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Many lung diseases require detailed visualisation for accurate diagnosis and treatment. High-resolution computed tomography (CT) is the gold-standard technique for non-invasive lung disease detection, but it presents a risk to the patient through the relatively high ionising radiation dose required. Utilising the X-ray phase information may allow improvements in image resolution at equal or lower radiation levels than current clinical imaging. Propagation-based phase-contrast imaging requires minimal adaption of existing medical systems, and is well suited to lung imaging due to the strong phase gradients introduced by the lung-air material interfaces. Herein, propagation-based phase contrast CT is demonstrated for large animals, namely lambs, as a model for paediatric patients, using monochromatic radiation and a photon-counting detector at the Imaging and Medical Beamline of the Australian Synchrotron. Image quality, normalised against radiation dose, was optimised as a function of the beam energy and propagation distance, with the optimal conditions used to test the available image quality at very low radiation dose. The resulting CT images demonstrate superior resolution to existing high-resolution CT systems, pushing dose to the quantum limit to comply with current Australian guidelines for infant chest CT exposure of $<2.5\:\text{mSv}$ effective dose. Constituent raw projections are shown to have significant proportions of pixels with zero photon counts that would create severe information loss in conventional CT. Phase retrieval enabled clear visualisation of minor lung airways at doses up to 1,225$\pm$31\% times lower than conventional CT reconstruction, at a voxel size of just 75$\mathrm{\mu}$m.

We present a theoretical framework for 'collective transition quenching', a quantum many-body dissipative phenomenon that occurs in systems with multiple collective decay channels. Despite the competition, interactions suppress all but the dominant decay transition, leading to a 'winner takes all' dynamic where the system primarily settles into the dominant ground state. We prove that, in the presence of permutation symmetry, this problem is exactly solvable for any number of competing channels. Additionally, we develop an approximate model for the dynamics by mapping the evolution into a continuity equation for a fluid, and show analytically that the dominant transition ratio converges to unity with increasing system size as a power-law, for any branching ratio. This near-deterministic preparation of the dominant ground state has broad applicability. As an example we discuss a protocol for molecular photoassociation where collective dynamics effectively acts as a catalyst, amplifying the yield in a particular final state. Our results open new avenues for many-body strategies in the preparation and control of quantum systems., Comment: 5 pages, 3 figures

The gendered expectations about ideal body types can lead to body image concerns, dissatisfaction, and in extreme cases, disordered eating and other psychopathologies across the gender spectrum. While research has focused on pro-anorexia online communities that glorify the 'thin ideal', less attention has been given to the broader spectrum of body image concerns or how emerging disorders like muscle dysmorphia ('bigorexia') present in online discussions. To address these gaps, we analyze 46 Reddit discussion forums related to diet, fitness, and associated mental health challenges. Using membership structure analysis and transformer-based language models, we project these communities along gender and body ideal axes, revealing complex interactions between gender, body ideals, and emotional expression. Our findings show that feminine-oriented communities generally express more negative emotions, particularly in thinness-promoting forums. Conversely, communities focused on the muscular ideal exhibit less negativity, regardless of gender orientation. We also uncover a gendered pattern in emotional indicators of mental health challenges, with communities discussing serious issues aligning more closely with thinness-oriented, predominantly feminine-leaning communities. By revealing the gendered emotional dynamics of online communities, our findings can inform the development of more effective content moderation approaches that facilitate supportive interactions, while minimizing exposure to potentially harmful content.

Constructing Driver Hamiltonians and Mixing Operators such that they satisfy constraints is an important ansatz construction for quantum algorithms. We give general algebraic expressions for finding Hamiltonian terms and analogously unitary primitives, that satisfy constraint embeddings and use these to give complexity characterizations of the related problems. Finding operators that enforce classical constraints is proven to be NP-Complete in the general case; algorithmic procedures with worse-case polynomial runtime to find any operators with a constant locality bound, applicable for many constraints. We then give algorithmic procedures to turn these algebraic primitives into Hamiltonian drivers and unitary mixers that can be used for Constrained Quantum Annealing (CQA) and Quantum Alternating Operator Ansatz (QAOA) constructions by tackling practical problems related to finding an appropriate set of reduced generators and defining corresponding drivers and mixers accordingly. We then apply these concepts to the construction of ansaetze for 1-in-3 SAT instances. We consider the ordinary x-mixer QAOA, a novel QAOA approach based on the maximally disjoint subset, and a QAOA approach based on the disjoint subset as well as higher order constraint satisfaction terms. We empirically benchmark these approaches on instances sized between 12 and 22, showing the best relative performance for the tailored ansaetze and that exponential curve fits on the results are consistent with a quadratic speedup by utilizing alternative ansaetze to the x-mixer. We provide very general algorithmic prescriptions for finding driver or mixing terms that satisfy embedded constraints that can be utilized to probe quantum speedups for constraints problems with linear, quadratic, or even higher order polynomial constraints.

