Search

Background: Adolescents are particularly vulnerable to mental disorders, with over 75% of cases manifesting before the age of 25. Research indicates that only 18 to 34% of young people experiencing high levels of depression or anxiety symptoms seek support. Digital tools leveraging smartphones offer scalable and early intervention opportunities. Objective: Using a novel machine learning framework, this study evaluated the feasibility of integrating active and passive smartphone data to predict mental disorders in non-clinical adolescents. Specifically, we investigated the utility of the Mindcraft app in predicting risks for internalising and externalising disorders, eating disorders, insomnia and suicidal ideation. Methods: Participants (N=103; mean age 16.1 years) were recruited from three London schools. Participants completed the Strengths and Difficulties Questionnaire, the Eating Disorders-15 Questionnaire, Sleep Condition Indicator Questionnaire and indicated the presence/absence of suicidal ideation. They used the Mindcraft app for 14 days, contributing active data via self-reports and passive data from smartphone sensors. A contrastive pretraining phase was applied to enhance user-specific feature stability, followed by supervised fine-tuning. The model evaluation employed leave-one-subject-out cross-validation using balanced accuracy as the primary metric. Results: The integration of active and passive data achieved superior performance compared to individual data sources, with mean balanced accuracies of 0.71 for SDQ-High risk, 0.67 for insomnia, 0.77 for suicidal ideation and 0.70 for eating disorders. The contrastive learning framework stabilised daily behavioural representations, enhancing predictive robustness. This study demonstrates the potential of integrating active and passive smartphone data with advanced machine-learning techniques for predicting mental health risks.

In Bayesian inference prior hyperparameters are chosen subjectively or estimated using empirical Bayes methods. Generalised Bayesian Inference also has hyperparameters (the learning rate, and parameters of the loss). As part of the Generalised-Bayes workflow it is necessary to check sensitivity to the choice of hyperparameters, but running MCMC or fitting a variational approximation at each hyperparameter setting is impractical when there are more than a few hyperparameters. Simulation Based Inference has been used to amortise over data and hyperparameters and can be useful for Bayesian problems. However, there is no Simulation Based Inference for Generalised Bayes posteriors, as there is no generative model for the data. Working with a variational family parameterised by a normalising flow, we show how to fit a variational Generalised Bayes posterior, amortised over all hyperparameters. This may be sampled very efficiently at different hyperparameter values without refitting, and supports efficient robustness checks and hyperparameter selection. We show that there exist amortised normalising-flow architectures which are universal approximators. We test our approach on a relatively large-scale application of Generalised Bayesian Inference. The code is available online.

We introduce the Speak & Improve Corpus 2025, a dataset of L2 learner English data with holistic scores and language error annotation, collected from open (spontaneous) speaking tests on the Speak & Improve learning platform. The aim of the corpus release is to address a major challenge to developing L2 spoken language processing systems, the lack of publicly available data with high-quality annotations. It is being made available for non-commercial use on the ELiT website. In designing this corpus we have sought to make it cover a wide-range of speaker attributes, from their L1 to their speaking ability, as well as providing manual annotations. This enables a range of language-learning tasks to be examined, such as assessing speaking proficiency or providing feedback on grammatical errors in a learner's speech. Additionally the data supports research into the underlying technology required for these tasks including automatic speech recognition (ASR) of low resource L2 learner English, disfluency detection or spoken grammatical error correction (GEC). The corpus consists of around 315 hours of L2 English learners audio with holistic scores, and a subset of audio annotated with transcriptions and error labels.

