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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 study examines whether people using AI to complete tasks can accurately monitor how well they perform. 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 use. We discuss how AI levels our cognitive and metacognitive performance and consider the consequences of performance overestimation for designing interactive AI systems that enhance cognition., Comment: 20 pages, 6 figures, 4 tables

The distribution of different types of atmospheres and surfaces 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 the detection of atmospheres. One technique is to measure day-side emission to search for lower thermal emission than expected for a black-body planet due to atmospheric absorption features. Another technique is to measure phase curves of thermal emission to search for night-side emission due to atmospheric heat redistribution. In this work we compare strategies for detecting atmospheres on rocky exoplanets using these techniques. We simulate secondary eclipse and phase curve observations in the MIRI F1500W and F1280W filters, for a range of surfaces and atmospheres on thirty exoplanets selected for their F1500W signal-to-noise ratio. Our results show that secondary eclipse observations are highly degenerate between surfaces and atmospheres, given the wide range of potential surface albedos. We also show that thick atmospheres can support emission consistent with a black-body planet in these filters. These two results make it difficult to unambiguously detect or rule out atmospheres using their photometric day-side emission, except in a subset of CO$_{2}$-dominated atmospheres. We suggest that an F1500W phase curve could instead be observed for a similar sample of planets, allowing the unambiguous detection of atmospheres by night-side emission.

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.

Financial cybercrime prevention is an increasing issue with many organisations and governments. As deep learning models have progressed to identify illicit activity on various financial and social networks, the explainability behind the model decisions has been lacklustre with the investigative analyst at the heart of any deep learning platform. In our paper, we present a state-of-the-art, novel multimodal proactive approach to addressing XAI in financial cybercrime detection. We leverage a triad of deep learning models designed to distill essential representations from transaction sequencing, subgraph connectivity, and narrative generation to significantly streamline the analyst's investigative process. Our narrative generation proposal leverages LLM to ingest transaction details and output contextual narrative for an analyst to understand a transaction and its metadata much further., Comment: 6 pages, 3 figures

The $g$ factors of $11/2^-$ isomers in semimagic $^{109}$Sn and $^{111}$Sn (isomeric lifetimes $\tau = 2.9(3)$ ns and $\tau = 14.4(7)$ ns, respectively) were measured by an extension of the Time Differential Perturbed Angular Distribution technique, which uses \LaBr detectors and the hyperfine fields of a gadolinium host to achieve precise measurements in a new regime of short-lived isomers. The results, $g(11/2^-; {^{109}\textrm{Sn}}) = -0.186(8)$ and $g(11/2^-; {^{111}\textrm{Sn}}) = -0.214(4)$, are significantly lower in magnitude than those of the $11/2^-$ isomers in the heavier isotopes and depart from the value expected for a near pure neutron $h_{11/2}$ configuration. Broken-symmetry density functional theory calculations applied to the sequence of $11/2^-$ states reproduce the magnitude and location of this deviation. The $g(11/2^-)$ values are affected by shape core polarization; the odd $0h_{11/2}$ neutron couples to $J^{\pi}=2^+,4^+,6^+...$ configurations in the weakly-deformed effective core, causing a decrease in the $g$-factor magnitudes., Comment: 8 pages, 7 figures. Accepted in Physics Letters B

Photoionization models frequently assume constant temperature or density within HII regions. We investigate this assumption by measuring the detailed temperature and density structures of four HII regions in the Large Magellanic Cloud and the Small Magellanic Cloud, using integral-field spectroscopic data from the Wide-Field Spectrograph on the ANU 2.3m telescope. We analyse the distribution of emission-lines of low-ionization species, intermediate-ionization species and high-ionization species. We present the complex electron temperature and density structures within HII regions. All four nebulae present a negative gradient in the electron density profile. Both positive and negative temperature gradients are observed in the nebulae. We create a series of nebula models with a constant ISM pressure and varying temperature and density distributions. Comparison of the line ratios between our HII regions and models suggests that none of the simple nebula models can reproduce the observed temperature and density structures. Comparison between the models and the data suggests that the ISM pressure of nebulae in LMC and SMC is between log(P/k)=6-7.5. Complex internal structures of the nebulae highlight the importance of future Monte-Carlo photoionization codes for accurate nebula modeling, which include a comprehensive consideration of arbitrary geometries of HII regions., Comment: 24 pages, 12 figures, 11 tables, Accepted for publication in ApJ

