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Stellar evolution modelling is fundamental to many areas of astrophysics including stellar populations in both nearby and distant galaxies. It is heavily influenced by chemical composition. Observations of distant galaxies and nucleosynthesis calculations show that $\alpha$-process elements are enriched faster than iron group elements. We present a dense grid of single-star models calculated using the BPASS stellar evolution code and covering masses ($0.1\le\mathrm{M/M}_\odot\le316$), metallicity mass fractions ($10^{-5} \le Z \le 0.04$) and $\alpha$-to-iron abundance ratios ($-0.2\le[\alpha/\mathrm{Fe}]\le+0.6$). By comparing Solar-scaled models to ones enriched in $\alpha$-process elements, we find that stellar radii, surface temperatures, Main Sequence lifetimes, supernova progenitor properties and supernova rates are all sensitive to changes in [$\alpha$/Fe]. Lifetimes of low-mass stars differ by up to 0.4 dex, while surface temperatures of massive stars at the end of the Main Sequence also differ by around 0.4 dex. Inferred supernova rates when [Fe/H] is unknown can be highly uncertain. Models with different [$\alpha$/Fe] but comparable iron abundances show smaller variations, indicating that while iron primarily defines the course of evolution; $\alpha$-enhancement nonetheless has an impact of up to 0.1 dex on stellar properties. Such changes are small for individual stars, but have a large cumulative effect when considering an entire stellar population as demonstrated by isochrone fitting to nearby clusters. Changes in radii and lifetimes have further consequences for a stellar population including binary stars, as they influence the timing, nature and occurrence rate of mass transfer events., Comment: Submitted to MNRAS on 01 August 2024. 18 pages, 12 figures, 1 appendix with 6 supplementary figures

As a new generation of large-sky spectroscopic surveys comes online, the enormous data volume poses unprecedented challenges in classifying spectra. Modern unsupervised techniques have the power to group spectra based on their dominant features, circumventing the complete reliance on training data suffered by supervised methods. We outline the use of dimensionality reduction to generate a 2D map of the structure of an intermediate-resolution spectroscopic dataset. This technique efficiently separates white dwarfs of different spectral classes in the Dark Energy Spectroscopic Instrument's Early Data Release (DESI EDR), identifying spectral features that had been missed even by visual classification. By focusing the method on particular spectral regions, we identify white dwarfs with helium features at 90 per cent recall, and cataclysmic variables at 100 per cent recall, illustrating rapid selection of low-contamination samples from spectroscopic surveys. We also demonstrate the use of dimensionality reduction in a supervised manner, outlining a procedure to classify any white dwarf spectrum in comparison with those in the DESI EDR. With upcoming surveys promising tens of millions of spectra, our work highlights the potential for semi-supervised techniques as an efficient means of classification and dataset visualisation., Comment: 15 pages, 8 figures. Accepted for publication in MNRAS

The circumgalactic medium (CGM) in $\gtrsim 10^{12}$ $\mathrm{M}_{\odot}$ halos is dominated by a hot phase ($T \gtrsim 10^{6}$ K). While many models exist for the hot gas structure, there is as yet no consensus. We compare cooling flow models, in which the hot CGM flows inward due to radiative cooling, to the CGM of $\sim 10^{12}-10^{13}$ $\mathrm{M}_{\odot}$ halos in galaxy formation simulations from the FIRE project at $z\sim0$. The simulations include realistic cosmological evolution and feedback from stars but neglect AGN feedback. At both mass scales, CGM inflows are typically dominated by the hot phase rather than by the `precipitation' of cold gas. Despite being highly idealized, we find that cooling flows describe $\sim 10^{13}$ $\mathrm{M}_{\odot}$ halos very well, with median agreement in the density and temperature profiles of $\sim 20\%$ and $\sim 10\%$, respectively. This indicates that stellar feedback has little impact on CGM scales in those halos. For $\sim 10^{12}$ $\mathrm{M}_{\odot}$ halos, the thermodynamic profiles are also accurately reproduced in the outer CGM. For some of these lower-mass halos, cooling flows significantly overpredict the hot gas density in the inner CGM. This could be due to multidimensional angular momentum effects not well captured by our 1D cooling flow models and/or to the larger cold gas fractions in these regions. Turbulence, which contributes $\sim 10-40\%$ of the total pressure, must be included to accurately reproduce the temperature profiles. Overall, cooling flows predict entropy profiles in better agreement with the FIRE simulations than other idealized models in the literature., Comment: 20 pages, 14 figures. Submitted to MNRAS

We introduce the concept of a sum-rank saturating system and outline its correspondence to a covering properties of a sum-rank metric code. We consider the problem of determining the shortest sum-rank-$\rho$-saturating systems of a fixed dimension, which is equivalent to the covering problem in the sum-rank metric. We obtain upper and lower bounds on this quantity. We also give constructions of saturating systems arising from geometrical structures.

