Greater London Authority

The estimation of a mixture of real-and integer-valued parameter vectors appears in different control and signal processing applications. In some particular cases, for instance when dealing with covariance or rotation matrices, the real parameter must be constrained by geometrical properties. This article considers the general estimation problem containing both Lie group and integer parameters, for which the corresponding performance bound is a important missing tool. Leveraging recent results on the Cramer-Rao bound (CRB) for a mixture of real/integer parameters and the CRB for matrix Lie groups, the aim is twofold: To derive new performance lower bounds (McAulay-Seidman bound and CRB) on the Lie group-integer space, and to obtain a closed-form CRB expression for multiantenna Global Navigation Satellite Systems attitude estimation, where observations depend on both an integer carrier phase ambiguity vector and a rotation matrix lying on the Lie group SO(3). The proposed bound is validated through numerical simulations in a realistic scenario to support the discussion.

Background: Being not in education, employment, or training (NEET) is associated with poorer educational, health and socio-economic outcomes, yet why young people become NEET, how they exit NEET status and who remains at risk are poorly understood. In England, gaps in the evidence base are compounded by a fragmented data infrastructure. Methods: We systematically reviewed the academic (Ovid, EBSCOhost, Scopus, Web of science, British Education Index, EconLit and Google scholar) and grey literature databases (Office for National Statistics, Administrative Dara Research, Closer, Department for Work and Pensions, Department for Education, and UK Data Service) from 1990 to 2026, identifying 861 records, of which 36 datasets met the inclusion criteria of data sources collecting data from young people aged 16-24 in England; published, collected or updated from 2000 onwards; sources where NEET status can be derived or is directly measured; administrative data and longitudinal surveys and grey literature. We then analysed and mapped our datasets across five categories: prevalence and monitoring; profiling; trajectories; explanatory and outcome. Results: No dataset combined comprehensive population coverage, robust NEET measurement, longitudinal follow-up, and sufficient domain breadth to investigate NEET entry, persistence and trajectories. England therefore has substantial capacity to monitor the prevalence of NEET status, reasonably accurately, but considerably less capacity to profile who becomes NEET and when, understand trajectories into and out of NEET status, or investigate the factors associated with persistence. Conclusions: This limitation constrains research into the mechanisms underlying NEET status and the longer-term consequences of disengagement. Strategic linkage of complementary datasets, including underused administrative sources and major longitudinal birth cohorts, could substantially strengthen the evidence base for understanding NEET trajectories and informing prevention. Data linkage alone, however, will not be sufficient. Understanding factors associated with why young people become and remain NEET will require a data infrastructure designed to capture their trajectories, circumstances and outcomes across the life course.

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderstorms that occur on the sub-resolution scale. Hybrid methods combining physics with machine learning (ML) offer faster, higher fidelity climate simulations by outsourcing compute-hungry, high-resolution simulations to ML emulators. However, these hybrid physics-ML simulations require domain-specific data and workflows that have been inaccessible to many ML experts. This paper is an extended version of our NeurIPS award-winning ClimSim dataset paper (Yu et al., 2024). The ClimSim dataset includes 5.7 billion pairs of multivariate input/output vectors spanning ten years at high temporal resolution, capturing the influence of high-resolution, high-fidelity physics on a host climate simulator's macro-scale state. In this extended version, we introduce a significant new contribution in Section 5, which provides a cross-platform, containerized pipeline to integrate ML models into operational climate simulators for hybrid testing. We also implement various baselines of ML models and hybrid simulators to highlight the ML challenges of building stable, skillful emulators.

The Hera mission of the European Space Agency was launched successfully on October 7, 2024 and will perform the first rendezvous with a binary asteroid in fall 2026. It will measure in great detail the characteristics of the binary asteroid (65803) Didymos. This will include for the first time the interior of an asteroid, as well as the outcome of the impact of the NASA DART mission on the small moon, called Dimorphos; of the binary system. The first asteroid deflection test will thus be fully validated, enabling impact model extrapolations to other cases. Hera uses a unique architecture that includes for the first time a main spacecraft and two cubesats for deep space asteroid exploration. It takes place in the context of the golden age of asteroid exploration, with no less than 8 missions in development or already flying to asteroids and great successes of past missions, in particular the two recent asteroid sample return missions OSIRIS-REx by NASA and Hayabusa2 by JAXA. Up to now, all new asteroids visited by a spacecraft have generated great surprises, especially regarding their often counter-intuitive response to external actions, showing that we are still far from fully understanding the properties of these bodies in their low-gravity environment. By investigating and interacting with small asteroids, we should eventually be able to better understand and predict their properties as a function of common characteristics identified by ground-based observations. We are not there yet. In this paper, we present how Hera will contribute to this endeavor.

The moral panic surrounding trans, non-binary, and gender diverse (TGD) lives in the United Kingdom (UK) has been incited by high-level political and government actors and exacerbated by pervasive misinformation in social and press media. This hostile environment contributes to increasing interpersonal violence and social exclusion experienced by TGD people. Structural and interpersonal discrimination—conceptualised as minority stress—is understood to elicit physiological and psychological stress responses that predispose TGD individuals to a range of adverse health outcomes, including cardiovascular disease, and risk behaviours such as alcohol use. Health disparities among TGD people in the UK are driven by a combination of minority stress, barriers to general healthcare, and disadvantage across multiple social determinants of health. Limited access to gender-affirming healthcare compounds these disparities, contributing to stark differences in morbidity and mortality relative to cisgender populations. Preventive healthcare engagement is also disproportionately low among TGD individuals, further exacerbating long-term health risks. The intersection of social exclusion, policy-driven discrimination, and systemic healthcare inequities places TGD people at significant and potentially increasing risk of poor health outcomes. The health disparities faced by TGD people in the UK constitute a real-time public health crisis that demands urgent and sustained intervention. TGD people must be central to shaping the strategic direction of a coordinated and adequately resourced response to these harms.