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The instruments at the focus of the Euclid space observatory offer superb, diffraction-limited imaging over an unprecedented (from space) wide field of view of 0.57 deg$^2$. This exquisite image quality has the potential to produce high-precision astrometry for point sources once the undersampling of Euclid's cameras is taken into account by means of accurate, effective point spread function (ePSF) modelling. We present a complex, detailed workflow to simultaneously solve for the geometric distortion (GD) and model the undersampled ePSFs of the Euclid detectors. Our procedure was successfully developed and tested with data from the Early Release Observations (ERO) programme focused on the nearby globular cluster NGC 6397. Our final one-dimensional astrometric precision for a well-measured star just below saturation is 0.7 mas (0.007 pixel) for the Visible Instrument (VIS) and 3 mas (0.01 pixel) for the Near-Infrared Spectrometer and Photometer (NISP). Finally, we present a specific scientific application of this high-precision astrometry: the combination of Euclid and Gaia data to compute proper motions and study the internal kinematics of NGC 6397. Future work, when more data become available, will allow for a better characterisation of the ePSFs and GD corrections that are derived here, along with assessment of their temporal stability, and their dependencies on the spectral energy distribution of the sources as seen through the wide-band filters of Euclid., Comment: 23 pages, 21 figures. Accepted for publication in A&A on October 24, 2024. Astro-photometric catalogs and stacked images will be available at the CDS after the paper will be published

Distributed Asynchronous Object Store (DAOS) is a novel software-defined object store leveraging Non-Volatile Memory (NVM) devices, designed for high performance. It provides a number of interfaces for applications to undertake I/O, ranging from a native object storage API to a DAOS FUSE module for seamless compatibility with existing applications using POSIX file system APIs. In this paper we discuss these interfaces and the options they provide, exercise DAOS through them with various I/O benchmarks, and analyse the observed performance. We also briefly compare the performance with a distributed file system and another object storage system deployed on the same hardware, and showcase DAOS' potential and increased flexibility to support high-performance I/O., Comment: 9 pages, 38 figures, PDSW'24

Large Language Models (LLMs) have rapidly increased in size and apparent capabilities in the last three years, but their training data is largely English text. There is growing interest in multilingual LLMs, and various efforts are striving for models to accommodate languages of communities outside of the Global North, which include many languages that have been historically underrepresented in digital realms. These languages have been coined as "low resource languages" or "long-tail languages", and LLMs performance on these languages is generally poor. While expanding the use of LLMs to more languages may bring many potential benefits, such as assisting cross-community communication and language preservation, great care must be taken to ensure that data collection on these languages is not extractive and that it does not reproduce exploitative practices of the past. Collecting data from languages spoken by previously colonized people, indigenous people, and non-Western languages raises many complex sociopolitical and ethical questions, e.g., around consent, cultural safety, and data sovereignty. Furthermore, linguistic complexity and cultural nuances are often lost in LLMs. This position paper builds on recent scholarship, and our own work, and outlines several relevant social, cultural, and ethical considerations and potential ways to mitigate them through qualitative research, community partnerships, and participatory design approaches. We provide twelve recommendations for consideration when collecting language data on underrepresented language communities outside of the Global North.

As part of our comprehensive, ongoing characterisation of the low-mass end of the main sequence in the Solar neighbourhood, we used the OSIRIS instrument at the 10.4 m Gran Telescopio Canarias to acquire low- and mid-resolution (R$\approx$300 and R$\approx$2500) optical spectroscopy of 53 late-M and L ultracool dwarfs. Most of these objects are known but poorly investigated and lacking complete kinematics. We measured spectral indices, determined spectral types (six of which are new) and inferred effective temperature and surface gravity from BT-Settl synthetic spectra fits for all objects. We were able to measure radial velocities via line centre fitting and cross correlation for 46 objects, 29 of which lacked previous radial velocity measurements. Using these radial velocities in combination with the latest Gaia DR3 data, we also calculated Galactocentric space velocities. From their kinematics, we identified two candidates outside of the thin disc and four in young stellar kinematic groups. Two further ultracool dwarfs are apparently young field objects: 2MASSW J1246467$+$402715 (L4$\beta$), which has a potential, weak lithium absorption line, and G 196$-$3B (L3$\beta$), which was already known as young due to its well-studied primary companion., Comment: 33 pages, 14 figures, Accepted by MNRAS

What is it to interpret the outputs of an opaque machine learning model. One approach is to develop interpretable machine learning techniques. These techniques aim to show how machine learning models function by providing either model centric local or global explanations, which can be based on mechanistic interpretations revealing the inner working mechanisms of models or nonmechanistic approximations showing input feature output data relationships. In this paper, we draw on social philosophy to argue that interpreting machine learning outputs in certain normatively salient domains could require appealing to a third type of explanation that we call sociostructural explanation. The relevance of this explanation type is motivated by the fact that machine learning models are not isolated entities but are embedded within and shaped by social structures. Sociostructural explanations aim to illustrate how social structures contribute to and partially explain the outputs of machine learning models. We demonstrate the importance of sociostructural explanations by examining a racially biased healthcare allocation algorithm. Our proposal highlights the need for transparency beyond model interpretability, understanding the outputs of machine learning systems could require a broader analysis that extends beyond the understanding of the machine learning model itself.