The increasingly large number of complex organic molecules detected in the interstellar medium necessitates robust kinetic models that can be relied upon for investigating the involved chemical processes. Such models require rate constants for each of the thousands of reactions; the values of these are often estimated or extrapolated, leading to large uncertainties that are rarely quantified. We have performed a global Monte Carlo and a more local one-at-a-time sensitivity analysis on the gas-phase rate coefficients in a 3-phase dark cloud model. Time-dependent sensitivities have been calculated using four metrics to determine key reactions for the overall network as well as for the cyanonaphthalene molecule in particular, an important interstellar species that is severely under-produced by current models. All four metrics find that reactions involving small, reactive species that initiate hydrocarbon growth have large effects on the overall network. Cyanonaphthalene is most sensitive to a number of these reactions as well as ring-formation of the phenyl cation (C6H5+) and aromatic growth from benzene to naphthalene. Future efforts should prioritize constraining rate coefficients of key reactions and expanding the network surrounding these processes. These results highlight the strength of sensitivity analysis techniques to identify critical processes in complex chemical networks, such as those often used in astrochemical modeling., Comment: 21 pages and 13 figures, accepted for publication in Physical Chemistry Chemical Physics

We explore the transformative potential of SAM 2, a vision foundation model, in advancing gaze estimation and eye tracking technologies. By significantly reducing annotation time, lowering technical barriers through its ease of deployment, and enhancing segmentation accuracy, SAM 2 addresses critical challenges faced by researchers and practitioners. Utilizing its zero-shot segmentation capabilities with minimal user input-a single click per video-we tested SAM 2 on over 14 million eye images from diverse datasets, including virtual reality setups and the world's largest unified dataset recorded using wearable eye trackers. Remarkably, in pupil segmentation tasks, SAM 2 matches the performance of domain-specific models trained solely on eye images, achieving competitive mean Intersection over Union (mIoU) scores of up to 93% without fine-tuning. Additionally, we provide our code and segmentation masks for these widely used datasets to promote further research., Comment: Virmarie Maquiling and Sean Anthony Byrne contributed equally to this paper, 8 pages, 3 figures, CHI Case Study, pre-print

Long-form question answering (LFQA) aims at generating in-depth answers to end-user questions, providing relevant information beyond the direct answer. However, existing retrievers are typically optimized towards information that directly targets the question, missing out on such contextual information. Furthermore, there is a lack of training data for relevant context. To this end, we propose and compare different weak supervision techniques to optimize retrieval for contextual information. Experiments demonstrate improvements on the end-to-end QA performance on ASQA, a dataset for long-form question answering. Importantly, as more contextual information is retrieved, we improve the relevant page recall for LFQA by 14.7% and the groundedness of generated long-form answers by 12.5%. Finally, we show that long-form answers often anticipate likely follow-up questions, via experiments on a conversational QA dataset., Comment: Accepted at EMNLP 2024 (Findings)

Galaxy mergers can enhance star formation rates throughout the merger sequence, with this effect peaking around the time of coalescence. However, owing to a lack of information about their time of coalescence, post-mergers could only previously be studied as a single, time-averaged population. We use timescale predictions of post-coalescence galaxies in the UNIONS survey, based on the Multi-Model Merger Identifier deep learning framework (\textsc{Mummi}) that predicts the time elapsed since the last merging event. For the first time, we capture a complete timeline of star formation enhancements due to galaxy mergers by combining these post-merger predictions with data from pre-coalescence galaxy pairs in SDSS. Using a sample of $564$ galaxies with $M_* \geq 10^{10} M_\odot$ at $0.005 < z < 0.3$ we demonstrate that: 1) galaxy mergers enhance star formation by, on average, up to a factor of two; 2) this enhancement peaks within 500 Myr of coalescence; 3) enhancements continue for up to 1~Gyr after coalescence; and 4) merger-induced star formation significantly contributes to galaxy mass assembly, with galaxies increasing their final stellar masses by, $10\%$ to $20\%$ per merging event, producing on average $\log(M_*/M_\odot) = {9.56_{-0.19}^{+0.13}}$ more mass than non-interacting star-forming galaxies solely due to the excess star formation., Comment: Letter Submitted to MNRAS, 6 pages, 4 figures. For a temporal view of quenching in the post-merger regime see companion paper Ellison et al. 2024

The focused helium ion beam microscope is a versatile imaging and nanofabrication instrument enabling direct-write lithography with sub-10-nm resolution. Subsurface damage and swelling of substrates due to helium ion implantation is generally unwanted. However, these effects can also be leveraged for specific nanofabrication tasks. To explore this, we investigate focused helium ion beam induced swelling of bulk crystalline silicon and free-standing amorphous silicon nitride membranes using various irradiation strategies. We show that the creation of near-surface voids due to helium ion implantation can be used to induce surface nanostructure and create subsurface nanochannels. By tailoring the ion dose and beam energy, the size and depth of the swollen features can be controlled. Swelling heights of several hundred nanometers are demonstrated and for the embedded nanochannels, void internal diameters down to 30nm are shown. Potential applications include the engineering of texturized substrates and the prototyping of on-chip nanofluidic transport devices.

The logistic equation is ubiquitous in applied mathematics as a minimal model of saturating growth. Here, we examine a generalisation of the logistic model to fitness-structured populations, motivated by examples that range from the ageing of individuals in a species to immune cell exhaustion by cancerous tissue. Through exploration of a range of concrete examples and a general analysis of polynomial fitness functions, we derive necessary and sufficient conditions for the dependence of the kinetics on structure to result in closed, low-dimensional moment equations that are exact. Further, we showcase how coarse-grained moment information can be used to elucidate the details of structured dynamics, with immediate potential for model selection and hypothesis testing.

