 XO144, and 2020 RX133) showed surface brightness profiles consistent with low-level activity, equating to ∼19% of our total sample. We note that 2020 RX133 is a Jupiter Trojan. When we considered the heliocentric distance range of the asteroids at the time when they showed activity, this fraction increased to 33% of the targets in the 3.16 au ≤ Rh ≤ 4.56 au region, and therefore it is possible to infer that at least ∼30 asteroids with TJ ≤ 3.05 and in the 4.05 au &lt; a &lt; 5.05 au parameter space may potentially exhibit low-level activity. We also estimated nuclear radii for the three active targets of rn = 1.8 ± 0.2 km, rn ≤ 0.8 km, and rn ≤ 0.5 km for (669525) 2012 XO144, 2011 WM183, and 2020 RX133, respectively. The median color index for the observed asteroids is (g − r)PS1 = 0.52 ± 0.13, aligning with those expected for D-type asteroids

Visualisation of user stories to UML use case diagram

The growing usage of Agile methodology in software development projects among industry professionals (software engineers, system analysts, requirement engineers, etc.) and academia (software engineering students) leads to the need for the implementation of UML diagram for requirements modelling. Use case diagram, an example of UML diagram, is a very powerful tool to model the requirements specified by the users while also helping the development teams understand the functionalities and interactions between users and the system. However, there is a lack of a system or tool that can perform the operation to visualise the use case diagram directly from user stories because generating this diagram manually requires a deep understanding of the requirements and effective communications with stakeholders and it consume lots of time while previous studies which relate to this study are unable to fulfil the relationship elements of use case diagram. This study will introduce a method to visualise the use case diagram from structured textual user stories by utilising Natural Language Processing (NLP) and application of logical rules which will be done in four stages, namely Requirement Gathering, Natural Language Processing, Application of Logical Rules and UML Diagram Generation. A tool named Stanford CoreNLP will be used to perform four techniques of NLP: tokenisation, stemming and lemmatisation, POS tagging and dependency parsing to process the textual user stories, followed by applying the logical rules before generating the use case diagram. This study will propose a method to solve the gap, which is the problem with the generation of relationship elements, while contribute a semi-automated approach to generate a use case diagram from user stories

Debate: Response to ‘Studying the regulatory space of public sector audit—the historical turn’ and the need for local government accounting history

Local governments play a vital role in managing and delivering services that directly impact the lives of citizens. In order to ensure that the allocation of public funds is delivering value for money and local governments are held to account for their decisions, the system requires good, audited accounting information. In the UK, however, local government audit is in crisis and is ‘hampering efforts to ensure that the billions of pounds per year spent by councils is properly accounted’ for (UK Parliament, Citation2023). As of 2025, the picture is no better. For example, the City of Birmingham, Europe’s largest local authority, remains technically bankrupt (Brackley, Citation2025) and, in 2024, the UK’s National Audit Office (NAO) was unable to obtain sufficient evidence from local governments to contribute to the UK’s Whole of Government Accounts for the financial year ending 31 March 2023 (HM Treasury, Citation2024). This resulted in a disclaimed audit opinion—an outcome the NAO described as unprecedented. Given that some form of UK local government audit has existed for centuries, with a more formalized system in place since the 1800s (Chandler, Citation2007; Dewar &amp; Funnell, Citation2016; Midgley et al., Citation2024), these developments raise important historical and institutional questions. While Ferry et al. (Citation2025) note that researchers have long identified issues with UK local government audit and advocate for historical analysis to deepen our understanding of public sector institutions, we argue this historical lens should extend beyond audit. Local government financial management also includes accounting practices—for example financial reporting, budgeting, and management accounting—which shape governance and decision-making. The evolution of these practices over time has influenced today’s challenges in transparency, accountability, and financial sustainability. Therefore, we support the call in Public Money &amp; Management by Ferry et al. (Citation2025) for a historical turn, but suggest expanding it to encompass the broader financial and management accounting practices of local governments. A more comprehensive historical perspective could yield deeper insights into the systemic issues facing local government management today

Understanding the influence of gender, masculinity, and femininity on attitudes and behaviours around meat consumption: comparison of measures to better inform policy action

Meat consumption has important implications for both human and environmental health, and identifying barriers and opportunities that reduce this dietary preference are a key policy target. Numerous studies suggest men and more masculine individuals exhibit higher attachment to meat, which impacts their willingness to reduce its consumption; however, research exploring gender associations concerning masculinity and femininity is limited. Through a survey of 959 individuals, this study investigates the relationships between gender, masculinity, femininity, and dietary behaviours, including meat consumption and willingness to reduce consumption. A novel measure was introduced to assess gender-related traits independently of gender, enabling the effects of both masculinity and femininity on meat-related dietary behaviours to be explored. Men and women expressed masculinity and femininity across the scale, suggesting that men and women can exhibit different levels of both traits, regardless of gender. Men were more likely to consume meat than women, showing a significant gender association which was reflected in the masculinity and combined masculinity and femininity measures. Men and individuals with higher masculinity and masculinity-femininity scores displayed higher meat-eater identities, were more susceptible to social influence, and exhibited lower awareness or acceptance of health and environmental impacts. Men and higher-scoring masculinity-femininity individuals exhibited lower willingness to reduce meat. This result was not significant for masculinity alone, suggesting that incorporating constructs of both femininity and masculinity into a measure may provide a more nuanced understanding of dietary behaviours and attitudes. Policy makers and health professionals should consider how masculinity and gender might influence the acceptance of interventions which aim to change the prevalence of meat in people’s diets.<br/