In this paper, we introduce Splatt3R, a pose-free, feed-forward method for in-the-wild 3D reconstruction and novel view synthesis from stereo pairs. Given uncalibrated natural images, Splatt3R can predict 3D Gaussian Splats without requiring any camera parameters or depth information. For generalizability, we build Splatt3R upon a ``foundation'' 3D geometry reconstruction method, MASt3R, by extending it to deal with both 3D structure and appearance. Specifically, unlike the original MASt3R which reconstructs only 3D point clouds, we predict the additional Gaussian attributes required to construct a Gaussian primitive for each point. Hence, unlike other novel view synthesis methods, Splatt3R is first trained by optimizing the 3D point cloud's geometry loss, and then a novel view synthesis objective. By doing this, we avoid the local minima present in training 3D Gaussian Splats from stereo views. We also propose a novel loss masking strategy that we empirically find is critical for strong performance on extrapolated viewpoints. We train Splatt3R on the ScanNet++ dataset and demonstrate excellent generalisation to uncalibrated, in-the-wild images. Splatt3R can reconstruct scenes at 4FPS at 512 x 512 resolution, and the resultant splats can be rendered in real-time., Comment: Our project page can be found at: https://splatt3r.active.vision/

Motivated by the study of state opioid policies, we propose a novel approach that uses autoregressive models for causal effect estimation in settings with panel data and staggered treatment adoption. Specifically, we seek to estimate of the impact of key opioid-related policies by quantifying the effects of must access prescription drug monitoring programs (PDMPs), naloxone access laws (NALs), and medical marijuana laws on opioid prescribing. Existing methods, such as differences-in-differences and synthetic controls, are challenging to apply in these types of dynamic policy landscapes where multiple policies are implemented over time and sample sizes are small. Autoregressive models are an alternative strategy that have been used to estimate policy effects in similar settings, but until this paper have lacked formal justification. We outline a set of assumptions that tie these models to causal effects, and we study biases of estimates based on this approach when key causal assumptions are violated. In a set of simulation studies that mirror the structure of our application, we also show that our proposed estimators frequently outperform existing estimators. In short, we justify the use of autoregressive models to provide robust evidence on the effectiveness of four state policies in combating the opioid crisis.

Differential privacy is a popular privacy-enhancing technology that has been deployed both in industry and government agencies. Unfortunately, existing explanations of differential privacy fail to set accurate privacy expectations for data subjects, which depend on the choice of deployment model. We design and evaluate new explanations of differential privacy for the local and central models, drawing inspiration from prior work explaining other privacy-enhancing technologies. We find that consequences-focused explanations in the style of privacy nutrition labels that lay out the implications of differential privacy are a promising approach for setting accurate privacy expectations. Further, we find that while process-focused explanations are not enough to set accurate privacy expectations, combining consequences-focused explanations with a brief description of how differential privacy works leads to greater trust.

We present the Ultracool Dwarf Companion Catalogue of 278 multiple systems, 32 of which are newly discovered, each with at least one spectroscopically confirmed Ultracool Dwarf, within a 100 pc volume-limited sample. This catalogue is compiled using the Gaia Catalogue of Nearby Stars for stellar primaries and the Gaia Ultracool Dwarf Sample for low-mass companions and includes 241 doubles, 33 triples, and 4 higher-order systems established from positional, proper motion, and parallax constraints. The catalogue seeks to identify probable benchmark systems within 100 pc to obtain model-independent astrophysical parameters of Ultracool Dwarfs. Chance alignment probabilities are calculated to evaluate the physical nature of each system. Astrometric and photometric data from Gaia Data Release 3 and the Two Micron All Sky Survey are included for all objects. We identify potential unseen companions using a combination of the Renormalised Unit Weight Error, Image Parameter Determination statistics, Non-Single Star solutions, and photometric blending as provided by Gaia, identifying hierarchical Ultracool triple systems. Our catalogue includes 17 White Dwarf - Ultracool Dwarf systems, whose ages are determined using cooling models. We also use the Gaia FLAME results and the BANYAN $\Sigma$ procedures to age 40 and 34 systems respectively, and derive mass estimates from evolutionary models., Comment: 26 pages, 8 figures

Quantum simulation algorithms often require numerous ancilla qubits and deep circuits, prohibitive for near-term hardware. We introduce a framework for simulating quantum channels using ensembles of low-depth circuits in place of many-qubit dilations. This naturally enables simulations of open systems, which we demonstrate by preparing damped many-qubit GHZ states on ibm_hanoi. The technique further inspires two Hamiltonian simulation algorithms with asymptotic independence of the spectral precision, reducing resource requirements by several orders of magnitude for a benchmark system., Comment: 11 pages, 7 figures