Atmospheric energy transport is central to the cooling of primordial magma oceans. Theoretical studies of atmospheres on lava planets have assumed that convection is the only process involved in setting the atmospheric temperature structure. This significantly influences the ability for a magma ocean to cool. It has been suggested that convective stability in these atmospheres could preclude permanent magma oceans. We develop a new 1D radiative-convective model in order to investigate when the atmospheres overlying magma oceans are convectively stable. Using a coupled interior-atmosphere framework, we simulate the early evolution of two terrestrial-mass exoplanets: TRAPPIST-1 c and HD 63433 d. Our simulations suggest that the atmosphere of HD 63433 d exhibits deep isothermal layers which are convectively stable. However, it is able to maintain a permanent magma ocean and an atmosphere depleted in H2O. It is possible to maintain permanent magma oceans underneath atmospheres without convection. Absorption features of CO2 and SO2 within synthetic emission spectra are associated with mantle redox state, meaning that future observations of HD 63433 d may provide constraints on the geochemical properties of a magma ocean analogous with the early Earth. Simulations of TRAPPIST-1 c indicate that it is expected to have solidified within 100 Myr, outgassing a thick atmosphere in the process. Cool isothermal stratospheres generated by low molecular-weight atmospheres can mimic the emission of an atmosphere-less body. Future work should consider how atmospheric escape and chemistry modulates the lifetime of magma oceans, and the role of tidal heating in sustaining atmospheric convection, Comment: Accepted for publication in MNRAS

This paper presents the "Speak & Improve Challenge 2025: Spoken Language Assessment and Feedback" -- a challenge associated with the ISCA SLaTE 2025 Workshop. The goal of the challenge is to advance research on spoken language assessment and feedback, with tasks associated with both the underlying technology and language learning feedback. Linked with the challenge, the Speak & Improve (S&I) Corpus 2025 is being pre-released, a dataset of L2 learner English data with holistic scores and language error annotation, collected from open (spontaneous) speaking tests on the Speak & Improve learning platform. The corpus consists of approximately 315 hours of audio data from second language English learners with holistic scores, and a 55-hour subset with manual transcriptions and error labels. The Challenge has four shared tasks: Automatic Speech Recognition (ASR), Spoken Language Assessment (SLA), Spoken Grammatical Error Correction (SGEC), and Spoken Grammatical Error Correction Feedback (SGECF). Each of these tasks has a closed track where a predetermined set of models and data sources are allowed to be used, and an open track where any public resource may be used. Challenge participants may do one or more of the tasks. This paper describes the challenge, the S&I Corpus 2025, and the baseline systems released for the Challenge.

Traditional methods for spatial inference estimate smooth interpolating fields based on features measured at well-located points. When the spatial locations of some observations are missing, joint inference of the fields and locations is possible as the fields inform the locations and vice versa. If the number of missing locations is large, conventional Bayesian Inference fails if the generative model for the data is even slightly mis-specified, due to feedback between estimated fields and the imputed locations. Semi-Modular Inference (SMI) offers a solution by controlling the feedback between different modular components of the joint model using a hyper-parameter called the influence parameter. Our work is motivated by linguistic studies on a large corpus of late-medieval English textual dialects. We simultaneously learn dialect fields using dialect features observed in ``anchor texts'' with known location and estimate the location of origin for ``floating'' textual dialects of unknown origin. The optimal influence parameter minimises a loss measuring the accuracy of held-out anchor data. We compute a (flow-based) variational approximation to the SMI posterior for our model. This allows efficient computation of the optimal influence. MCMC-based approaches, feasible on small subsets of the data, are used to check the variational approximation.

Interactions between magma oceans and overlying atmospheres on young rocky planets leads to an evolving feedback of outgassing, greenhouse forcing, and mantle melt fraction. Previous studies have predominantly focused on the solidification of oxidized Earth-similar planets, but the diversity in mean density and irradiation observed in the low-mass exoplanet census motivate exploration of strongly varying geochemical scenarios. We aim to explore how variable redox properties alter the duration of magma ocean solidification, the equilibrium thermodynamic state, melt fraction of the mantle, and atmospheric composition. We develop a 1D coupled interior-atmosphere model that can simulate the time-evolution of lava planets. This is applied across a grid of fixed redox states, orbital separations, hydrogen endowments, and C/H ratios around a Sun-like star. The composition of these atmospheres is highly variable before and during solidification. The evolutionary path of an Earth-like planet at 1 AU ranges between permanent magma ocean states and solidification within 1 Myr. Recently solidified planets typically host H2O- or H2-dominated atmospheres in the absence of escape. Orbital separation is the primary factor determining magma ocean evolution, followed by the total hydrogen endowment, mantle oxygen fugacity, and finally the planet's C/H ratio. Collisional absorption by H2 induces a greenhouse effect which can prevent or stall magma ocean solidification. Through this effect, as well as the outgassing of other volatiles, geochemical properties exert significant control over the fate of magma oceans on rocky planets., Comment: Accepted for publication in JGR: Planets

