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In experimental and observational data settings, researchers often have limited knowledge of the reasons for censored or missing outcomes. To address this uncertainty, we propose bounds on causal effects for censored outcomes, accommodating the scenario where censoring is an unobserved mixture of informative and non-informative components. Within this mixed censoring framework, we explore several assumptions to derive bounds on causal effects, including bounds expressed as a function of user-specified sensitivity parameters. We develop influence-function based estimators of these bounds to enable flexible, non-parametric, and machine learning based estimation, achieving root-n convergence rates and asymptotic normality under relatively mild conditions. We further consider the identification and estimation of bounds for other causal quantities that remain meaningful when informative censoring reflects a competing risk, such as death. We conduct extensive simulation studies and illustrate our methodology with a study on the causal effect of antipsychotic drugs on diabetes risk using a health insurance dataset.

We introduce Dynamic Information Sub-Selection (DISS), a novel framework of AI assistance designed to enhance the performance of black-box decision-makers by tailoring their information processing on a per-instance basis. Blackbox decision-makers (e.g., humans or real-time systems) often face challenges in processing all possible information at hand (e.g., due to cognitive biases or resource constraints), which can degrade decision efficacy. DISS addresses these challenges through policies that dynamically select the most effective features and options to forward to the black-box decision-maker for prediction. We develop a scalable frequentist data acquisition strategy and a decision-maker mimicking technique for enhanced budget efficiency. We explore several impactful applications of DISS, including biased decision-maker support, expert assignment optimization, large language model decision support, and interpretability. Empirical validation of our proposed DISS methodology shows superior performance to state-of-the-art methods across various applications.

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Massive contact binaries are fundamental test-beds to study properties of close binaries en route to stellar mergers. Progress in their understanding has been largely impeded due to a dearth in their observed counterparts. We accumulate 61 over-contact binary candidates from the OGLE survey of the Magellanic Clouds and form the largest known sample of such systems. We mine the OGLE-IV catalogue for B-type and earlier systems which match our synthetic light curves, and thereafter examine their properties in comparison with our binary populations. Simultaneously, we also investigate the degeneracy in the light curves between over-contact and near-contact binaries and attempt to distinguish these populations. We construct new synthetic populations of such systems from detailed $\texttt{MESA}$ models with initial parameters of M$_\textrm{total,i}=14-80\,\textrm{M}_{\odot}$ and P$_\textrm{i}=0.6-23\,$days, and compute analogous $\texttt{PHOEBE}$ light curves. We then extract OGLE systems that match our synthetic light curves and compare their numbers, and distributions in periods, absolute V-band magnitudes and colours (V-I) with our population predictions. We expect massive over-contact binaries to have smooth equal-depth light curves due to their equal-mass ($q\approx1$) and consequently, equal-temperature components. We mine 47 and 14 predominantly B-type over-contact OGLE binaries from the Large and Small Magellanic Clouds respectively, with periods of $\approx0.6-1\,$d, along with a few wider O-type systems. This sample is consistent with our theoretically expected numbers and colour-magnitude and period distributions. Additionally, we find that binaries nearing contact have periods as large as $4.5\,$d and mass ratios of $q\approx0.4-0.95$, some of which may masquerade as over-contact binaries and thereby lead to potential observational misclassifications., Comment: 14 pages, 11 figures, submitted to A\&A

In this third installment of SETI in 20xx, we very briefly and subjectively review developments in SETI in 2022. Our primary focus is 80 papers and books published or made public in 2022, which we sort into six broad categories: results from actual searches, new search methods and instrumentation, target and frequency selection, the development of technosignatures, theory of ETIs, and social aspects of SETI., Comment: Accepted to Acta Astronautica. 11pp, 1 figure