We report the discovery of five white dwarf + ultracool dwarf systems identified as common proper motion wide binaries in the Gaia Catalogue of Nearby Stars. The discoveries include a white dwarf + L subdwarf binary, VVV 1256-62AB, a gravitationally bound system located 75.6(+1.9/-1.8) pc away with a projected separation of 1375(+35/-33) au. The primary is a cool DC white dwarf with a hydrogen dominated atmosphere, and has a total age of 10.5(+3.3/-2.1) Gyr, based on white dwarf model fitting. The secondary is an L subdwarf with a metallicity of [M/H] = -0.72(+0.08/-0.10) (i.e. [Fe/H] = -0.81+/-0.10) and Teff = 2298(+45/-43) K based on atmospheric model fitting of its optical to near infrared spectrum, and likely has a mass just above the stellar/substellar boundary. The sub-solar metallicity of the L subdwarf and the system's total space velocity of 406 km/s indicates membership in the Galactic halo, and it has a flat eccentric Galactic orbit passing within 1~kpc of the centre of the Milky Way every ~0.4Gyr and extending to 15-31 kpc at apogal. VVV 1256-62B is the first L subdwarf to have a well-constrained age, making it an ideal benchmark of metal-poor ultracool dwarf atmospheres and evolution., Comment: 15 pages, 12 figures

Habituation - a phenomenon in which a dynamical system exhibits a diminishing response to repeated stimulations that eventually recovers when the stimulus is withheld - is universally observed in living systems from animals to unicellular organisms. Despite its prevalence, generic mechanisms for this fundamental form of learning remain poorly defined. Drawing inspiration from prior work on systems that respond adaptively to step inputs, we study habituation from a nonlinear dynamics perspective. This approach enables us to formalize classical hallmarks of habituation that have been experimentally identified in diverse organisms and stimulus scenarios. We use this framework to investigate distinct dynamical circuits capable of habituation. In particular, we show that driven linear dynamics of a memory variable with static nonlinearities acting at the input and output can implement numerous hallmarks in a mathematically interpretable manner. This work establishes a foundation for understanding the dynamical substrates of this primitive learning behavior and offers a blueprint for the identification of habituating circuits in biological systems., Comment: 6 figures

We present infrared spectral energy distributions of 23 late-type T and Y dwarfs obtained with the James Webb Space Telescope. The spectral energy distributions consist of NIRSpec PRISM and MIRI LRS spectra covering the $\sim$1--12 $\mu$m wavelength range at $\lambda/ \Delta \lambda \approx 100$ and broadband photometry at 15, 18, and 21 $\mu$m. The spectra exhibit absorption features common to these objects including H$_2$O, CH$_4$, CO, CO$_2$, and NH$_3$. Interestingly, while the spectral morphology changes relatively smoothly with spectral type at $\lambda < 3$ $\mu$m and $\lambda > 8$ $\mu$m, it shows no clear trend in the 5 $\mu$m region where a large fraction of the flux emerges. The broad wavelength coverage of the data enables us to compute the first accurate measurements of the bolometric fluxes of cool brown dwarfs. Combining these bolometric fluxes with parallaxes from Spitzer and HST, we also obtain the first accurate bolometric luminosities of these cool dwarfs. We then used the Sonora Bobcat solar metallicity evolutionary models to estimate the radii of the dwarfs which results in effective temperature estimates ranging from $\sim$1000 to 350 K with a median uncertainty of $\pm$20 K which is nearly an order of magnitude improvement over previous work. We also discuss how various portions of the spectra either do or do not exhibit a clear sequence when ordered by their effective temperatures., Comment: Accepted, 33 pages, 19 figures, 8 tables, Fixed Appendix table (photometry ordering) and Fixed Table 1 (full designation of WISE2018 and WISE0410 photometry)

Accurate models for open quantum systems -- quantum states that have non-trivial interactions with their environment -- may aid in the advancement of a diverse array of fields, including quantum computation, informatics, and the prediction of static and dynamic molecular properties. In recent years, quantum algorithms have been leveraged for the computation of open quantum systems as the predicted quantum advantage of quantum devices over classical ones may allow previously inaccessible applications. Accomplishing this goal will require input and expertise from different research perspectives, as well as the training of a diverse quantum workforce, making a compilation of current quantum methods for treating open quantum systems both useful and timely. In this Review, we first provide a succinct summary of the fundamental theory of open quantum systems and then delve into a discussion on recent quantum algorithms. We conclude with a discussion of pertinent applications, demonstrating the applicability of this field to realistic chemical, biological, and material systems.