Polycyclic aromatic hydrocarbons (PAHs) are among the most ubiquitous compounds in the universe, accounting for up to ~25% of all interstellar carbon. Since most unsubstituted PAHs do not possess permanent dipole moments, they are invisible to radio astronomy. Constraining their abundances relies on the detection of polar chemical proxies, such as aromatic nitriles. We report the detection of 2- and 4-cyanopyrene, isomers of the recently detected 1-cyanopyrene. We find that these isomers are present in an abundance ratio of ~2:1:2, which mirrors the number of equivalent sites available for CN addition. We conclude that there is evidence that the cyanopyrene isomers formed by direct CN addition to pyrene under kinetic control in hydrogen-rich gas at 10 K and discuss constraints on the H/CN ratio for PAHs in TMC-1., Comment: Submitted version to comply with licensing agreements

Polycyclic aromatic hydrocarbons (PAHs) are expected to be the most abundant class of organic molecules in space. Their interstellar lifecycle is not well understood, and progress is hampered by difficulties detecting individual PAH molecules. Here, we present the discovery of CN-functionalized pyrene, a 4-ring PAH, in the dense cloud TMC-1 using the 100-m Green Bank Telescope. We derive an abundance of 1-cyanopyrene of ~1.52 x $10^{12}$ cm$^{-2}$, and from this estimate that the un-substituted pyrene accounts for up to ~0.03-0.3% of the carbon budget in the dense interstellar medium which trace the birth sites of stars and planets. The presence of pyrene in this cold (~10 K) molecular cloud agrees with its recent measurement in asteroid Ryugu where isotopic clumping suggest a cold, interstellar origin. The direct link to the birth site of our solar system is strengthened when we consider the solid state pyrene content in the pre-stellar materials compared to comets, which represent the most pristine material in the solar system. We estimate that solid state pyrene can account for 1% of the carbon within comets carried by this one single organic molecule. The abundance indicates pyrene is an "island of stability" in interstellar PAH chemistry and suggests a potential cold molecular cloud origin of the carbon carried by PAHs that is supplied to forming planetary systems, including habitable worlds such as our own., Comment: Version of manuscript revised to comply with licensing requirements

We derive and investigate two DPO variants that explicitly model the possibility of declaring a tie in pair-wise comparisons. We replace the Bradley-Terry model in DPO with two well-known modeling extensions, by Rao and Kupper and by Davidson, that assign probability to ties as alternatives to clear preferences. Our experiments in neural machine translation and summarization show that explicitly labeled ties can be added to the datasets for these DPO variants without the degradation in task performance that is observed when the same tied pairs are presented to DPO. We find empirically that the inclusion of ties leads to stronger regularization with respect to the reference policy as measured by KL divergence, and we see this even for DPO in its original form. These findings motivate and enable the inclusion of tied pairs in preference optimization as opposed to simply discarding them., Comment: 24 pages

Epigenetics plays a key role in cellular differentiation and maintaining cell identity, enabling cells to regulate their genetic activity without altering the DNA sequence. Epigenetic regulation occurs within the context of hierarchically folded chromatin, yet the interplay between the dynamics of epigenetic modifications and chromatin architecture remains poorly understood. In addition, it remains unclear what mechanisms drive the formation of rugged epigenetic patterns, characterised by alternating genomic regions enriched in activating and repressive marks. In this study, we focus on post-translational modifications of histone H3 tails, particularly H3K27me3, H3K4me3, and H3K27ac. We introduce a mesoscopic stochastic model that incorporates chromatin architecture and competition of histone-modifying enzymes into the dynamics of epigenetic modifications in small genomic loci comprising several nucleosomes. Our approach enables us to investigate the mechanisms by which epigenetic patterns form on larger scales of chromatin organisation, such as loops and domains. Through bifurcation analysis and stochastic simulations, we demonstrate that the model can reproduce uniform chromatin states (open, closed, and bivalent) and generate previously unexplored rugged profiles. Our results suggest that enzyme competition and chromatin conformations with high-frequency interactions between distant genomic loci can drive the emergence of rugged epigenetic landscapes. Additionally, we hypothesise that bivalent chromatin can act as an intermediate state, facilitating transitions between uniform and rugged landscapes. This work offers a powerful mathematical framework for understanding the dynamic interactions between chromatin architecture and epigenetic regulation, providing new insights into the formation of complex epigenetic patterns., Comment: 39 pages, graphical abstract, 8 figures, 1 table. Submitted for publication to the Journal of Theoretical Biology

Nanopores in 2D materials are of significant interest in advanced membrane technologies aimed at the sensing and separation of ions and molecules. These applications necessitate 2D nanopores that are precise in size and shape, and abundant in number. However, conventional fabrication techniques often struggle to achieve both high precision and throughput. In this study, we introduce a decoupled seeding and growth approach designed to overcome this limitation. The method allows the controlled fabrication of ensembles of nanopores with narrow size distribution and is demonstrated for free-standing monolayer hexagonal boron nitride. Using light ion showering, we first create vacancy defect seeds. These seeds are then expanded into triangular nanopores through element-specific preferential atom removal under broad-beam electron irradiation in a transmission electron microscope. Nanopore density and size are controlled by the ion and electron irradiation doses, respectively. During the electron irradiation step, high-resolution imaging allows real-time tracking of the nanopore formation process, enabling the highest level of control. An additional workflow is introduced using thermal annealing in air for the nanopore growth step, delivering the most flexible platform for nanopore fabrication over larger areas with the significant benefit of concurrently removing surface hydrocarbon contamination to mitigate pore clogging and distortion. This study provides researchers with a novel approach to create ensembles of meticulously designed nanopores with well-controlled size and geometry, facilitating the development of next-generation membrane devices that will demand high precision and high throughput nanopore fabrication pipelines.

Synaptic plasticity is a key component of neuronal dynamics, describing the process by which the connections between neurons change in response to experiences. In this study, we extend a network model of $\theta$-neuron oscillators to include a realistic form of adaptive plasticity. In place of the less tractable spike-timing-dependent plasticity, we employ recently validated phase-difference-dependent plasticity rules, which adjust coupling strengths based on the relative phases of $\theta$-neuron oscillators. We investigate two approaches for implementing this plasticity: pairwise coupling strength updates and global coupling strength updates. A mean-field approximation of the system is derived and we investigate its validity through comparison with the $\theta$-neuron simulations across various stability states. The synchrony of the system is examined using the Kuramoto order parameter. A bifurcation analysis, by means of numerical continuation and the calculation of maximal Lyapunov exponents, reveals interesting phenomena, including bistability and evidence of period-doubling and boundary crisis routes to chaos, that would otherwise not exist in the absence of adaptive coupling.