The spectra of singly ionised Strontium and Yttrium (Sr {\sc ii} and Y {\sc ii}) have been proposed as identifications of certain spectral features in the AT2017gfo spectrum. With the growing demand for NLTE simulations of Kilonovae, there is a increasing need for atomic data for these and other $r$-process elements. Our goal is to expand upon the current set of atomic data for $r$-process elements, by presenting transition probabilities and Maxwellian-averaged effective collision strengths for Sr {\sc ii} and Y {\sc ii}. The Breit-Pauli and DARC $R$-matrix codes are employed to calculate the appropriate collision strengths, which are thermally averaged according to a Maxwellian distribution to calculate excitation and de-excitation rates. The {\sc tardis} and {\sc ColRadPy} packages are subsequently used to perform LTE and NLTE modelling respectively. A complete set of transition probabilities and effective collision strengths involving levels for Sr {\sc ii} and Y {\sc ii} have been calculated for temperature ranges compatible with kilonova plasma conditions. Forbidden transitions were found to disagree heavily with the Axelrod approximation, an approximation which is currently employed by other models within the literature. Theoretically important spectral lines are identified with both LTE and NLTE modelling codes. LTE simulations in {\sc tardis} reveal no new significant changes to the full synthetic spectra. NLTE simulations in {\sc ColRadPy} provide indications of which features are expected to be strong for a range of regimes, and we include luminosity estimates. Synthetic emission spectra over KNe densities and temperatures reveal potentially interesting spectral lines in the NIR.

The Programme for International Student Assessment (PISA) assesses the knowledge and skills in mathematics, reading and science of 15-year-old pupils in countries around the world. PISA is run by the Organisation for Economic Co-operation and Development (OECD), and assessment is typically undertaken every 3 years, allowing us to chart how performance changes over time and across different education systems. PISA 2022, which was undertaken 4 years after the previous cycle due to the global COVID-19 pandemic, involved 81 education systems, including England. In England, 4,763 15-year-old pupils from 165 schools completed a 2-hour computer-based assessment and pupil questionnaire. Headteachers at participating schools were also asked to complete a school questionnaire. The study was carried out in November and December 2022, with most of those pupils who participated completing their GCSE exams in 2023. This report analyses their performance in the three subject domains of mathematics, reading and science, as well as their responses to the pupil questionnaire, and their headteachers' responses to the school questionnaire. When reading this report, it is important to keep in mind that England's sample of participating pupils may not be entirely representative of all 15-year-old pupils in England. This is, to some degree, always the case with international studies such as PISA, but in this case the sample for England did not meet 2 of the 82 PISA Technical Standards. Analysis of the characteristics of the pupils who participated revealed that the final sample had somewhat higher academic attainment on average than the general population and a somewhat lower proportion of pupils who had been eligible for free school meals in the past 6 years. In other words, higher performing pupils may be overrepresented in the final sample and some of the PISA results may therefore be somewhat higher than they might otherwise be. This issue was also a challenge for some of the other participating education systems including several OECD countries. Given that the sample may not be entirely representative of the population, caution is required when interpreting the analysis that is presented in this report, though this does not necessarily translate directly to a particular score being a certain number of points higher than its 'true' value, and the OECD itself makes no adjustments to the scores in any education system in which some of the PISA's Technical Standards were not met. The term 'significant' is used throughout this report to refer to statistically significant differences between scores or values. In this report, we use a '95% confidence level' to define statistical significance. A statistically significant result is one that is not likely to occur by chance, due to the sampling process, and is more likely to be attributable to a genuine difference between groups. Similarly, the term average, as in 'average score', is used to refer to the arithmetic mean for the relevant group, unless stated otherwise.