Immune cell subsets in young kidney transplant recipients: mechanistic and clinical perspectives

Kidney transplantation provides the best survival advantage for children, adolescents, and young adults with end-stage kidney disease, yet this group paradoxically experiences the poorest long-term graft survival. Immune-mediated rejection is the predominant cause, but the cellular mechanisms that underpin this age-related disparity remain incompletely defined. This review synthesises current evidence on the impact of immune ageing across adaptive and innate compartments, focusing on T cells, B cells, and natural killer (NK) cells. In younger recipients, a large naïve T- and B-cell pool, robust thymic output, and efficient germinal centre activity confer heightened alloimmune reactivity, driving increased risk of acute cellular and antibody-mediated rejection. In contrast, older recipients exhibit features of immunosenescence, including loss of CD28 expression, accumulation of terminally differentiated effector subsets, impaired germinal centre responses, and attenuated NK cytotoxicity, resulting in diminished capacity to mount de novo responses but greater vulnerability to infection. These immune trajectories have direct clinical implications: younger recipients may require intensified, mechanism-targeted immunosuppression, whereas older recipients may be more amenable to minimisation or tolerance protocols. We further highlight emerging evidence for premature immunosenescence in paediatric dialysis populations, the contribution of age-associated B cells and NK subsets, and the role of immunophenotype-guided therapeutic strategies. Current uniform immunosuppression protocols inadequately account for developmental and age-related immune heterogeneity. We argue for an age- and immune phenotype–informed approach to therapy, integrating longitudinal immune profiling, biomarker development, and systems immunology to improve risk stratification, promote tolerance, and ultimately extend allograft survival across all age groups.<br/

Modelling of interfacial morphology formation driven by thermal and hydrodynamic instabilities in injection overmoulding of fibre reinforced polymer composites

Composite injection overmoulding offers a cost-effective and repeatable method for manufacturing complex composite structures. However, accurately predicting the strength of the interface between subcomponents remains a significant challenge, particularly for load-bearing applications. This difficulty mainly arises from the multiscale nature of interface formation. This study presents a combined numerical and experimental investigation into the formation of a complex interface morphology at the microscale. A multiscale CFD framework was developed to simulate transient heat and momentum transfer in a two-component composite system, composed of polyetheretherketone (PEEK) overmoulded onto a carbon fabric reinforced polyphenylene sulfide (PPS) laminate. Two interface configurations, differentiated by surface resin depth, are examined. The simulations reproduce key morphological patterns observed at the microscale, which are shown to arise from hydrodynamic instabilities, including Kelvin–Helmholtz and Richtmyer–Meshkov. The numerical results are validated against scanning electron microscopy (SEM) observations, providing a link between interfacial transport phenomena and the resulting structural morphology. The study demonstrates how local thermal gradients and shear-induced effects contribute to resin penetration and surface patterning at the interface in addition to local temperature. As interface temperature is a critical factor in healing-based models of interface strength, these findings underscore the potential of microscale simulations to predict temperature profiles and explain the formation of weld-line features. These predictions ultimately inform the design of stronger, more reliable overmoulded composites and are also of interest for researchers working on thermal–fluid transport, multiphase polymer flows, and interface dynamics in advanced manufacturing contexts.<br/

A unified predefined-time hybrid control framework for swing-stance stability in legged locomotion

This article presents a hierarchical control framework that integrates predefined-time convergence and adaptive compliance control to achieve robust and stable quadruped locomotion over complex and irregular terrains. The proposed framework addresses the challenges posed by terrain uncertainties and dynamic interactions during locomotion. Specifically, a predefined-time nonsingular fast terminal sliding mode controller (PTNFTSMC) is developed for the swing phase to ensure fast and accurate trajectory tracking within a predefined time bound, while avoiding singularities and being independent of initial conditions. For the stance phase, a disturbance-aware adaptive impedance controller is proposed, which enables real-time stiffness estimation and damping adjustment based on variations in contact forces, thereby improving ground contact stability and enhancing terrain adaptability. A unified Lyapunov-based theoretical framework is employed to guarantee global predefined-time stability across all gait phases, ensuring reliable performance even in uncertain environments. The effectiveness and robustness of the proposed control strategy are validated through extensive simulations and real-world hardware experiments. The results demonstrate that the proposed approach significantly improves tracking accuracy, reduces convergence time, and enhances the quadruped robot's ability to maintain stable locomotion under various terrain conditions and external disturbances.<br/