The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionisation (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Array (HERA), aimed at systematically mapping this emission for the first time. Precision calibration, however, is a requirement in 21 cm radio observations. Due to the spatial compactness of HERA, the array is prone to the effects of mutual coupling, which inevitably lead to non-smooth calibration errors that contaminate the data. When unsmooth gains are used in calibration, intrinsically spectrally-smooth foreground emission begins to contaminate the data in a way that can prohibit a clean detection of the cosmological EoR signal. In this paper, we show that the effects of mutual coupling on calibration quality can be reduced by applying custom time-domain filters to the data prior to calibration. We find that more robust calibration solutions are derived when filtering in this way, which reduces the observed foreground power leakage. Specifically, we find a reduction of foreground power leakage by 2 orders of magnitude at k=0.5.

A number of recent articles have employed the Lorentz ansatz to reduce a network of Izhikevich neurons to a tractable mean-field description. In this letter, we construct an equivalent phase model for the Izhikevich model and apply the Ott-Antonsen ansatz, to derive the mean field dynamics in terms of the Kuramoto order parameter. In addition, we show that by defining an appropriate order parameter in the voltage-firing rate framework, the conformal mapping of Montbri\'o et al., which relates the two mean-field descriptions, remains valid.

Merging and interactions can radically transform galaxies. However, identifying these events based solely on structure is challenging as the status of observed mergers is not easily accessible. Fortunately, cosmological simulations are now able to produce more realistic galaxy morphologies, allowing us to directly trace galaxy transformation throughout the merger sequence. To advance the potential of observational analysis closer to what is possible in simulations, we introduce a supervised deep learning Convolutional Neural Network (CNN) and Vision Transformer (ViT) hybrid framework, Mummi (MUlti Model Merger Identifier). Mummi is trained on realism-added synthetic data from IllustrisTNG100-1, and is comprised of a multi-step ensemble of models to identify mergers and non-mergers, and to subsequently classify the mergers as interacting pairs or post-mergers. To train this ensemble of models, we generate a large imaging dataset of 6.4 million images targeting UNIONS with RealSimCFIS. We show that Mummi offers a significant improvement over many previous machine learning classifiers, achieving 95% pure classifications even at Gyr long timescales when using a jury-based decision making process, mitigating class imbalance issues that arise when identifying real galaxy mergers from $z=0$ to $0.3$. Additionally, we can divide the identified mergers into pairs and post-mergers at 96% success rate. We drastically decrease the false positive rate in galaxy merger samples by 75%. By applying Mummi to the UNIONS DR5-SDSS DR7 overlap, we report a catalog of 13,448 high confidence galaxy merger candidates. Finally, we demonstrate that Mummi produces powerful representations solely using supervised learning, which can be used to bridge galaxy morphologies in simulations and observations., Comment: 23 pages, 16 figures. Accepted for publication in MNRAS

Tensor networks and quantum computation are two of the most powerful tools for the simulation of quantum many-body systems. Rather than viewing them as competing approaches, here we consider how these two methods can work in tandem. We introduce a novel algorithm that combines tensor networks and quantum computation to produce results that are more accurate than what could be achieved by either method used in isolation. Our algorithm is based on multiproduct formulas (MPF) - a technique that linearly combines Trotter product formulas to reduce algorithmic error. Our algorithm uses a quantum computer to calculate the expectation values and tensor networks to calculate the coefficients used in the linear combination. We present a detailed error analysis of the algorithm and demonstrate the full workflow on a one-dimensional quantum simulation problem on $50$ qubits using two IBM quantum computers: $ibm\_torino$ and $ibm\_kyiv$.

We show that interactions between axion-like particles (ALPs) and co-dimension one defects, such as phase-transition bubble walls and solitonic domain walls, can lead to important changes in the evolution of both walls and ALPs. The leading effect arises from the change in the ALP decay constant across the interface, which naturally follows from shift-symmetric interactions with the corresponding order parameter. Specifically, we show that for thin walls moving relativistically, an ALP background -- such as e.g. axion dark matter -- gives rise to a frictional force on the interface that is proportional to $\gamma^2$, with $\gamma$ the Lorentz factor of the wall, and that this effect is present in both the oscillating and frozen axion regimes. We explore the broader consequences of this effect for bubble and domain walls in the early universe, and show that this source of friction can be present even in the absent of a conventional medium such as radiation or matter. Possible implications include modifications to the dynamics of bubble and domain walls and their corresponding gravitational wave signatures, as well as the generation of a dark radiation component of ALPs in the form of ultra-relativistic `axion shells' with Lorentz factor $\gamma_\text{shell} \simeq 2\gamma^2 \gg 1$ that may remain relativistic until the present day., Comment: 35 pages, 5 figures