Cleavable metallic antiferromagnets may be of use for low-dissipation spintronic devices; however, few are currently known. Here we present orthorhombic MnRhBi3 as one such compound and present a thorough study of its physical properties. Exfoliation is demonstrated experimentally, and the cleavage energy and electronic structure are examined by density functional theory calculations. It is concluded that MnRhBi3 is a van der Waals layered material that cleaves easily between neighboring Bi layers, and that the Bi atoms have lone pairs extending into the van der Waals gaps. A series of four phase transitions are observed below room temperature, and neutron diffraction shows that at least two of the transitions involve the formation of antiferromagnetic order. Anomalous thermal expansion points to a crystallographic phase transition and/or strong magnetoelastic coupling. This work reveals a complex phase evolution in MnRhBi3 and establishes this cleavable antiferromagnetic metal as an interesting material for studying the interplay of structure, magnetism, and transport in the bulk and ultrathin limits as well as the role of lone pair electrons in interface chemistry and proximity effects in van der Waals heterostructures.

Language models are susceptible to bias, sycophancy, backdoors, and other tendencies that lead to unfaithful responses to the input context. Interpreting internal states of language models could help monitor and correct unfaithful behavior. We hypothesize that language models represent their input contexts in a latent world model, and seek to extract this latent world state from the activations. We do so with 'propositional probes', which compositionally probe tokens for lexical information and bind them into logical propositions representing the world state. For example, given the input context ''Greg is a nurse. Laura is a physicist.'', we decode the propositions ''WorksAs(Greg, nurse)'' and ''WorksAs(Laura, physicist)'' from the model's activations. Key to this is identifying a 'binding subspace' in which bound tokens have high similarity (''Greg'' and ''nurse'') but unbound ones do not (''Greg'' and ''physicist''). We validate propositional probes in a closed-world setting with finitely many predicates and properties. Despite being trained on simple templated contexts, propositional probes generalize to contexts rewritten as short stories and translated to Spanish. Moreover, we find that in three settings where language models respond unfaithfully to the input context -- prompt injections, backdoor attacks, and gender bias -- the decoded propositions remain faithful. This suggests that language models often encode a faithful world model but decode it unfaithfully, which motivates the search for better interpretability tools for monitoring LMs.

The Krohn-Rhodes Theorem proves that a finite semigroup divides a wreath product of groups and aperiodic semigroups. Krohn-Rhodes complexity equals the minimal number of groups that are needed. Determining an algorithm to compute complexity has been an open problem for more than 50 years. The main result of this paper proves that it is decidable whether a semigroup has complexity k for any k greater than or equal to 0. This builds on our previous work for complexity 1. In that paper we proved using profinite methods and results on free Burnside semigroups by McCammond and others that the lower bound from a 2012 paper by Henckell, Rhodes and Steinberg is precise for complexity 1. In this paper we define an improved version of the lower bound from the 2012 paper and prove that it is exact for arbitrary complexity.

We have derived the governing equations for an SIR model with delay terms in both the infectivity and recovery of the disease. The equations are derived by modelling the dynamics as a continuous time random walk, where individuals move between the classic SIR compartments. With an appropriate choice of distributions for the infectivity and recovery processes delay terms are introduced into the governing equations in a manner that ensures the physicality of the model. This provides novel insight into the underlying dynamics of an SIR model with time delays. The SIR model with delay infectivity and recovery allows for a more diverse range of dynamical behaviours. The model accounts for an incubation effect without the need to introduce new compartments., Comment: 17 pages, 6 figures

Filtering - the task of estimating the conditional distribution of states of a dynamical system given partial, noisy, observations - is important in many areas of science and engineering, including weather and climate prediction. However, the filtering distribution is generally intractable to obtain for high-dimensional, nonlinear systems. Filters used in practice, such as the ensemble Kalman filter (EnKF), are biased for nonlinear systems and have numerous tuning parameters. Here, we present a framework for learning a parameterized analysis map - the map that takes a forecast distribution and observations to the filtering distribution - using variational inference. We show that this methodology can be used to learn gain matrices for filtering linear and nonlinear dynamical systems, as well as inflation and localization parameters for an EnKF. Future work will apply this framework to learn new filtering algorithms., Comment: Workshop on Machine Learning for Earth System Modeling, International Conference on Machine Learning (ICML) 2024