Structured light plays an important role in metrology, optical trapping and manipulation, communications, quantum technologies, nonlinear optics and provides a rich playground for addressing new optical phenomena. Here we demonstrate a novel approach for manipulating vector vortex beams carrying longitudinal field components using metamaterials with extreme anisotropy. Implementing vectorial spectroscopy, we show that the propagation of complex beams with inhomogeneous polarisation is strongly affected by the interplay of the metamaterial anisotropy with the transverse and longitudinal field structure of the beam. This phenomenon is especially pronounced in the epsilon-near-zero regime, exclusively realised for light polarised along the metamaterial optical axis, strongly influencing the interaction of longitudinal fields with the metamaterial. The requirements on the balance between the transverse and longitudinal fields to maintain a polarisation singularity at the beam axis allow control of the beam modal content, filtering diffraction effects and tailoring spatial polarisation distribution. The proposed approach offers important capabilities for wavefront shaping as well as local spatial polarisation engineering. The understanding of the interaction of vector beams with metamaterials opens new opportunities for applications in microscopy, information encoding, biochemical sensing and quantum technologies.

Dirichlet-Neumann Operators (DNOs) are important to the formulation, analysis, and simulation of many crucial models found in engineering and the sciences. For instance, these operators permit moving-boundary problems, such as the classical water wave problem (free-surface ideal fluid flow under the influence of gravity and capillarity), to be restated in terms of interfacial quantities, which not only eliminates the boundary tracking problem, but also reduces the problem dimension. While these DNOs have been the object of much recent study regarding their numerical simulation and rigorous analysis, they have yet to be examined in the setting of laterally quasiperiodic boundary conditions. The purpose of this contribution is to begin this investigation with a particular eye towards the problem of more realistically simulating two and three dimensional surface water waves. Here we not only carefully define the DNO with respect to these boundary conditions for Laplace's equation, but we also show the rigorous analyticity of these operators with respect to sufficiently smooth boundary perturbations. These theoretical developments suggest a novel algorithm for the stable and high-order simulation of the DNO, which we implement and extensively test., Comment: 43 pages, 7 figures

Over the past twenty years, the field of plasmonics has been revolutionized with the isolation and utilization of two--dimensional materials, particularly graphene. Consequently there is significant interest in rapid, robust, and highly accurate computational schemes which can incorporate such materials. Standard volumetric approaches can be contemplated, but these require huge computational resources. Here we describe an algorithm which addresses this issue for nonlocal models of the electromagnetic response of graphene. Our methodology not only approximates the graphene layer with a surface current, but also reformulates the governing volumetric equations in terms of surface quantities using Dirichlet--Neumann Operators. We have recently shown how these surface equations can be numerically simulated in an efficient, stable, and accurate fashion using a High--Order Perturbation of Envelopes methodology. We extend these results to the nonlocal model mentioned above, and using an implementation of this algorithm, we study absorbance spectra of TM polarized plane--waves scattered by a periodic grid of graphene ribbons., Comment: 21 pages, 4 figures

Generalised Bayesian Inference (GBI) attempts to address model misspecification in a standard Bayesian setup by tempering the likelihood. The likelihood is raised to a fractional power, called the learning rate, which reduces its importance in the posterior and has been established as a method to address certain kinds of model misspecification. Posterior Predictive Checks (PPC) attempt to detect model misspecification by locating a diagnostic, computed on the observed data, within the posterior predictive distribution of the diagnostic. This can be used to construct a hypothesis test where a small $p$-value indicates potential misfit. The recent Embedded Diachronic Sense Change (EDiSC) model suffers from misspecification and benefits from likelihood tempering. Using EDiSC as a case study, this exploratory work examines whether PPC could be used in a novel way to set the learning rate in a GBI setup. Specifically, the learning rate selected is the lowest value for which a hypothesis test using the log likelihood diagnostic is not rejected at the 10% level. The experimental results are promising, though not definitive, and indicate the need for further research along the lines suggested here.