The development of a colloidal synthesis procedure to produce nanomaterials of a specific size with high shape and size purity is often a time consuming, iterative process. This is often due to the time, resource and expertise intensive characterization methods required for quantitative determination of nanomaterial size and shape. Absorption spectroscopy is often the easiest method of colloidal nanomaterial characterization, however, due to the lack of a reliable method to extract nanoparticle shapes from absorption spectroscopy, it is generally treated as a more qualitative measure for metal nanoparticles. This work demonstrates a gold nanorod (AuNR) spectral morphology analysis (SMA) tool, AuNR-SMA, which is a fast and accurate method to extract quantitative information about an AuNR sample's structural parameters from its absorption spectra. We apply AuNR-SMA in three distinct applications. First, we demonstrate its utility as an automated analysis tool in a high throughput AuNR synthesis procedure by generating quantitative size information from optical spectra. Second, we use the predictions generated by this model to train a machine learning model capable of predicting the resulting AuNR size distributions from the reaction conditions used to synthesize them. Third, we turn this model to spectra extracted from the literature where no size distributions are reported to impute unreported quantitative information of AuNR synthesis. This approach can potentially be extended to any other nanocrystal system where the absorption spectra are size dependent and accurate numerical simulation of the absorption spectra is possible. In addition, this pipeline could be integrated into automated synthesis apparatuses to provide interpretable data from simple measurements and help explore the synthesis science of nanoparticles in a rational manner or facilitate closed-loop workflows.

Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation have been proposed which allow for using new input data without retraining the model. They could also dramatically accelerate the costly data assimilation process used in operational regional weather models. Here, in a central US testbed, we demonstrate the viability of score-based data assimilation in the context of realistically complex km-scale weather. We train an unconditional diffusion model to generate snapshots of a state-of-the-art km-scale analysis product, the High Resolution Rapid Refresh. Then, using score-based data assimilation to incorporate sparse weather station data, the model produces maps of precipitation and surface winds. The generated fields display physically plausible structures, such as gust fronts, and sensitivity tests confirm learnt physics through multivariate relationships. Preliminary skill analysis shows the approach already outperforms a naive baseline of the High-Resolution Rapid Refresh system itself. By incorporating observations from 40 weather stations, 10\% lower RMSEs on left-out stations are attained. Despite some lingering imperfections such as insufficiently disperse ensemble DA estimates, we find the results overall an encouraging proof of concept, and the first at km-scale. It is a ripe time to explore extensions that combine increasingly ambitious regional state generators with an increasing set of in situ, ground-based, and satellite remote sensing data streams., Comment: 18 pages, 7 figures

Interferometric experiments designed to detect the highly redshifted 21-cm signal from neutral hydrogen are producing increasingly stringent constraints on the 21-cm power spectrum, but some k-modes remain systematics-dominated. Mutual coupling is a major systematic that must be overcome in order to detect the 21-cm signal, and simulations that reproduce effects seen in the data can guide strategies for mitigating mutual coupling. In this paper, we analyse 12 nights of data from the Hydrogen Epoch of Reionization Array and compare the data against simulations that include a computationally efficient and physically motivated semi-analytic treatment of mutual coupling. We find that simulated coupling features qualitatively agree with coupling features in the data; however, coupling features in the data are brighter than the simulated features, indicating the presence of additional coupling mechanisms not captured by our model. We explore the use of fringe-rate filters as mutual coupling mitigation tools and use our simulations to investigate the effects of mutual coupling on a simulated cosmological 21-cm power spectrum in a "worst case" scenario where the foregrounds are particularly bright. We find that mutual coupling contaminates a large portion of the "EoR Window", and the contamination is several orders-of-magnitude larger than our simulated cosmic signal across a wide range of cosmological Fourier modes. While our fiducial fringe-rate filtering strategy reduces mutual coupling by roughly a factor of 100 in power, a non-negligible amount of coupling cannot be excised with fringe-rate filters, so more sophisticated mitigation strategies are required., Comment: 19 pages, 12 figures, submitted to MNRAS

A detailed analysis of ptychography for 3D phase reconstructions of thick specimens is performed. We introduce multi-focus ptychography, which incorporates a 4D-STEM defocus series to enhance the quality of 3D reconstructions along the beam direction through a higher overdetermination ratio. This method is compared with established multi-slice ptychography techniques, such as conventional ptychography, regularized ptychography, and multi-mode ptychography. Additionally, we contrast multi-focus ptychography with an alternative method that uses virtual optical sectioning through a reconstructed scattering matrix ($\mathcal{S}$-matrix), which offers more precise 3D structure information compared to conventional ptychography. Our findings from multiple 3D reconstructions based on simulated and experimental data demonstrate that multi-focus ptychography surpasses other techniques, particularly in accurately reconstructing the surfaces and interface regions of thick specimens.

Fine-tuning large language models (LLMs) for machine translation has shown improvements in overall translation quality. However, it is unclear what is the impact of fine-tuning on desirable LLM behaviors that are not present in neural machine translation models, such as steerability, inherent document-level translation abilities, and the ability to produce less literal translations. We perform an extensive translation evaluation on the LLaMA and Falcon family of models with model size ranging from 7 billion up to 65 billion parameters. Our results show that while fine-tuning improves the general translation quality of LLMs, several abilities degrade. In particular, we observe a decline in the ability to perform formality steering, to produce technical translations through few-shot examples, and to perform document-level translation. On the other hand, we observe that the model produces less literal translations after fine-tuning on parallel data. We show that by including monolingual data as part of the fine-tuning data we can maintain the abilities while simultaneously enhancing overall translation quality. Our findings emphasize the need for fine-tuning strategies that preserve the benefits of LLMs for machine translation., Comment: Accepted to ACL 2024 (long, main). Latest version includes link to the IdiomsInCtx-MT dataset