We consider the problem of droplet impact and droplet spreading on a smooth surface in the case of a viscous Newtonian fluid. We revisit the concept of the rim-lamella model, in which the droplet spreading is described by a system of ordinary differential equations (ODEs). We show that these models contain a singularity which needs to be regularized to produce smooth solutions, and we explore the different regularization techniques in detail. We adopt one particular technique for further investigation, and we use differential inequalities to derive upper and lower bounds for the maximum spreading radius $\mathcal{R}_{max}$. Without having to resort to dimensional analysis or scaling arguments, our bounds confirm the leading-order behaviour $\mathcal{R}_{max} \sim \mathrm{Re}^{1/5}$, as well as a correction to the leading-order behaviour involving the combination $\mathrm{We}^{-1/2}\mathrm{Re}^{2/5}$, in agreement with the experimental literature on droplet spread. Our bounds are also consistent with full numerical solutions of the ODEs, as well as with energy-budget calculations, once head loss is accounted for in the latter., Comment: 25 pages, 9 figures

Accurate lesion segmentation in whole-body PET/CT scans is crucial for cancer diagnosis and treatment planning, but limited datasets often hinder the performance of automated segmentation models. In this paper, we explore the potential of leveraging the deep prior from a generative model to serve as a data augmenter for automated lesion segmentation in PET/CT scans. We adapt the DiffTumor method, originally designed for CT images, to generate synthetic PET-CT images with lesions. Our approach trains the generative model on the AutoPET dataset and uses it to expand the training data. We then compare the performance of segmentation models trained on the original and augmented datasets. Our findings show that the model trained on the augmented dataset achieves a higher Dice score, demonstrating the potential of our data augmentation approach. In a nutshell, this work presents a promising direction for improving lesion segmentation in whole-body PET/CT scans with limited datasets, potentially enhancing the accuracy and reliability of cancer diagnostics., Comment: 9 pages

Surveys in the Milky Way and Large Magellanic Cloud revealed that the majority of massive stars will interact with companions during their lives. However, knowledge of the binary properties of massive stars at low metallicity, which approaches the conditions of the Early Universe, remains sparse. We present the Binarity at LOw Metallicity (BLOeM) campaign - an ESO large programme designed to obtain 25 epochs of spectroscopy for 929 massive stars in the SMC - the lowest metallicity conditions in which multiplicity is probed to date (Z = 0.2 Zsun). BLOeM will provide (i) the binary fraction, (ii) the orbital configurations of systems with periods P < 3 yr, (iii) dormant OB+BH binaries, and (iv) a legacy database of physical parameters of massive stars at low metallicity. The stars are observed with the LR02 setup of the giraffe instrument of the Very Large Telescope (3960-4570A, resolving power R=6200; typical signal-to-noise ratio S/N=70-100). This paper utilises the first 9 epochs obtained over a three-month time. We describe the survey and data reduction, perform a spectral classification of the stacked spectra, and construct a Hertzsprung-Russell diagram of the sample via spectral-type and photometric calibrations. The sample covers spectral types from O4 to F5, spanning the effective temperature and luminosity ranges 6.5

The factors contributing to the persistence and stability of life are fundamental for understanding complex living systems. Organisms are commonly challenged by harsh and fluctuating environments that are suboptimal for growth and reproduction, which can lead to extinction. Species often contend with unfavorable and noisy conditions by entering a reversible state of reduced metabolic activity, a phenomenon known as dormancy. Here, we develop Spore Life, a model to investigate the effects of dormancy on population dynamics. It is based on Conway's Game of Life, a deterministic cellular automaton where simple rules govern the metabolic state of an individual based on the metabolic state of its neighbors. For individuals that would otherwise die, Spore Life provides a refuge in the form of an inactive state. These dormant individuals (spores) can resuscitate when local conditions improve. The model includes a parameter alpha that controls the survival probability of spores, interpolating between Game of Life (alpha = 0) and Spore Life (alpha = 1), while capturing stochastic dynamics in the intermediate regime (0 < alpha < 1). In addition to identifying the emergence of unique periodic configurations, we find that spore survival increases the average number of active individuals and buffers populations from extinction. Contrary to expectations, the stabilization of the population is not the result of a large and long-lived seed bank. Instead, the demographic patterns in Spore Life only require a small number of resuscitation events. Our approach yields novel insight into what is minimally required for the emergence of complex behaviors associated with dormancy and the seed banks that they generate.