Optimizing human-AI interaction requires users to reflect on their own performance critically. Our paper examines whether people using AI to complete tasks can accurately monitor how well they perform. In Study 1, participants (N = 246) used AI to solve 20 logical problems from the Law School Admission Test. While their task performance improved by three points compared to a norm population, participants overestimated their performance by four points. Interestingly, higher AI literacy was linked to less accurate self-assessment. Participants with more technical knowledge of AI were more confident but less precise in judging their own performance. Using a computational model, we explored individual differences in metacognitive accuracy and found that the Dunning-Kruger effect, usually observed in this task, ceased to exist with AI. Study 2 (N = 452) replicates these findings. We discuss how AI levels metacognitive performance and consider consequences of performance overestimation for interactive AI systems enhancing cognition., Comment: 27 pages, 10 figures, 6 tables

The prevalence of atmospheres on rocky planets is one of the major questions in exoplanet astronomy, but there are currently no published unambiguous detections of atmospheres on any rocky exoplanets. The MIRI instrument on JWST can measure thermal emission from tidally locked rocky exoplanets orbiting small, cool stars. This emission is a function of their surface and atmospheric properties, potentially allowing detections of atmospheres. One way to find atmospheres is to search for lower day-side emission than would be expected for a black body planet. Another technique is to measure phase curves of thermal emission to search for night-side emission due to atmospheric heat redistribution. Here, we compare strategies for detecting atmospheres on rocky exoplanets. We simulate secondary eclipse and phase curve observations in the MIRI F1500W and F1280W filters, for a range of surfaces (providing our open access albedo data) and atmospheres on thirty exoplanets selected for their F1500W signal-to-noise ratio. We show that secondary eclipse observations are more degenerate between surfaces and atmospheres than suggested in previous work, and that thick atmospheres can support emission consistent with a black body planet in these filters. These results make it difficult to unambiguously detect or rule out atmospheres using their photometric day-side emission alone. We suggest that an F1500W phase curve could instead be observed for a similar sample of planets. While phase curves are time-consuming and their instrumental systematics can be challenging, we suggest that they allow the only unambiguous detections of atmospheres by night-side thermal emission., Comment: Updated with version accepted for publication in the Astrophysical Journal Letters

Partial orders may be used for modeling and summarising ranking data when the underlying order relations are less strict than a total order. They are a natural choice when the data are lists recording individuals' positions in queues in which queue order is constrained by a social hierarchy, as it may be appropriate to model the social hierarchy as a partial order and the lists as random linear extensions respecting the partial order. In this paper, we set up a new prior model for partial orders incorporating ties by clustering tied actors using a Poisson Dirichlet process. The family of models is projective. We perform Bayesian inference with different choices of noisy observation model. In particular, we propose a Mallow's observation model for our partial orders and give a recursive likelihood evaluation algorithm. We demonstrate our model on the 'Royal Acta' (Bishop) list data where we find the model is favored over well-known alternatives which fit only total orders.

By quantizing network weights and activations to low bitwidth, we can obtain hardware-friendly and energy-efficient networks. However, existing quantization techniques utilizing the straight-through estimator and piecewise constant functions face the issue of how to represent originally high-bit input data with low-bit values. To fully quantize deep neural networks, we propose pixel embedding, which replaces each float-valued input pixel with a vector of quantized values by using a lookup table. The lookup table or low-bit representation of pixels is differentiable and trainable by backpropagation. Such replacement of inputs with vectors is similar to word embedding in the natural language processing field. Experiments on ImageNet and CIFAR-100 show that pixel embedding reduces the top-5 error gap caused by quantizing the floating points at the first layer to only 1% for the ImageNet dataset, and the top-1 error gap caused by quantizing first and last layers to slightly over 1% for the CIFAR-100 dataset. The usefulness of pixel embedding is further demonstrated by inference time measurements, which demonstrate over 1.7 times speedup compared to floating point precision first layer.