Interactions between bubbles/domain walls and the surrounding medium are a topic of active research, particularly as they apply to friction effects on accelerated expansion during first-order phase transitions. In this paper, we analyze for the first time friction pressure on relativistic walls in phase transitions where gauge symmetry is restored, particularly motivated by the observation that this pressure can, in principle, be negative at leading order, since some particles lose mass by definition as they cross into the new phase. We find, however, that at NLO, the soft emission of vectors from a charged current leads to positive pressure scaling as the wall's Lorentz boost factor $\gamma_w$, similar to the case of gauge symmetry breaking. Contrary to the latter case, we find that the dominant contribution in single emission is safe from IR divergences and exhibits a much stronger dependence on the wall shape. Finally, we argue that in any phase transition, no multi-particle process on the wall can impart negative pressure greater than the leading order result, in the asymptotic limit of large velocity., Comment: 17 pages + appendices

Recent clinical xenotransplantation and human decedent studies demonstrate that clinical hyperacute rejection of genetically engineered porcine organs can be reliably avoided but that antibody mediated rejection continues to limit graft survival. We previously identified porcine glycans and proteins which are immunogenic after cardiac xenotransplantation in nonhuman primates, but the clinical immune response to antigens present in glycan depleted triple knockout (TKO) donor pigs is poorly understood. In this study we use fluorescence barcoded HEK cells and HEK cell lines expressing porcine glycans (Gal and SDa) or proteins (CD9, CD46, CD59, PROCR and ANXA2) to screen antibody reactivity in human serum from 160 swine veterinarians, a serum source with potential occupational immune challenge from porcine tissues and pathogens. High levels of anti-Gal IgM were present in all samples and lower levels of anti-SDa IgM were present in 41% of samples. IgM binding to porcine proteins, primarily CD9 and CD46, previously identified as immunogenic in pig to non-human primate cardiac xenograft recipients, was detected in 28 of the 160 swine veterinarian samples. These results suggest that barcoded HEK cell lines expressing porcine protein antigens can be useful for screening human patient serum. A comprehensive analysis of sera from clinical xenotransplant recipients to define a panel of commonly immunogenic porcine antigens will likely be necessary to establish an array of porcine non-Gal antigens for effective monitoring of patient immune responses and allow earlier therapies to reverse antibody mediated rejection., Comment: 13 pages, 2 Tables, 3 Figures

Recent research has shown that large language models (LLMs) can achieve remarkable translation performance through supervised fine-tuning (SFT) using only a small amount of parallel data. However, SFT simply instructs the model to imitate the reference translations at the token level, making it vulnerable to the noise present in the references. Hence, the assistance from SFT often reaches a plateau once the LLMs have achieved a certain level of translation capability, and further increasing the size of parallel data does not provide additional benefits. To overcome this plateau associated with imitation-based SFT, we propose a preference-based approach built upon the Plackett-Luce model. The objective is to steer LLMs towards a more nuanced understanding of translation preferences from a holistic view, while also being more resilient in the absence of gold translations. We further build a dataset named MAPLE to verify the effectiveness of our approach, which includes multiple translations of varying quality for each source sentence. Extensive experiments demonstrate the superiority of our approach in "breaking the plateau" across diverse LLMs and test settings. Our in-depth analysis underscores the pivotal role of diverse translations and accurate preference scores in the success of our approach., Comment: Accepted to NAACL 2024 (long, main)

Macrophages in atherosclerotic lesions exhibit a spectrum of behaviours or phenotypes. The phenotypic distribution of monocyte-derived macrophages (MDMs), its correlation with MDM lipid content, and relation to blood lipoprotein densities are not well understood. Of particular interest is the balance between low density lipoproteins (LDL) and high density lipoproteins (HDL), which carry bad and good cholesterol respectively. To address these issues, we have developed a mathematical model for early atherosclerosis in which the MDM population is structured by phenotype and lipid content. The model admits a simpler, closed subsystem whose analysis shows how lesion composition becomes more pathological as the blood density of LDL increases relative to the HDL capacity. We use asymptotic analysis to derive a power-law relationship between MDM phenotype and lipid content at steady-state. This relationship enables us to understand why, for example, lipid-laden MDMs have a more inflammatory phenotype than lipid-poor MDMs when blood LDL lipid density greatly exceeds HDL capacity. We show further that the MDM phenotype distribution always attains a local maximum, while the lipid content distribution may be unimodal, adopt a quasi-uniform profile or decrease monotonically. Pathological lesions exhibit a local maximum in both the phenotype and lipid content MDM distributions, with the maximum at an inflammatory phenotype and near the lipid content capacity respectively. These results illustrate how macrophage heterogeneity arises in early atherosclerosis and provide a framework for future model validation through comparison with single-cell RNA sequencing data.

The Directed Acyclic Transformer is a fast non-autoregressive (NAR) model that performs well in Neural Machine Translation. Two issues prevent its application to general Natural Language Generation (NLG) tasks: frequent Out-Of-Vocabulary (OOV) errors and the inability to faithfully generate entity names. We introduce Control-DAG, a constrained decoding algorithm for our Directed Acyclic T5 (DA-T5) model which offers lexical, vocabulary and length control. We show that Control-DAG significantly enhances DA-T5 on the Schema Guided Dialogue and the DART datasets, establishing strong NAR results for Task-Oriented Dialogue and Data-to-Text NLG., Comment: 11 pages. NAACL 2024

Sidewalk robots are increasingly common across the globe. Yet, their operation on public paths poses challenges for people with mobility disabilities (PwMD) who face barriers to accessibility, such as insufficient curb cuts. We interviewed 15 PwMD to understand how they perceive sidewalk robots. Findings indicated that PwMD feel they have to compete for space on the sidewalk when robots are introduced. We next interviewed eight robotics practitioners to learn about their attitudes towards accessibility. Practitioners described how issues often stem from robotic companies addressing accessibility only after problems arise. Both interview groups underscored the importance of integrating accessibility from the outset. Building on this finding, we held four co-design workshops with PwMD and practitioners in pairs. These convenings brought to bear accessibility needs around robots operating in public spaces and in the public interest. Our study aims to set the stage for a more inclusive future around public service robots.