We provide an early assessment of the imaging capabilities of the Euclid space mission to probe deeply into nearby star-forming regions and associated very young open clusters, and in particular to check to what extent it can shed light on the new-born free-floating planet population. This paper focuses on a low-reddening region observed in just one Euclid pointing where the dust and gas has been cleared out by the hot sigma Orionis star. One late-M and six known spectroscopically confirmed L-type substellar members in the sigma Orionis cluster are used as benchmarks to provide a high-purity procedure to select new candidate members with Euclid. The exquisite angular resolution and depth delivered by the Euclid instruments allow us to focus on bona-fide point sources. A cleaned sample of sigma Orionis cluster substellar members has been produced and the initial mass function (IMF) has been estimated by combining Euclid and Gaia data. Our sigma Orionis substellar IMF is consistent with a power-law distribution with no significant steepening at the planetary-mass end. No evidence of a low-mass cutoff is found down to about 4 Jupiter masses at the young age (3 Myr) of the sigma Orionis open cluster., Comment: 13 pages, 10 figures, submitted to the A&A special issue on "Euclid on the sky"

As large language models (LLMs) evolve, their integration with 3D spatial data (3D-LLMs) has seen rapid progress, offering unprecedented capabilities for understanding and interacting with physical spaces. This survey provides a comprehensive overview of the methodologies enabling LLMs to process, understand, and generate 3D data. Highlighting the unique advantages of LLMs, such as in-context learning, step-by-step reasoning, open-vocabulary capabilities, and extensive world knowledge, we underscore their potential to significantly advance spatial comprehension and interaction within embodied Artificial Intelligence (AI) systems. Our investigation spans various 3D data representations, from point clouds to Neural Radiance Fields (NeRFs). It examines their integration with LLMs for tasks such as 3D scene understanding, captioning, question-answering, and dialogue, as well as LLM-based agents for spatial reasoning, planning, and navigation. The paper also includes a brief review of other methods that integrate 3D and language. The meta-analysis presented in this paper reveals significant progress yet underscores the necessity for novel approaches to harness the full potential of 3D-LLMs. Hence, with this paper, we aim to chart a course for future research that explores and expands the capabilities of 3D-LLMs in understanding and interacting with the complex 3D world. To support this survey, we have established a project page where papers related to our topic are organized and listed: https://github.com/ActiveVisionLab/Awesome-LLM-3D.

Learning in living organisms is typically associated with networks of neurons. The use of large numbers of adjustable units has also been a crucial factor in the continued success of artificial neural networks. In light of the complexity of both living and artificial neural networks, it is surprising to see that very simple organisms -- even unicellular organisms that do not possess a nervous system -- are capable of certain forms of learning. Since in these cases learning may be implemented with much simpler structures than neural networks, it is natural to ask how simple the building blocks required for basic forms of learning may be. The purpose of this study is to discuss the simplest dynamical systems that model a fundamental form of non-associative learning, habituation, and to elucidate technical implementations of such systems, which may be used to implement non-associative learning in neuromorphic computing and related applications.

Natural climate solutions (NCS) are critical for mitigating climate change through ecosystem-based carbon removal and emissions reductions. NCS implementation can also generate biodiversity and human well-being co-benefits and trade-offs ("NCS co-impacts"), but the volume of evidence on NCS co-impacts has grown rapidly across disciplines, is poorly understood, and remains to be systematically collated and synthesized. A global evidence map of NCS co-impacts would overcome key barriers to NCS implementation by providing relevant information on co-benefits and trade-offs where carbon mitigation potential alone does not justify NCS projects. We employ large language models to assess over two million articles, finding 257,266 relevant articles on NCS co-impacts. We analyze this large and dispersed body of literature using innovative machine learning methods to extract relevant data (e.g., study location, species, and other key variables), and create a global evidence map on NCS co-impacts. Evidence on NCS co-impacts has grown approximately ten-fold in three decades, although some of the most abundant evidence is associated with pathways that have less mitigation potential. We find that studies often examine multiple NCS pathways, indicating natural NCS pathway complements, and each NCS is often associated with two or more coimpacts. Finally, NCS co-impacts evidence and priority areas for NCS are often mismatched--some countries with high mitigation potential from NCS have few published studies on the broader co-impacts of NCS implementation. Our work advances and makes available novel methods and systematic and representative data of NCS co-impacts studies, thus providing timely insights to inform NCS research and action globally., Comment: 28 pages, 5 figures

Gaia can not individually resolve very close binary systems, however, the collected data can still be used to identify them. A powerful indicator of stellar multiplicity is the sources reported Renormalized Unit Weight Error (ruwe), which effectively captures the astrometric deviations from single-source solutions. We aim to characterise the imprints left on ruwe caused by binarity. By flagging potential binary systems based on ruwe, we aim to characterise which of their properties will contribute the most to their detectability. We develop a model to estimate ruwe values for observations of Gaia sources, based on the biases to the single-source astrometric track arising from the presence of an unseen companion. Then, using the recipes from previous GaiaUnlimited selection functions, we estimate the selection probability of sources with high ruwe, and discuss what binary properties contribute to increasing the sources ruwe. We compute the maximum ruwe value which is compatible with single-source solutions as a function of their location on-sky. We see that binary systems selected as sources with a ruwe higher than this sky-varying threshold have a strong detectability window in their orbital period distribution, which peaks at periods equal to the Gaia observation time baseline. We demonstrate how our sky-varying ruwe threshold provides a more complete sample of binary systems when compared to single sky-averaged values by studying the unresolved binary population in the Gaia Catalogue of Nearby Stars. We provide the code and tools used in this study, as well as the sky-varying ruwe threshold through the GaiaUnlimited Python package, Comment: 11 pages, 10 figures. Accepted for publication in A&A