Electron backscatter diffraction (EBSD) has developed over the last few decades into a valuable crystallographic characterisation method for a wide range of sample types. Despite these advances, issues such as the complexity of sample preparation, relatively slow acquisition, and damage in beam-sensitive samples, still limit the quantity and quality of interpretable data that can be obtained. To mitigate these issues, here we propose a method based on the subsampling of probe positions and subsequent reconstruction of an incomplete dataset. The missing probe locations (or pixels in the image) are recovered via an inpainting process using a dictionary-learning based method called beta-process factor analysis (BPFA). To investigate the robustness of both our inpainting method and Hough-based indexing, we simulate subsampled and noisy EBSD datasets from a real fully sampled Ni-superalloy dataset for different subsampling ratios of probe positions using both Gaussian and Poisson noise models. We find that zero solution pixel detection (inpainting un-indexed pixels) enables higher quality reconstructions to be obtained. Numerical tests confirm high quality reconstruction of band contrast and inverse pole figure maps from only 10% of the probe positions, with the potential to reduce this to 5% if only inverse pole figure maps are needed. These results show the potential application of this method in EBSD, allowing for faster analysis and extending the use of this technique to beam sensitive materials.

Electromagnetic waves interacting with three--dimensional periodic structures occur in many applications of great scientific and engineering interest. These three dimensional interactions are extremely complicated and subtle, so it is unsurprising that practitioners find their rapid, robust, and accurate numerical simulation to be of paramount interest. Among the wide array of possible numerical approaches, the High--Order Spectral algorithms are often preferred due to their surpassing fidelity with a moderate number of unknowns, and here we describe an algorithm that fits into this class. In addition, we take a perturbative approach to the problem which views the deviation of the permittivity from a reference value as the deformation and we conduct a regular perturbation theory. This work concludes a line of research on these methods which began with two-dimensional problems governed by the Helmholtz equation and moved to small perturbations in the fully three-dimensional vector Maxwell equations. We now extend these latter results to large (real) perturbations constituting a rigorous analytic continuation., Comment: arXiv admin note: text overlap with arXiv:2307.15152

Despite the widespread use of Scanning Transmission Electron Microscopy (STEM) for observing the structure of materials at the atomic scale, a detailed understanding of some relevant electron beam damage mechanisms is limited. Recent reports suggest that certain types of damage can be modeled as a diffusion process and that the accumulation effects of this process must be kept low in order to reduce damage. We therefore develop an explicit mathematical formulation of spatiotemporal diffusion processes in STEM that take into account both instrument and sample parameters. Furthermore, our framework can aid the design of Diffusion Controlled Sampling (DCS) strategies using optimally selected probe positions in STEM, that constrain the cumulative diffusion distribution. Numerical simulations highlight the variability of the cumulative diffusion distribution for different experimental STEM configurations. These analytical and numerical frameworks can subsequently be used for careful design of 2- and 4-dimensional STEM experiments where beam damage is minimised., Comment: Main document: 29 pages, 12 figures; Supplementary document: 18 pages, 5 figures

Massively multiplayer online (MMO) games boomed in the late 1990s to 2000s. In parallel, ethnographic studies of these communities emerged, generally involving participant observation and interviews. Several decades on, many MMOs have been reconfigured, remastered or are potentially no longer accessible at all, which presents challenges for their continued study and long-term preservation. In this paper we explore the "go-along" methodology, in which a researcher joins a participant on a walk through a familiar place and asks them questions, as a qualitative research method applicable for the study and preservation of games culture. Though the methodology has been introduced in digital media studies, to date it has had limited application in digital games, if at all. We report on a pilot study exploring applications of the go-along method to the sandbox MMO Wurm Online; a persistent, player-directed world with a rich history. We report on our motivations for the work, our analysis of the resulting interviews, and our reflections on both the use of go-alongs in digital games, as well as the unique and inspiring culture and community of this lesser-known game., Comment: 16 pages, 6 figures