In this paper, we study efficient approximate sampling for probability distributions known up to normalization constants. We specifically focus on a problem class arising in Bayesian inference for large-scale inverse problems in science and engineering applications. The computational challenges we address with the proposed methodology are: (i) the need for repeated evaluations of expensive forward models; (ii) the potential existence of multiple modes; and (iii) the fact that gradient of, or adjoint solver for, the forward model might not be feasible. While existing Bayesian inference methods meet some of these challenges individually, we propose a framework that tackles all three systematically. Our approach builds upon the Fisher-Rao gradient flow in probability space, yielding a dynamical system for probability densities that converges towards the target distribution at a uniform exponential rate. This rapid convergence is advantageous for the computational burden outlined in (i). We apply Gaussian mixture approximations with operator splitting techniques to simulate the flow numerically; the resulting approximation can capture multiple modes thus addressing (ii). Furthermore, we employ the Kalman methodology to facilitate a derivative-free update of these Gaussian components and their respective weights, addressing the issue in (iii). The proposed methodology results in an efficient derivative-free sampler flexible enough to handle multi-modal distributions: Gaussian Mixture Kalman Inversion (GMKI). The effectiveness of GMKI is demonstrated both theoretically and numerically in several experiments with multimodal target distributions, including proof-of-concept and two-dimensional examples, as well as a large-scale application: recovering the Navier-Stokes initial condition from solution data at positive times., Comment: 42 pages, 10 figures

A recent experiment [Cohen et al., Science 382, 542 (2023)] observed a quantized $e^2/2h$ conductance tunneling into a fractional quantum hall edge in the strong coupling limit, which is consistent with the prediction of a quantum Hall transformer. We model this quantum point contact (QPC) as a quantum wire with a spatially-varying Luttinger parameter and a back-scattering impurity and use a numerical solution to find the wavepacket scattering and microwave (i.e. finite frequency) has conductance at the interface. We then compare the microwave conductance to an analytic solution of a Luttinger liquid with an abruptly varying Luttinger parameter and a back-scattering impurity, which is obtained by mapping the problem to a boundary sine-Gordon (BSG) model. Finally, we show that the quantum Hall transformer can survive the inclusion of the domain wall which is expected to occur in a wide QPC, which is experimentally relevant, provided the momentum change in the QPC is generated by electrostatics. We find that back-scattering generated by the domain wall is of an order of magnitude consistent with the experiment., Comment: 5 pages, 3 figures

Purpose: The purpose of this study was to introduce SLOctolyzer: an open-source analysis toolkit for en face retinal vessels in infrared reflectance scanning laser ophthalmoscopy (SLO) images. Methods: SLOctolyzer includes two main modules: segmentation and measurement. The segmentation module uses deep learning methods to delineate retinal anatomy, and detects the fovea and optic disc, whereas the measurement module quantifies the complexity, density, tortuosity, and calibre of the segmented retinal vessels. We evaluated the segmentation module using unseen data and measured its reproducibility. Results: SLOctolyzer's segmentation module performed well against unseen internal test data (Dice for all-vessels = 0.91; arteries = 0.84; veins = 0.85; optic disc = 0.94; and fovea = 0.88). External validation against severe retinal pathology showed decreased performance (Dice for arteries = 0.72; veins = 0.75; and optic disc = 0.90). SLOctolyzer had good reproducibility (mean difference for fractal dimension = -0.001; density = -0.0003; calibre = -0.32 microns; and tortuosity density = 0.001). SLOctolyzer can process a 768 x 768 pixel macula-centred SLO image in under 20 seconds and a disc-centred SLO image in under 30 seconds using a laptop CPU. Conclusions: To our knowledge, SLOctolyzer is the first open-source tool to convert raw SLO images into reproducible and clinically meaningful retinal vascular parameters. SLO images are captured simultaneous to optical coherence tomography (OCT), and we believe SLOctolyzer will be useful for extracting retinal vascular measurements from large OCT image sets and linking them to ocular or systemic diseases. It requires no specialist knowledge or proprietary software, and allows manual correction of segmentations and re-computing of vascular metrics. SLOctolyzer is freely available at https://github.com/jaburke166/SLOctolyzer., Comment: 13 pages, 6 figures, 6 tables + Supplementary (9 pages, 13 figures, 4 tables, 2 code listings). Accepted and published at ARVO Translational Vision Science and Technology