Heavy metal subdwarfs are a class of hot subdwarfs with very high abundances of heavy elements, typically around 10 000 times solar. They include stars which are strongly enhanced in either lead or zirconium, as well as other elements. Vertical stratification of the enhanced elements, where the element is concentrated in a thin layer of the atmosphere, has been proposed as a mechanism to explain the apparent high abundances. This paper explores the effects of the vertical stratification of lead on theoretical spectra of hot subdwarfs. The concentration of lead in different regions of the model atmosphere is found to affect individual lines in a broadly wavelength-dependent manner, with the potential for lines to display modified profiles depending on the location of lead enhancement in the atmosphere. This wavelength dependence highlights the importance of observations in both the optical and the UV for determining whether stratification is present in real stars., Comment: 11 pages, 11 figures, accepted for publication in MNRAS

Of the hundreds of $z\gtrsim6$ quasars discovered to date, only one is known to be gravitationally lensed, despite the high lensing optical depth expected at $z\gtrsim6$. High-redshift quasars are typically identified in large-scale surveys by applying strict photometric selection criteria, in particular by imposing non-detections in bands blueward of the Lyman-$\alpha$ line. Such procedures by design prohibit the discovery of lensed quasars, as the lensing foreground galaxy would contaminate the photometry of the quasar. We present a novel quasar selection methodology, applying contrastive learning (an unsupervised machine learning technique) to Dark Energy Survey imaging data. We describe the use of this technique to train a neural network which isolates an 'island' of 11 sources, of which 7 are known $z\sim6$ quasars. Of the remaining four, three are newly discovered quasars (J0109-5424, $z=6.07$; J0122-4609, $z=5.99$; J0603-3923, $z=5.94$), as confirmed by follow-up Gemini-South/GMOS and archival NTT/EFOSC2 spectroscopy, implying a 91 per cent efficiency for our novel selection method; the final object on the island is a brown dwarf. In one case (J0109-5424), emission below the Lyman limit unambiguously indicates the presence of a foreground source, though high-resolution optical/near-infrared imaging is still needed to confirm the quasar's lensed (multiply-imaged) nature. Detection in the g band has led this quasar to escape selection by traditional colour cuts. Our findings demonstrate that machine learning techniques can thus play a key role in unveiling populations of quasars missed by traditional methods., Comment: 11 pages, 5 figures, 4 tables. Accepted 26 March 2024 for publication in MNRAS

We use both chirped-pulse Fourier transform and frequency modulated absorption spectroscopy to study the rotational spectrum of 2-methoxyethanol in several frequency regions ranging from 8.7-500 GHz. The resulting rotational parameters permitted a search for this molecule in Atacama Large Millimeter/submillimeter Array (ALMA) observations toward the massive protocluster NGC 6334I as well as source B of the low-mass protostellar system IRAS 16293-2422. 25 rotational transitions are observed in the ALMA Band 4 data toward NGC 6334I, resulting in the first interstellar detection of 2-methoxyethanol. A column density of $1.3_{-0.9}^{+1.4} \times 10^{17}$ cm$^{-2}$ is derived at an excitation temperature of $143_{-39}^{+31}$ K. However, molecular signal is not observed in the Band 7 data toward IRAS 16293-2422B and an upper limit column density of $2.5 \times 10^{15}$ cm$^{-2}$ is determined. Various possible formation pathways--including radical recombination and insertion reactions--are discussed. We also investigate physical differences between the two interstellar sources that could result in the observed abundance variations., Comment: Accepted for publication in The Astrophysical Journal Letters

Two approaches have emerged to input images into large language models (LLMs). The first is to caption images into natural language. The second is to map image feature embeddings into the domain of the LLM and pass the mapped embeddings directly to the LLM. The majority of recent few-shot multimodal work reports performance using architectures that employ variations of one of these two approaches. But they overlook an important comparison between them. We design a controlled and focused experiment to compare these two approaches to few-shot visual question answering (VQA) with LLMs. Our findings indicate that for Flan-T5 XL, a 3B parameter LLM, connecting visual embeddings directly to the LLM embedding space does not guarantee improved performance over using image captions. In the zero-shot regime, we find using textual image captions is better. In the few-shot regimes, how the in-context examples are selected determines which is better.

While Reinforcement Learning from Human Feedback (RLHF) effectively aligns pretrained Large Language and Vision-Language Models (LLMs, and VLMs) with human preferences, its computational cost and complexity hamper its wider adoption. To alleviate some of the computational burden of fine-tuning, parameter efficient methods, like LoRA were introduced. In this work, we empirically evaluate the setup of Parameter Efficient Reinforcement Learning from Human Feedback (PE-RLHF) that leverages LoRA fine-tuning for Reward Modeling, and Reinforcement Learning. We benchmark the PE-RLHF setup on six diverse datasets spanning summarization, harmless/helpful response generation, UI automation, and visual question answering in terms of effectiveness of the trained models, and the training resources required. Our findings show, for the first time, that PE-RLHF achieves comparable performance to RLHF, while significantly reducing training time (up to 90% faster for reward models, and 30% faster for RL), and memory footprint (up to 50% reduction for reward models, and 27% for RL). We provide comprehensive ablations across LoRA ranks, and model sizes for both reward modeling and reinforcement learning. By mitigating the computational burden associated with RLHF, we push for a broader adoption of PE-RLHF as an alignment technique for LLMs and VLMs.