Archaeogaming and queer games studies have both grown as paradigms in the last decade. The former broadly refers to the archaeological study of games, while the later concerns the application of queer theory to the medium. To date, there has been limited engagement of archaeogamers with queer games scholarship, and vice versa. This article argues that there are epistomological parallels between the two; as they are both concerned with the limits and ethics of representation, the personal and political contexts of game development and engagement with video games through transgressive play. The paper is structured around an extended literature review and three vignettes that reflect on the author's personal experience of conducting archaeogaming research, an ethnographic study of Wurm Online, an archaeological survey of Elden Ring and a player study of the generative archaeology game Nothing Beside Remains. While archaeogaming can learn from the centring of subjective lived experience and labour in the queer games sphere, archaeogaming as a form of game preservation can also benefit queer games studies., Comment: 9 pages

Although the principal physical behaviour of a material is inherently connected to its fundamental crystal structure, the behaviours observed in the real-world are often driven by the microstructure, which for many polycrystalline materials, equates to the size and shape of the constituent crystal grains. Here we highlight a cutting edge synthesis route to the controlled engineering of grain structures in thin films and the simplification of associated 3-dimensional problems to less complex 2D ones. This has been applied to the actinide ceramic, uranium dioxide, to replicate structures typical in nuclear fission fuel pellets, in order to investigate the oxidation and subsequent transformation of cubic UO$_{2}$ to orthorhombic U$_{3}$O$_{8}$. This article shows how this synthesis approach could be utilised to investigate a range of phenomena, affected by grain morphology, and highlights some unusual results in the oxidation behaviour of UO$_{2}$, regarding the phase transition to U$_{3}$O$_{8}$., Comment: 21 pages, 3 figures

Mildly irradiated mini-Neptunes have densities potentially consistent with them hosting substantial liquid water oceans (`Hycean' planets). The presence of CO2 and simultaneous absence of ammonia (NH3) in their atmospheres has been proposed as a fingerprint of such worlds. JWST observations of K2-18b, the archetypal Hycean, have found the presence of CO2 and the depletion of NH3 to <100 ppm; hence, it has been inferred that this planet may host liquid water oceans. In contrast, climate modelling suggests that many of these mini-Neptunes, including K2-18b, may likely be too hot to host liquid water. We propose a solution to this discrepancy between observation and climate modelling by investigating the effect of a magma ocean on the atmospheric chemistry of mini-Neptunes. We demonstrate that atmospheric NH3 depletion is a natural consequence of the high solubility of nitrogen species in magma at reducing conditions; precisely the conditions prevailing where a thick hydrogen envelope is in communication with a molten planetary surface. The magma ocean model reproduces the present JWST spectrum of K2-18b to < 3 sigma, suggesting this is as credible an explanation for current observations as the planet hosting a liquid water ocean. Spectral areas that could be used to rule out the magma ocean model include the >4um region, where CO2 and CO features dominate: Magma ocean models suggest a systematically lower CO2/CO ratio than estimated from free chemistry retrieval, indicating that deeper observations of this spectral region may be able to distinguish between oceans of liquid water and magma on mini-Neptunes., Comment: 13 pages, 5 figures, Published in The Astrophysical Journal Letters; corrects error on y-axis of Figure 2

Word meanings change over time, and word senses evolve, emerge or die out in the process. For ancient languages, where the corpora are often small and sparse, modelling such changes accurately proves challenging, and quantifying uncertainty in sense-change estimates consequently becomes important. GASC (Genre-Aware Semantic Change) and DiSC (Diachronic Sense Change) are existing generative models that have been used to analyse sense change for target words from an ancient Greek text corpus, using unsupervised learning without the help of any pre-training. These models represent the senses of a given target word such as "kosmos" (meaning decoration, order or world) as distributions over context words, and sense prevalence as a distribution over senses. The models are fitted using Markov Chain Monte Carlo (MCMC) methods to measure temporal changes in these representations. This paper introduces EDiSC, an Embedded DiSC model, which combines word embeddings with DiSC to provide superior model performance. It is shown empirically that EDiSC offers improved predictive accuracy, ground-truth recovery and uncertainty quantification, as well as better sampling efficiency and scalability properties with MCMC methods. The challenges of fitting these models are also discussed.