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA's work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware co-design in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing and describe improved methods for simulating quantum systems of various types on high-performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, as well as insights into fundamental quantum physics that can be harnessed for computational purposes., Comment: 27 pages, 0 figures

Fast radio bursts (FRBs) are brilliant short-duration flashes of radio emission originating at cosmological distances. The vast diversity in the properties of currently known FRBs, and the fleeting nature of these events make it difficult to understand their progenitors and emission mechanism(s). Here we report high time resolution polarization properties of FRB 20210912A, a highly energetic event detected by the Australian Square Kilometre Array Pathfinder (ASKAP) in the Commensal Real-time ASKAP Fast Transients (CRAFT) survey, which show intra-burst PA variation similar to Galactic pulsars and unusual variation of Faraday Rotation Measure (RM) across its two sub-bursts. The observed intra-burst PA variation and apparent RM variation pattern in FRB 20210912A may be explained by a rapidly-spinning neutron star origin, with rest-frame spin periods of ~1.1 ms. This rotation timescale is comparable to the shortest known rotation period of a pulsar, and close to the shortest possible rotation period of a neutron star. Curiously, FRB 20210912A exhibits a remarkable resemblance with the previously reported FRB 20181112A, including similar rest-frame emission timescales and polarization profiles. These observations suggest that these two FRBs may have similar origins., Comment: Accepted for publication in ApJ Letters

Circularly polarized, nearly parallel propagating waves are prevalent in the solar wind at ion-kinetic scales. At these scales, the spectrum of turbulent fluctuations in the solar wind steepens, often called the transition-range, before flattening at sub-ion scales. Circularly polarized waves have been proposed as a mechanism to couple electromagnetic fluctuations to ion gyromotion, enabling ion-scale dissipation that results in observed ion-scale steepening. Here, we study Parker Solar Probe observations of an extended stream of fast solar wind ranging from 15-55 solar radii. We demonstrate that, throughout the stream, transition-range steepening at ion-scales is associated with the presence of significant left handed ion-kinetic scale waves, which are thought to be ion-cyclotron waves. We implement quasilinear theory to compute the rate at which ions are heated via cyclotron resonance with the observed circularly polarized waves given the empirically measured proton velocity distribution functions. We apply the Von Karman decay law to estimate the turbulent decay of the large-scale fluctuations, which is equal to the turbulent energy cascade rate. We find that the ion-cyclotron heating rates are correlated with, and amount to a significant fraction of, the turbulent energy cascade rate, implying that cyclotron heating is an important dissipation mechanism in the solar wind.

Virtual, Mixed, and Augmented Reality (XR) technologies hold immense potential for transforming productivity beyond PC. Therefore there is a critical need for improved text input solutions for XR. However, achieving efficient text input in these environments remains a significant challenge. This paper examines the current landscape of XR text input techniques, focusing on the importance of keyboards (both physical and virtual) as essential tools. We discuss the unique challenges and opportunities presented by XR, synthesizing key trends from existing solutions.

In this work, exact solutions are derived for an integer- and fractional-order time-delayed diffusion equation with arbitrary initial conditions. The solutions are obtained using Fourier transform methods in conjunction with the known properties of delay functions. It is observed that the solutions do not exhibit infinite speed of propagation for smooth initial conditions that are bounded and positive. Sufficient conditions on the initial condition are also established such that the finite time blowup of the solutions can be explicitly calculated. Examples are provided that highlight the contrasting behaviours of these exact solutions with the known dynamics of solutions to the standard diffusion equation., Comment: Errors were discovered in the analysis, significant revisions are being made to the manuscript