A major focus of the planetary science and astrobiology community is the understanding of planetary habitability, including the myriad factors that control the evolution and sustainability of temperate surface environments such as that of Earth. The few substantial terrestrial planetary atmospheres within the Solar System serve as a critical resource in studying these habitability factors, from which models can be constructed for application to extrasolar planets. The recent Astronomy and Astrophysics and Planetary Science and Astrobiology Decadal Surveys both emphasise the need for an improved understanding of planetary habitability as an essential goal within the context of astrobiology. The divergence in climate evolution of Venus and Earth provides a major, accessible basis for understanding how the habitability of large rocky worlds evolves with time and what conditions limit the boundaries of habitability. Here, we argue that Venus can be considered an "anchor point" for understanding planetary habitability within the context of terrestrial planet evolution. We discuss the major factors that have influenced the respective evolutionary pathways of Venus and Earth, how these factors might be weighted in their overall influence, and the measurements that will shed further light on their impacts of these worlds' histories. We further discuss the importance of Venus with respect to both of the recent decadal surveys, and how these community consensus reports can help shape the exploration of Venus in the coming decades., Comment: 23 pages, 3 figures, 1 table, accepted for publication in Nature Astronomy

In this study, we explore the effectiveness of persuasive messages endorsing the adoption of a privacy protection technology (IoT Inspector) tailored to individuals' regulatory focus (promotion or prevention). We explore if and how regulatory fit (i.e., tuning the goal-pursuit mechanism to individuals' internal regulatory focus) can increase persuasion and adoption. We conducted a between-subject experiment (N = 236) presenting participants with the IoT Inspector in gain ("Privacy Enhancing Technology" -- PET) or loss ("Privacy Preserving Technology" -- PPT) framing. Results show that the effect of regulatory fit on adoption is mediated by trust and privacy calculus processes: prevention-focused users who read the PPT message trust the tool more. Furthermore, privacy calculus favors using the tool when promotion-focused individuals read the PET message. We discuss the contribution of understanding the cognitive mechanisms behind regulatory fit in privacy decision-making to support privacy protection.

This paper provides an introduction to quantum machine learning, exploring the potential benefits of using quantum computing principles and algorithms that may improve upon classical machine learning approaches. Quantum computing utilizes particles governed by quantum mechanics for computational purposes, leveraging properties like superposition and entanglement for information representation and manipulation. Quantum machine learning applies these principles to enhance classical machine learning models, potentially reducing network size and training time on quantum hardware. The paper covers basic quantum mechanics principles, including superposition, phase space, and entanglement, and introduces the concept of quantum gates that exploit these properties. It also reviews classical deep learning concepts, such as artificial neural networks, gradient descent, and backpropagation, before delving into trainable quantum circuits as neural networks. An example problem demonstrates the potential advantages of quantum neural networks, and the appendices provide detailed derivations. The paper aims to help researchers new to quantum mechanics and machine learning develop their expertise more efficiently., Comment: Published as a DTIC report under the title "Quantum Computing for Machine Learning - An Introduction". Distribution Statement A: Approved for Public Release. Distribution is Unlimited

Large Multimodal Models (LMMs) excel in natural language and visual understanding but are challenged by exacting tasks such as Knowledge-based Visual Question Answering (KB-VQA) which involve the retrieval of relevant information from document collections to use in shaping answers to questions. We present an extensive training and evaluation framework, M2KR, for KB-VQA. M2KR contains a collection of vision and language tasks which we have incorporated into a single suite of benchmark tasks for training and evaluating general-purpose multi-modal retrievers. We use M2KR to develop PreFLMR, a pre-trained version of the recently developed Fine-grained Late-interaction Multi-modal Retriever (FLMR) approach to KB-VQA, and we report new state-of-the-art results across a range of tasks. We also present investigations into the scaling behaviors of PreFLMR intended to be useful in future developments in general-purpose multi-modal retrievers., Comment: ACL 2024; Project page: https://preflmr.github.io/

Leveraging planning during learning and decision-making is central to the long-term development of intelligent agents. Recent works have successfully combined tree-based search methods and self-play learning mechanisms to this end. However, these methods typically face scaling challenges due to the sequential nature of their search. While practical engineering solutions can partly overcome this, they often result in a negative impact on performance. In this paper, we introduce SPO: Sequential Monte Carlo Policy Optimisation, a model-based reinforcement learning algorithm grounded within the Expectation Maximisation (EM) framework. We show that SPO provides robust policy improvement and efficient scaling properties. The sample-based search makes it directly applicable to both discrete and continuous action spaces without modifications. We demonstrate statistically significant improvements in performance relative to model-free and model-based baselines across both continuous and discrete environments. Furthermore, the parallel nature of SPO's search enables effective utilisation of hardware accelerators, yielding favourable scaling laws., Comment: Accepted to NeurIPS 2024. 34 pages, 3 main figures

We investigate the connection between the most luminous active galactic nuclei (AGN), galaxy pairs, and post-mergers in the IllustrisTNG simulation. We select galaxy pairs and post-mergers with a mass ratio between 1:10 $< \mu <$ 1:1 and a redshift between $010^{44}$ erg/s occur in $\sim $26\% of paired galaxies with a companion within 20 kpc, compared with $\sim $14\% in controls (a fractional excess of $\sim$2). The enhancement of AGN in galaxy pairs is luminosity dependent, with the highest excess (a factor of $\sim6\pm2$ at the closest separations) for AGN with $L_{\mathrm{bol}}>10^{45}$ erg/s. Additionally, pairs exhibit a modest yet statistically significant excess of luminous AGN up to $\sim$150 kpc in separation. For pairs which merge between $010^{45}$ erg/s, $\sim$55\% are interacting, compared with a 30\% interaction fraction in AGN with $10^{44}