The evolution and fate of massive stars are thought to be affected by rotationally induced internal mixing. The surface boron abundance is a sensitive tracer of this in early B-type main sequence stars. We test current stellar evolution models of massive main sequence stars which include rotational mixing through a systematic study of their predicted surface boron depletion. We construct a dense grid of rotating single star models using MESA, for which we employ a new nuclear network which follows all the stable isotopes up to silicon, including lithium, beryllium, boron, as well as the radioactive isotope aluminium-26. We also compile the measured physical parameters of the 90 Galactic early B-type stars with boron abundance information. We then compare each observed stars with our models through a Bayesian analysis, which yields the mixing efficiency parameter with which the star is reproduced the best, and the probability that it is represented by the stellar models. We find that about two-thirds of the sample stars are well represented by the stellar models, with the best agreement achieved for a rotational mixing efficiency of ~50% compared to the widely adopted value. The remaining one third of the stars, of which many are strongly boron depleted slow rotators, are largely incompatible with our models, for any rotational mixing efficiency. We investigate the observational incidence of binary companions and surface magnetic fields, and discuss their evolutionary implications. Our results confirm the concept of rotational mixing in radiative stellar envelopes. On the other hand, we find that a different boron depletion mechanism, and likely a different formation path, is required to explain about one-third of the sample stars. The large spread in the surface boron abundances of these stars may hold a clue to understanding their origin.

The spin evolution of stars in close binary systems can be strongly affected by tides. We investigate the rotational synchronisation of the stellar components for 69 SB1 systems and 14 SB2 B-type systems in the 30 Doradus region of the Large Magellanic Cloud using observations from the VFTS and BBC surveys. Their orbital periods range from a few to a few hundred days, while estimated primary masses for these systems are in the range 5-20 Msun with mass ratio ranges of approximately q=0.03-0.5 and q=0.6-1.0 for the SB1 and SB2 systems, respectively. Projected rotational velocities of the stellar components have been compared with their synchronous velocities derived from the orbital periods. We find that effectively all systems with orbital period of more than 10 days must be asynchronous, whilst all the systems with periods of less than 3 days are likely synchronised. In terms of the stellar fractional radius (r), our results imply that all systems with r<0.1 are asynchronous, with those having r>0.2 probably being synchronised. For the apparently synchronised systems our results are more consistent with synchronisation at the mean orbital angular velocity rather than with that at periastron., Comment: 13 pages, 5 figures. submitted to A&A

The Small Magellanic Cloud (SMC) shows a large variation in ultraviolet (UV) dust extinction curves, ranging from Milky Way-like (MW) to significantly steeper curves with no detectable 2175 A bump. This result is based on a sample of only nine sightlines. From HST/STIS and IUE spectra of OB stars, we have measured UV extinction curves along 32 SMC sightlines where eight of these curves were published previously. We find 16 sightlines with steep extinction with no detectable 2175 A bump, four sightlines with MW-like extinction with a detectable 2175 A bump, two sightlines with fairly flat UV extinction and weak/absent 2175 A bumps, and 10 sightlines with unreliable curves due to low SMC dust columns. Our expanded sample shows that the sightlines with and without the 2175 A bump are located throughout the SMC and not limited to specific regions. The average extinction curve of the 16 bumpless sightlines is very similar to the previous average based on four sightlines. We find no correlation between dust column and the strength of the 2175 A bump. We test the hypothesis that the 2175 A bump is due to the same dust grains that are responsible for the mid-infrared carbonaceous (PAH) emission features and find they are correlated, confirming recent work in the MW. Overall, the slope of the UV extinction increases as the amplitudes of the 2175 A bump and far-UV curvature decrease. Finally, the UV slope is correlated with $N(HI)/A(V)$ and the 2175 A bump and nonlinear far-UV rise amplitudes are anti-correlated with $N(HI)/A(V)$., Comment: 24 pages, 12 figures, ApJ, accepted

We introduce two models of industrial drying - a simplified one-equation model, and a detailed three-equation model. The purpose of the simplified model is rigorous validation of numerical methods for PDE-constrained optimal control. The purpose of the detailed model is to be able to predict and control the behaviour of an industrial disk drier. For both models, we introduce a fully validated numerical method to compute the optimal source term to maintain the outlet temperature as close as possible to the set-point temperature. By performing simulations using realistic parameters for industrial driers, we illustrate potential applications of the method., Comment: 25 pages, 9 figures