Small planets transiting bright nearby stars are essential to our understanding of the formation and evolution of exoplanetary systems. However, few constitute prime targets for atmospheric characterization, and even fewer are part of multiple star systems. This work aims to validate TOI-4336 A b, a sub-Neptune-sized exoplanet candidate identified by the TESS space-based transit survey around a nearby M-dwarf. We validate the planetary nature of TOI-4336 A b through the global analysis of TESS and follow-up multi-band high-precision photometric data from ground-based telescopes, medium- and high-resolution spectroscopy of the host star, high-resolution speckle imaging, and archival images. The newly discovered exoplanet TOI-4336 A b has a radius of 2.1$\pm$0.1R$_{\oplus}$. Its host star is an M3.5-dwarf star of mass 0.33$\pm$0.01M$_{\odot}$ and radius 0.33$\pm$0.02R$_{\odot}$ member of a hierarchical triple M-dwarf system 22 pc away from the Sun. The planet's orbital period of 16.3 days places it at the inner edge of the Habitable Zone of its host star, the brightest of the inner binary pair. The parameters of the system make TOI-4336 A b an extremely promising target for the detailed atmospheric characterization of a temperate sub-Neptune by transit transmission spectroscopy with JWST., Comment: 27 pages, 19 figures, 7 tables. Accepted for publication in A&A

Gravitational waves from black-hole merging events have revealed a population of extra-galactic BHs residing in short-period binaries with masses that are higher than expected based on most stellar evolution models - and also higher than known stellar-origin black holes in our Galaxy. It has been proposed that those high-mass BHs are the remnants of massive metal-poor stars. Gaia astrometry is expected to uncover many Galactic wide-binary systems containing dormant BHs, which may not have been detected before. The study of this population will provide new information on the BH-mass distribution in binaries and shed light on their formation mechanisms and progenitors. As part of the validation efforts in preparation for the fourth Gaia data release (DR4), we analysed the preliminary astrometric binary solutions, obtained by the Gaia Non-Single Star pipeline, to verify their significance and to minimise false-detection rates in high-mass-function orbital solutions. The astrometric binary solution of one source, Gaia BH3, implies the presence of a 32.70 \pm 0.82 M\odot BH in a binary system with a period of 11.6 yr. Gaia radial velocities independently validate the astrometric orbit. Broad-band photometric and spectroscopic data show that the visible component is an old, very metal-poor giant of the Galactic halo, at a distance of 590 pc. The BH in the Gaia BH3 system is more massive than any other Galactic stellar-origin BH known thus far. The low metallicity of the star companion supports the scenario that metal-poor massive stars are progenitors of the high-mass BHs detected by gravitational-wave telescopes. The Galactic orbit of the system and its metallicity indicate that it might belong to the Sequoia halo substructure. Alternatively, and more plausibly, it could belong to the ED-2 stream, which likely originated from a globular cluster that had been disrupted by the Milky Way., Comment: 23 pages, accepted fro publication in A&A Letters. New version with small fixes

Bosonic quantum devices offer a novel approach to realize quantum computations, where the quantum two-level system (qubit) is replaced with the quantum (an)harmonic oscillator (qumode) as the fundamental building block of the quantum simulator. The simulation of chemical structure and dynamics can then be achieved by representing or mapping the system Hamiltonians in terms of bosonic operators. In this perspective, we review recent progress and future potential of using bosonic quantum devices for addressing a wide range of challenging chemical problems, including the calculation of molecular vibronic spectra, the simulation of gas-phase and solution-phase adiabatic and nonadiabatic chemical dynamics, the efficient solution of molecular graph theory problems, and the calculations of electronic structure., Comment: 40 pages including references, 13 figures, revised

Operational Numerical Weather Prediction (NWP) workflows are highly data-intensive. Data volumes have increased by many orders of magnitude over the last 40 years, and are expected to continue to do so, especially given the upcoming adoption of Machine Learning in forecast processes. Parallel POSIX-compliant file systems have been the dominant paradigm in data storage and exchange in HPC workflows for many years. This paper presents ECMWF's move beyond the POSIX paradigm, implementing a backend for their storage library to support DAOS -- a novel high-performance object store designed for massively distributed Non-Volatile Memory. This system is demonstrated to be able to outperform the highly mature and optimised POSIX backend when used under high load and contention, as per typical forecast workflow I/O patterns. This work constitutes a significant step forward, beyond the performance constraints imposed by POSIX semantics., Comment: 12 pages, 6 figures, accepted at The ACM Platform For Advanced Scientific Computing (PASC) Conference, 2024