Diamond-like carbon thin films have emerged as durable, chemically stable optical coatings for many optical and optoelectronics applications due to their hardness, chemical inertness, and optical transparency. This paper presents a novel high-energy electron cyclotron resonance ion beam sputter deposition technique to fabricate pure diamond-like carbon coatings at room temperature. The chemical composition of the deposited coatings including ratios of sp2/sp3 bonding in the thin films were determined by X-ray photoelectron spectroscopy. Results indicate that the sp3 percentage ranges from 45% - 85%. The transmission and reflectance spectra of the coatings were measured from UV to IR ({\lambda}= 185 to 2500 nm) by utilizing a spectrophotometer. The measured spectra were analysed by the Tauc method to determine the optical band gap and Urbach energy and an optical fitting software, which utilizes the model modified by OJL, to extract the refractive index and extinction coefficient. By varying the ion energy, the optical properties were found to be n = 2.30 - 2.51, band gap energy = 0.4 - 0.68 eV, and the Urbach energy = 0.33 - 0.49 eV. This study provides a flexible method for tuning the structural, optical, and electronic properties of diamond-like carbon coatings by controlling the ion energy during deposition.

A novel high-energy electron cyclotron resonance (ECR) ion beam deposition (IBD) technique was used to fabricate DLC films at different ion beam energies. The ratios of sp2/sp3 bonding in the DLC coatings were determined by Raman spectroscopy and XPS, with the confirmation of being hydrogen-free due to the lack of photoluminescence (PL) background in the Raman spectra. The results indicate that the sp3 percentage ranges from 45% - 85% for the ECR-IBD fabricated DLC films in this study. Monte-Carlo based SRIM simulation was used to extract the energy and angular distribution of the sputtered particles from the carbon target and correlate it to the highest sp3 fraction in the manufactured ECR-IBD DLCs. This study demonstrates a method of depositing DLC thin films under ambient conditions (room temperature with no post-annealing or additional bias voltage applied) which produces high-sp3 coatings (higher than those traditionally reported for other sputtering methods) suitable for applications where high quality DLC coatings are required.

Transformers, and the attention mechanism in particular, have become ubiquitous in machine learning. Their success in modeling nonlocal, long-range correlations has led to their widespread adoption in natural language processing, computer vision, and time-series problems. Neural operators, which map spaces of functions into spaces of functions, are necessarily both nonlinear and nonlocal if they are universal; it is thus natural to ask whether the attention mechanism can be used in the design of neural operators. Motivated by this, we study transformers in the function space setting. We formulate attention as a map between infinite dimensional function spaces and prove that the attention mechanism as implemented in practice is a Monte Carlo or finite difference approximation of this operator. The function space formulation allows for the design of transformer neural operators, a class of architectures designed to learn mappings between function spaces, for which we prove a universal approximation result. The prohibitive cost of applying the attention operator to functions defined on multi-dimensional domains leads to the need for more efficient attention-based architectures. For this reason we also introduce a function space generalization of the patching strategy from computer vision, and introduce a class of associated neural operators. Numerical results, on an array of operator learning problems, demonstrate the promise of our approaches to function space formulations of attention and their use in neural operators.

Control of frictional interactions among liquid-suspended particles has led to tunable, strikingly non-Newtonian rheology via the formation of strong flow constraints as particles come into close proximity under shear. Typically, these frictional interactions have been in the form of physical contact, controllable via particle shape and surface roughness. We investigate a different route, where molecular bridging between nearby particle surfaces generates a controllable "sticky" friction. This is achieved with surface-functionalized colloidal particles capable of forming dynamic covalent bonds with telechelic polymers that comprise the suspending fluid. At low shear stress this results in particles coated with a uniform polymer brush layer. Beyond an onset stress the telechelic polymers become capable of bridging and generate shear thickening. Over the size range investigated, we find that the dynamic brush layer leads to dependence of the onset stress on particle diameter that closely follows a power law with exponent -1.76. In the shear thickening regime, we observe an enhanced dilation in measurements of the first normal stress difference and reduction in the extrapolated volume fraction required for jamming, both consistent with an effective particle friction that increases with decreasing particle diameter. These results are discussed in light of predictions for suspensions of hard spheres and of polymer-grafted particles.