We develop a theoretical predictive model for an all-pass ring resonator that enables the most complete description of linear coupling regimes. The model is based on eigenmode decomposition of Maxwell's equations with full account of the confined and leaky modes, as opposed to the existing phenomenological methods restricted to the confined modes only. This model enables quantitative description of all-pass ring resonators and provides insights into the physics underlying microring-waveguide coupling. We experimentally validate the model using transmission measurements in the linear regime of aluminium nitride resonators. The developed model is then used to explore the field enhancement in microrings crucial for nonlinear photonic applications., Comment: 8 pages, 11 figures

New boron abundances or upper limits have been determined for 8 early-B stars in the young Galactic open cluster NGC 3293, using ultraviolet spectra obtained by the Hubble Space Telescope Cosmic Origins Spectrograph. With previous observations, there are now 18 early-B stars in this cluster with boron measurements. Six of the newly observed stars have projected rotational velocities greater than 200 km/s, allowing new constraints on rotationally driven mixing in main-sequence stars. When comparing to synthetic model populations, we find that the majority of our sample stars agree well with the predicted trends of stronger boron depletion for larger rotation and for larger mass or luminosity. Based on those, a smaller than the canonical rotational mixing efficiency,(fc = 0.0165 vs the more standard value of 0.033), appears to be required. However, our five most slowly rotating stars are not well explained by rotational mixing, and we speculate that they originate from binary mergers., Comment: 18 pages,7 figures, Submitted to AAS Journals

Diagnostics are critical on the path to commercial fusion reactors, since measurements and characterisation of the plasma is important for sustaining fusion reactions. Gamma spectroscopy is commonly used to provide information about the neutron energy spectrum from activation analysis, which can be used to calculate the neutron flux and fusion power. The detection limits for measuring nuclear dosimetry reactions used in such diagnostics are fundamentally related to Compton scattering events making up a background continuum in measured spectra. This background lies in the same energy region as peaks from low-energy gamma rays, leading to detection and characterisation limitations. This paper presents a digital machine learning Compton suppression algorithm (MLCSA), that uses state-of-the-art machine learning techniques to perform pulse shape discrimination (PSD) for high purity germanium (HPGe) detectors. The MLCSA identifies key features of individual pulses to differentiate between those that are generated from photopeaks and Compton scatter events. Compton events are then rejected, reducing the low energy background. This novel suppression algorithm improves gamma spectroscopy results by lowering minimum detectable activity (MDA) limits and thus reducing the measurement time required to reach the desired detection limit. In this paper, the performance of the MLCSA is demonstrated using an HPGe detector, with a gamma spectrum containing americium-241 (Am-241) and cobalt-60 (Co-60). The MDA of Am-241 improved by 51% and the signal to noise ratio (SNR) improved by 49%, while the Co-60 peaks were partially preserved (reduced by 78%). The MLCSA requires no modelling of the specific detector and so has the potential to be detector agnostic, meaning the technique could be applied to a variety of detector types and applications.

We propose and demonstrate a compositional framework for training and verifying reinforcement learning (RL) systems within a multifidelity sim-to-real pipeline, in order to deploy reliable and adaptable RL policies on physical hardware. By decomposing complex robotic tasks into component subtasks and defining mathematical interfaces between them, the framework allows for the independent training and testing of the corresponding subtask policies, while simultaneously providing guarantees on the overall behavior that results from their composition. By verifying the performance of these subtask policies using a multifidelity simulation pipeline, the framework not only allows for efficient RL training, but also for a refinement of the subtasks and their interfaces in response to challenges arising from discrepancies between simulation and reality. In an experimental case study we apply the framework to train and deploy a compositional RL system that successfully pilots a Warthog unmanned ground robot.