To overcome the challenges associated with poor temporal stability of perovskite solar cells, methods are required that allow for fast iteration of fabrication and characterisation, such that optimal device performance and stability may be actively pursued. Currently, establishing the causes of underperformance is both complex and time-consuming, and optimisation of device fabrication thus inherently slow. Here, we present a means of computational device characterisation of mobile halide ion parameters from room temperature current-voltage (J-V) measurements only, requiring $\sim 2$ hours of computation on basic computing resources. With our approach, the physical parameters of the device may be reverse modelled from experimental J-V measurements. In a drift-diffusion model, the set of coupled drift-diffusion partial differential equations cannot be inverted explicitly, so a method for inverting the drift-diffusion simulation is required. We show how Bayesian Parameter Estimation (BPE) coupled with a drift-diffusion perovskite solar cell model can determine the extent to which device parameters affect performance measured by J-V characteristics. Our method is demonstrated by investigating the extent to which device performance is influenced by mobile halide ions for a specific fabricated device. The ion vacancy density $N_0$ and diffusion coefficient $D_I$ were found to be precisely characterised for both simulated and fabricated devices. This result opens up the possibility of pinpointing origins of degradation by finding which parameters most influence device J-V curves as the cell degrades.

Here we show that compressive sensing allow 4-dimensional (4-D) STEM data to be obtained and accurately reconstructed with both high-speed and low fluence. The methodology needed to achieve these results compared to conventional 4-D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns, and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide data-set shows that it is possible to recover over 25dB peak signal-to-noise in the recovered phase using 0.3% of the total data.

Context: High-resolution spectra in the near-infrared (NIR) are an important tool for the detailed study of stellar atmospheres. The accurate identification of elements and molecules in these spectra can be used to determine chemical abundances and physical conditions in the photosphere of the observed star. Such identifications require precise line positions and strengths of both atomic and molecular features. Aims: This work focusses on the full identification of absorption lines in the NIR spectrum of the K-giant 10 Leo, including previously unidentified lines. The large number and complexity of the observed absorption lines require a deep search for potential spectral signatures to enable an unambiguous assignment to specific elements or molecular species. We aim to improve the published line lists of metals, some of which are determined by model calculations only, and many of which presently lack the completeness and accuracy of line parameters. Methods: The CRIRES-POP project provided high-resolution, high signal-to-noise ratio (S/N) spectra of several bright stars in the 1 to 5 $\mu$m range. For the K-giant 10 Leo, a spectrum corrected for telluric absorption and with precise wavelength calibration is available. This has been analysed by comparison with model spectra and up-to-date line lists. Results: We identified lines of 29 elements and eight molecular species. While the positions of many known lines could be confirmed, about 6% of all lines detected in 10 Leo could not be attributed to any known feature. For CO and its isotopologues, molecular constants could be derived and several additional lines identified. We report major inconsistencies for some prominent lines. In addition, abundances for several key elements in 10 Leo are provided., Comment: Accepted for publication in A&A

Traditional image acquisition for cryo focused ion-beam scanning electron microscopy tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the FIB-SEM without an appreciable low in image quality. In this work, experimental data is presented which validates subsampling and inpainting as a useful tool for convenient and reliable data acquisition in a FIB-SEM, with new methods of handling 3-dimensional data being employed in context of dictionary learning and inpainting algorithms using a newly developed microscope control software and data recovery algorithm., Comment: In submission to "Microscopy and Microanalysis" journal. Authorship reviewed from previous submission

TraitLab is a software package for simulating, fitting and analysing tree-like binary data under a stochastic Dollo model of evolution. The model also allows for rate heterogeneity through catastrophes, evolutionary events where many traits are simultaneously lost while new ones arise, and borrowing, whereby traits transfer laterally between species as well as through ancestral relationships. The core of the package is a Markov chain Monte Carlo (MCMC) sampling algorithm that enables the user to sample from the Bayesian joint posterior distribution for tree topologies, clade and root ages, and the trait loss, catastrophe and borrowing rates for a given data set. Data can be simulated according to the fitted Dollo model or according to a number of generalized models that allow for heterogeneity in the trait loss rate, biases in the data collection process and borrowing of traits between lineages. Coupled pairs of Markov chains can be used to diagnose MCMC mixing and convergence and to debias MCMC estimators. The raw data, MCMC run output, and model fit can be inspected using a number of useful graphical and analytical tools provided within the package or imported into other popular analysis programs. TraitLab is freely available and runs within the Matlab computing environment with its Statistics and Machine Learning toolbox, no other additional toolboxes are required., Comment: Manual describing the TraitLab software for phylogenetic inference