Objective: The transmit encoding model for synthetic aperture imaging is a robust and flexible framework for understanding the effects of acoustic transmission on ultrasound image reconstruction. Our objective is to use machine learning (ML) to construct scanning sequences, parameterized by time delays and apodization weights, that produce high-quality B-mode images. Approach: We use a custom ML model in PyTorch with simulated RF data from Field II to probe the space of possible encoding sequences for those that minimize a loss function that describes image quality. This approach is made computationally feasible by a novel formulation of the derivative for delay-and-sum beamforming. Main Results: When trained for a specified experimental setting (imaging domain, hardware restrictions, etc.), our ML model produces optimized encoding sequences that, when deployed in the REFoCUS imaging framework, improve a number of standard quality metrics over conventional sequences including resolution, field of view, and contrast. We demonstrate these results experimentally on both wire targets and a tissue-mimicking phantom. Significance: This work demonstrates that the set of commonly used encoding schemes represent only a narrow subset of those available. Additionally, it demonstrates the value for ML tasks in synthetic transmit aperture imaging to consider the beamformer within the model, instead of purely as a post-processing step., Comment: 31 pages, 12 figures

Radio Frequency Reinforcement Learning (RFRL) is anticipated to be a widely applicable technology in the next generation of wireless communication systems, particularly 6G and next-gen military communications. Given this, our research is focused on developing a tool to promote the development of RFRL techniques that leverage spectrum sensing. In particular, the tool was designed to address two cognitive radio applications, specifically dynamic spectrum access and jamming. In order to train and test reinforcement learning (RL) algorithms for these applications, a simulation environment is necessary to simulate the conditions that an agent will encounter within the Radio Frequency (RF) spectrum. In this paper, such an environment has been developed, herein referred to as the RFRL Gym. Through the RFRL Gym, users can design their own scenarios to model what an RL agent may encounter within the RF spectrum as well as experiment with different spectrum sensing techniques. Additionally, the RFRL Gym is a subclass of OpenAI gym, enabling the use of third-party ML/RL Libraries. We plan to open-source this codebase to enable other researchers to utilize the RFRL Gym to test their own scenarios and RL algorithms, ultimately leading to the advancement of RL research in the wireless communications domain. This paper describes in further detail the components of the Gym, results from example scenarios, and plans for future additions. Index Terms-machine learning, reinforcement learning, wireless communications, dynamic spectrum access, OpenAI gym

Context: The local census of very low-mass stars and brown dwarfs is crucial to improving our understanding of the stellar-substellar transition and their formation history. These objects, known as ultra-cool dwarfs (UCDs), are essential targets for searches of potentially habitable planets. However, their detection poses a challenge because of their low luminosity. The Gaia survey has identified numerous new UCD candidates thanks to its large survey and precise astrometry. Aims: We aim to characterise 60 UCD candidates detected by Gaia in the solar neighbourhood with a spectroscopic follow-up to confirm that they are UCDs, as well as to identify peculiarities. Methods: We acquired the near-infrared (NIR) spectra of 60 objects using the SOFI spectrograph between 0.93 and 2.5 microns (R$\sim600$). We identified their spectral types using a template-matching method. Their binarity is studied using astrometry and spectral features. Results: We confirm that 60 objects in the sample have ultra-cool dwarf spectral types close to those expected from astrometry. Their NIR spectra reveal that seven objects could host an unresolved coolest companion and seven UCDs share the same proper motions as other stars. The characterisation of these UCDs is part of a coordinated effort to improve our understanding of the Solar neighbourhood., Comment: Based on observations made with the ESO New Technology Telescope at the La Silla Observatory under programme 106.214E.001 and 108.22G4.001. Accepted by A&A on the 30/01/2024

Quantum computing offers several new pathways toward finding many-body eigenstates, with variational approaches being some of the most flexible and near-term oriented. These require particular parameterizations of the state, and for solving multiple eigenstates must incorporate orthogonality. In this work, we use techniques from manifold optimization to arrive at solutions of the many-body eigenstate problem via direct minimization over the Stiefel and Grassmannian manifolds, avoiding parameterizations of the states and allowing for multiple eigenstates to be simultaneously calculated. These Riemannian manifolds naturally encode orthogonality constraints and have efficient quantum representations of the states and tangent vectors. We provide example calculations for quantum many-body molecular systems and discuss different pathways for solving the multiple eigenstate problem., Comment: 19 pages, 5 figures