Blue supergiants are the brightest stars in their host galaxies and yet their evolutionary status has been a long-standing problem in stellar astrophysics. In this pioneering work, we present a large sample of 59 early B-type supergiants in the Large Magellanic Cloud with newly derived stellar parameters and identify the signatures of stars born from binary mergers among them. We simulate novel 1D merger models of binaries consisting of supergiants with hydrogen-free cores (primaries) and main-sequence companions (secondaries) and consider the effects of interaction of the secondary with the core of the primary. We follow the evolution of the new-born $16-40$ M$_{\odot}$ stars until core-carbon depletion, close to their final pre-explosion structure. Unlike stars which are born alone, stars born from such stellar mergers are blue throughout their core helium-burning phase and reproduce the surface gravities and Hertzsprung-Russel diagram positions of most of our sample. This indicates that the observed blue supergiants are structurally similar to merger-born stars. Moreover, the large nitrogen-to-carbon and oxygen ratios, and helium enhancements exhibited by at least half our data sample are uniquely consistent with our model predictions, leading us to conclude that a large fraction of blue supergiants are indeed products of binary mergers.

Dependently typed proof assistant rely crucially on definitional equality, which relates types and terms that are automatically identified in the underlying type theory. This paper extends type theory with definitional functor laws, equations satisfied propositionally by a large class of container-like type constructors $F : \mathrm{Type} \to \mathrm{Type}$, equipped with a $\mathrm{map}_{F} : (A \to B) \to F\,A \to F\,B$, such as lists or trees. Promoting these equations to definitional ones strengthens the theory, enabling slicker proofs and more automation for functorial type constructors. This extension is used to modularly justify a structural form of coercive subtyping, propagating subtyping through type formers in a map-like fashion. We show that the resulting notion of coercive subtyping, thanks to the extra definitional equations, is equivalent to a natural and implicit form of subsumptive subtyping. The key result of decidability of type-checking in a dependent type system with functor laws for lists has been entirely mechanized in Coq. This is the extended version of the work with the same name published at ESOP'24.

The Near-Earth Object (NEO) Surveyor mission is a NASA observatory designed to discover and characterize near-Earth asteroids and comets. The mission's primary objective is to find the majority of objects large enough to cause severe regional impact damage ($>$140 m in effective spherical diameter) within its five-year baseline survey. Operating at the Sun-Earth L1 Lagrange point, the mission will survey to within 45 degrees of the Sun in an effort to find the objects in the most Earth-like orbits. The survey cadence is optimized to provide observational arcs long enough to reliably distinguish near-Earth objects from more distant small bodies that cannot pose an impact hazard. Over the course of its survey, NEO Surveyor will discover $\sim$200,000 - 300,000 new NEOs down to sizes as small as $\sim$10 m and thousands of comets, significantly improving our understanding of the probability of an Earth impact over the next century., Comment: accepted to PSJ

We consider the classical problem of droplet impact and droplet spread on a smooth surface in the case of an ideal inviscid fluid. We revisit the rim-lamella model of Roisman et al. [\textit{Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences}, 458(2022), pp.1411-1430.]. This model comprises a system of ordinary differential equations (ODEs); we present a rigorous theoretical analysis of these ODEs, and derive upper and lower bounds for the maximum spreading radius. Both bounds possess a $\mathrm{We}^{1/2}$ scaling behaviour, and by a sandwich result, the spreading radius itself also possesses this scaling. We demonstrate rigorously that the rim-lamella model is self-consistent: once a rim forms, its height will invariably exceed that of the lamella. We introduce a rational procedure to obtain initial conditions for the rim-lamella model. Our approach to solving the rim-lamella model gives predictions for the maximum droplet spread that are in close agreement with existing experimental studies and direct numerical simulations., Comment: 24 pages, 7 figures

We present an extensive mechanization of the meta-theory of Martin-L\"of Type Theory (MLTT) in the Coq proof assistant. Our development builds on pre-existing work in Agda to show not only the decidability of conversion, but also the decidability of type checking, using an approach guided by bidirectional type checking. From our proof of decidability, we obtain a certified and executable type checker for a full-fledged version of MLTT with support for $\Pi$, $\Sigma$, $\mathbb{N}$, and identity types, and one universe. Furthermore, our development does not rely on impredicativity, induction-recursion or any axiom beyond MLTT with a schema for indexed inductive types and a handful of predicative universes, narrowing the gap between the object theory and the meta-theory to a mere difference in universes. Finally, we explain our formalization choices, geared towards a modular development relying on Coq's features, e.g. meta-programming facilities provided by tactics and universe polymorphism.