Assembly Theory (AT) was developed to help distinguish living from non-living systems. The theory is simple as it posits that the amount of selection or Assembly is a function of the number of complex objects where their complexity can be objectively determined using assembly indices. The assembly index of a given object relates to the number of recursive joining operations required to build that object and can be not only rigorously defined mathematically but can be experimentally measured. In pervious work we outlined the theoretical basis, but also extensive experimental measurements that demonstrated the predictive power of AT. These measurements showed that is a threshold in assembly indices for organic molecules whereby abiotic chemical systems could not randomly produce molecules with an assembly index greater or equal than 15. In a recent paper by Hazen et al [1] the authors not only confused the concept of AT with the algorithms used to calculate assembly indices, but also attempted to falsify AT by calculating theoretical assembly indices for objects made from inorganic building blocks. A fundamental misunderstanding made by the authors is that the threshold is a requirement of the theory, rather than experimental observation. This means that exploration of inorganic assembly indices similarly requires an experimental observation, correlated with the theoretical calculations. Then and only then can the exploration of complex inorganic molecules be done using AT and the threshold for living systems, as expressed with such building blocks, be determined. Since Hazen et al.[1] present no experimental measurements of assembly theory, their analysis is not falsifiable., Comment: 6 pages, 11 references

Magnetic skyrmion crystals are traditionally associated with non-centrosymmetric crystal structures; however, it has been demonstrated that skyrmion crystals can be stabilized by competing interactions in centrosymmetric crystals. To understand and optimize the physical responses associated with topologically-nontrivial skyrmion textures, it is important to quantify their magnetic interactions by comparing theoretical predictions with spectroscopic data. Here, we present neutron diffraction and spectroscopy data on the centrosymmetric skyrmion material GdRu$_2$Si$_2$, and show that the key spectroscopic features can be explained by the magnetic interactions calculated using density-functional theory calculations. We further show that the recently-proposed 2-$\mathbf{q}$ "topological spin stripe" structure yields better agreement with our data than a 1-$\mathbf{q}$ helical structure, and identify how the magnetic structure evolves with temperature., Comment: 6 pages, 3 figures

Transport phenomena play a vital role in various fields of science and engineering. In this work, exact solutions are derived for advection equations with integer- and fractional-order time derivatives and a constant time-delay in the spatial derivative. Solutions are obtained, for arbitrary separable initial conditions, by incorporating recently introduced delay functions in a separation of variables approach. Examples are provided showing oscillatory and translatory behaviours that are fundamentally different to standard propagating wave solutions., Comment: Letter

Do neural networks learn to implement algorithms such as look-ahead or search "in the wild"? Or do they rely purely on collections of simple heuristics? We present evidence of learned look-ahead in the policy network of Leela Chess Zero, the currently strongest neural chess engine. We find that Leela internally represents future optimal moves and that these representations are crucial for its final output in certain board states. Concretely, we exploit the fact that Leela is a transformer that treats every chessboard square like a token in language models, and give three lines of evidence (1) activations on certain squares of future moves are unusually important causally; (2) we find attention heads that move important information "forward and backward in time," e.g., from squares of future moves to squares of earlier ones; and (3) we train a simple probe that can predict the optimal move 2 turns ahead with 92% accuracy (in board states where Leela finds a single best line). These findings are an existence proof of learned look-ahead in neural networks and might be a step towards a better understanding of their capabilities., Comment: Project page: https://leela-interp.github.io/

Large language models generate code one token at a time. Their autoregressive generation process lacks the feedback of observing the program's output. Training LLMs to suggest edits directly can be challenging due to the scarcity of rich edit data. To address these problems, we propose neural diffusion models that operate on syntax trees of any context-free grammar. Similar to image diffusion models, our method also inverts ``noise'' applied to syntax trees. Rather than generating code sequentially, we iteratively edit it while preserving syntactic validity, which makes it easy to combine this neural model with search. We apply our approach to inverse graphics tasks, where our model learns to convert images into programs that produce those images. Combined with search, our model is able to write graphics programs, see the execution result, and debug them to meet the required specifications. We additionally show how our system can write graphics programs for hand-drawn sketches., Comment: https://tree-diffusion.github.io

Since volcanic activity was first discovered on Io from Voyager images in 1979, changes on Io's surface have been monitored from both spacecraft and ground-based telescopes. Here, we present the highest spatial resolution images of Io ever obtained from a ground-based telescope. These images, acquired by the SHARK-VIS instrument on the Large Binocular Telescope, show evidence of a major resurfacing event on Io's trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images show that a plume deposit from a powerful eruption at Pillan Patera has covered part of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io's surface using adaptive optics at visible wavelengths., Comment: 15 pages, 4 figures