Data annotation remains the sine qua non of machine learning and AI. Recent empirical work on data annotation has begun to highlight the importance of rater diversity for fairness, model performance, and new lines of research have begun to examine the working conditions for data annotation workers, the impacts and role of annotator subjectivity on labels, and the potential psychological harms from aspects of annotation work. This paper outlines a critical genealogy of data annotation; starting with its psychological and perceptual aspects. We draw on similarities with critiques of the rise of computerized lab-based psychological experiments in the 1970's which question whether these experiments permit the generalization of results beyond the laboratory settings within which these results are typically obtained. Do data annotations permit the generalization of results beyond the settings, or locations, in which they were obtained? Psychology is overly reliant on participants from Western, Educated, Industrialized, Rich, and Democratic societies (WEIRD). Many of the people who work as data annotation platform workers, however, are not from WEIRD countries; most data annotation workers are based in Global South countries. Social categorizations and classifications from WEIRD countries are imposed on non-WEIRD annotators through instructions and tasks, and through them, on data, which is then used to train or evaluate AI models in WEIRD countries. We synthesize evidence from several recent lines of research and argue that data annotation is a form of automated social categorization that risks entrenching outdated and static social categories that are in reality dynamic and changing. We propose a framework for understanding the interplay of the global social conditions of data annotation with the subjective phenomenological experience of data annotation work., Comment: 18 pages

Dreams of machines rivaling human intelligence have shaped the field of AI since its inception. Yet, the very meaning of human-level AI or artificial general intelligence (AGI) remains elusive and contested. Definitions of AGI embrace a diverse range of incompatible values and assumptions. Contending with the fractured worldviews of AGI discourse is vital for critiques that pursue different values and futures. To that end, we provide a taxonomy of AGI definitions, laying the ground for examining the key social, political, and ethical assumptions they make. We highlight instances in which these definitions frame AGI or human-level AI as a technical topic and expose the value-laden choices being implicitly made. Drawing on feminist, STS, and social science scholarship on the political and social character of intelligence in both humans and machines, we propose contextual, democratic, and participatory paths to imagining future forms of machine intelligence. The development of future forms of AI must involve explicit attention to the values it encodes, the people it includes or excludes, and a commitment to epistemic justice.

We present the SARAO MeerKAT Galactic Plane Survey (SMGPS), a 1.3 GHz continuum survey of almost half of the Galactic Plane (251\deg $\le l \le$ 358\deg and 2\deg $\le l \le$ 61\deg at $|b| \le 1.5\deg $). SMGPS is the largest, most sensitive and highest angular resolution 1 GHz survey of the Plane yet carried out, with an angular resolution of 8" and a broadband RMS sensitivity of $\sim$10--20 $\mu$ Jy/beam. Here we describe the first publicly available data release from SMGPS which comprises data cubes of frequency-resolved images over 908--1656 MHz, power law fits to the images, and broadband zeroth moment integrated intensity images. A thorough assessment of the data quality and guidance for future usage of the data products are given. Finally, we discuss the tremendous potential of SMGPS by showcasing highlights of the Galactic and extragalactic science that it permits. These highlights include the discovery of a new population of non-thermal radio filaments; identification of new candidate supernova remnants, pulsar wind nebulae and planetary nebulae; improved radio/mid-IR classification of rare Luminous Blue Variables and discovery of associated extended radio nebulae; new radio stars identified by Bayesian cross-matching techniques; the realisation that many of the largest radio-quiet WISE HII region candidates are not true HII regions; and a large sample of previously undiscovered background HI galaxies in the Zone of Avoidance., Comment: Accepted for publication in MNRAS. The data release is live and links can be found in the Data Availability Statement in the paper

The fields of AI current lacks methods to quantitatively assess and potentially alter the moral values inherent in the output of large language models (LLMs). However, decades of social science research has developed and refined widely-accepted moral value surveys, such as the World Values Survey (WVS), eliciting value judgments from direct questions in various geographies. We have turned those questions into value statements and use NLP to compute to how well popular LLMs are aligned with moral values for various demographics and cultures. While the WVS is accepted as an explicit assessment of values, we lack methods for assessing implicit moral and cultural values in media, e.g., encountered in social media, political rhetoric, narratives, and generated by AI systems such as LLMs that are increasingly present in our daily lives. As we consume online content and utilize LLM outputs, we might ask, which moral values are being implicitly promoted or undercut, or -- in the case of LLMs -- if they are intending to represent a cultural identity, are they doing so consistently? In this paper we utilize a Recognizing Value Resonance (RVR) NLP model to identify WVS values that resonate and conflict with a given passage of output text. We apply RVR to the text generated by LLMs to characterize implicit moral values, allowing us to quantify the moral/cultural distance between LLMs and various demographics that have been surveyed using the WVS. In line with other work we find that LLMs exhibit several Western-centric value biases; they overestimate how conservative people in non-Western countries are, they are less accurate in representing gender for non-Western countries, and portray older populations as having more traditional values. Our results highlight value misalignment and age groups, and a need for social science informed technological solutions addressing value plurality in LLMs., Comment: Accepted Paper to workshop on "AI meets Moral Philosophy and Moral Psychology: An Interdisciplinary Dialogue about Computational Ethics" at NeurIPS 2023

A complete accounting of nearby objects -- from the highest-mass white dwarf progenitors down to low-mass brown dwarfs -- is now possible, thanks to an almost complete set of trigonometric parallax determinations from Gaia, ground-based surveys, and Spitzer follow-up. We create a census of objects within a Sun-centered sphere of 20-pc radius and check published literature to decompose each binary or higher-order system into its separate components. The result is a volume-limited census of $\sim$3,600 individual star formation products useful in measuring the initial mass function across the stellar ($<8 M_\odot$) and substellar ($\gtrsim 5 M_{Jup}$) regimes. Comparing our resulting initial mass function to previous measurements shows good agreement above 0.8$M_\odot$ and a divergence at lower masses. Our 20-pc space densities are best fit with a quadripartite power law, $\xi(M) = dN/dM \propto M^{-\alpha}$ with long-established values of $\alpha = 2.3$ at high masses ($0.55 < M < 8.00 M_\odot$) and $\alpha = 1.3$ at intermediate masses ($0.22 < M < 0.55 M_\odot$), but at lower masses we find $\alpha = 0.25$ for $0.05 < M <0.22 M_\odot$ and $\alpha = 0.6$ for $0.01 < M < 0.05 M_\odot$. This implies that the rate of production as a function of decreasing mass diminishes in the low-mass star/high-mass brown dwarf regime before increasing again in the low-mass brown dwarf regime. Correcting for completeness, we find a star to brown dwarf number ratio of, currently, 4:1, and an average mass per object of 0.41 $M_\odot$., Comment: Accepted for publication in The Astrophysical Journal Supplement Series. 123 pages with four ancillary files

Gaia's readout window strategy is challenged by very dense fields in the sky. Therefore, in addition to standard Gaia observations, full Sky Mapper (SM) images were recorded for nine selected regions in the sky. A new software pipeline exploits these Service Interface Function (SIF) images of crowded fields (CFs), making use of the availability of the full two-dimensional (2D) information. This new pipeline produced half a million additional Gaia sources in the region of the omega Centauri ($\omega$ Cen) cluster, which are published with this Focused Product Release. We discuss the dedicated SIF CF data reduction pipeline, validate its data products, and introduce their Gaia archive table. Our aim is to improve the completeness of the {\it Gaia} source inventory in a very dense region in the sky, $\omega$ Cen. An adapted version of {\it Gaia}'s Source Detection and Image Parameter Determination software located sources in the 2D SIF CF images. We validated the results by comparing them to the public {\it Gaia} DR3 catalogue and external Hubble Space Telescope data. With this Focused Product Release, 526\,587 new sources have been added to the {\it Gaia} catalogue in $\omega$ Cen. Apart from positions and brightnesses, the additional catalogue contains parallaxes and proper motions, but no meaningful colour information. While SIF CF source parameters generally have a lower precision than nominal {\it Gaia} sources, in the cluster centre they increase the depth of the combined catalogue by three magnitudes and improve the source density by a factor of ten. This first SIF CF data publication already adds great value to the {\it Gaia} catalogue. It demonstrates what to expect for the fourth {\it Gaia} catalogue, which will contain additional sources for all nine SIF CF regions.

Context. Strongly lensed quasars are fundamental sources for cosmology. The Gaia space mission covers the entire sky with the unprecedented resolution of $0.18$" in the optical, making it an ideal instrument to search for gravitational lenses down to the limiting magnitude of 21. Nevertheless, the previous Gaia Data Releases are known to be incomplete for small angular separations such as those expected for most lenses. Aims. We present the Data Processing and Analysis Consortium GravLens pipeline, which was built to analyse all Gaia detections around quasars and to cluster them into sources, thus producing a catalogue of secondary sources around each quasar. We analysed the resulting catalogue to produce scores that indicate source configurations that are compatible with strongly lensed quasars. Methods. GravLens uses the DBSCAN unsupervised clustering algorithm to detect sources around quasars. The resulting catalogue of multiplets is then analysed with several methods to identify potential gravitational lenses. We developed and applied an outlier scoring method, a comparison between the average BP and RP spectra of the components, and we also used an extremely randomised tree algorithm. These methods produce scores to identify the most probable configurations and to establish a list of lens candidates. Results. We analysed the environment of 3 760 032 quasars. A total of 4 760 920 sources, including the quasars, were found within 6" of the quasar positions. This list is given in the Gaia archive. In 87\% of cases, the quasar remains a single source, and in 501 385 cases neighbouring sources were detected. We propose a list of 381 lensed candidates, of which we identified 49 as the most promising. Beyond these candidates, the associate tables in this Focused Product Release allow the entire community to explore the unique Gaia data for strong lensing studies further., Comment: 35 pages, 60 figures, accepted for publication by Astronomy and Astrophysics