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We report on the observation and measurement of astrometry, photometry, morphology, and activity of the interstellar object 3I/ATLAS, also designated C/2025 N1 (ATLAS), with the NSF-DOE Vera C. Rubin Observatory. The third interstellar object, comet 3I/ATLAS, was first discovered on UT 2025 July 1. Serendipitously, the Rubin Observatory collected imaging in the area of the sky inhabited by the object during regular commissioning activities. We successfully recovered object detections from Rubin visits spanning UT 2025 June 21 (10 days before discovery) to UT 2025 July 7. Facilitated by Rubin's high resolution and large aperture, we report on the detection of cometary activity as early as June 21st, and observe it throughout. We measure the location and magnitude of the object on 37 Rubin images in r, i, and z bands, with typical precision of about 20 mas (100 mas, systematic) and about 10 mmag, respectively. We use these to derive improved orbit solutions, and to show there is no detectable photometric variability on hourly timescales. We derive a V-band absolute magnitude of H_V = (13.7 +/- 0.2) mag, and an equivalent effective nucleus radius of around (5.6 +/- 0.7) km. These data represent the earliest observations of this object by a large (8-meter class) telescope reported to date, and illustrate the type of measurements (and discoveries) Rubin's Legacy Survey of Space and Time (LSST) will begin to provide once operational later this year.

The elephant trunk exemplifies a natural gripper where structure, actuation, and sensing are seamlessly integrated. Inspired by the distal morphology of the African elephant trunk, we present MELEGROS, a Monolithic ELEphant-inspired GRipper with Optical Sensors, emphasizing sensing as an intrinsic, co-fabricated capability. Unlike multi-material or tendon-based approaches, MELEGROS directly integrates six optical waveguide sensors and five pneumatic chambers into a pneumatically actuated lattice structure (12.5 mm cell size) using a single soft resin and one continuous 3D print. This eliminates mechanical mismatches between sensors, actuators, and body, reducing model uncertainty and enabling simulation-guided sensor design and placement. Only four iterations were required to achieve the final prototype, which features a continuous structure capable of elongation, compression, and bending while decoupling tactile and proprioceptive signals. MELEGROS (132 g) lifts more than twice its weight, performs bioinspired actions such as pinching, scooping, and reaching, and delicately grasps fragile items like grapes. The integrated optical sensors provide distinct responses to touch, bending, and chamber deformation, enabling multifunctional perception. MELEGROS demonstrates a new paradigm for soft robotics where fully embedded sensing and continuous structures inherently support versatile, bioinspired manipulation.

Dubbed a safer C, Rust is a modern programming language that combines memory safety and low-level control. This interesting combination has made Rust very popular among developers and there is a growing trend of migrating legacy codebases (very often in C) to Rust. In this paper, we present a C to Rust translation approach centred around static ownership analysis. We design a suite of analyses that infer ownership models of C pointers and automatically translate the pointers into safe Rust equivalents. The resulting tool, Crown, scales to real-world codebases (half a million lines of code in less than 10 seconds) and achieves a high conversion rate.

Ontologies and taxonomies of research fields are critical for managing and organising scientific knowledge, as they facilitate efficient classification, dissemination and retrieval of information. However, the creation and maintenance of such ontologies are expensive and time-consuming tasks, usually requiring the coordinated effort of multiple domain experts. Consequently, ontologies in this space often exhibit uneven coverage across different disciplines, limited inter-domain connectivity, and infrequent updating cycles. In this study, we investigate the capability of several large language models to identify semantic relationships among research topics within three academic domains: biomedicine, physics, and engineering. The models were evaluated under three distinct conditions: zero-shot prompting, chain-of-thought prompting, and fine-tuning on existing ontologies. Additionally, we assessed the cross-domain transferability of fine-tuned models by measuring their performance when trained in one domain and subsequently applied to a different one. To support this analysis, we introduce PEM-Rel-8K, a novel dataset consisting of over 8,000 relationships extracted from the most widely adopted taxonomies in the three disciplines considered in this study: MeSH, PhySH, and IEEE. Our experiments demonstrate that fine-tuning LLMs on PEM-Rel-8K yields excellent performance across all disciplines.

We present a new variant of the potential game and show that certain compact subsets of $\R^n$, including a large class of self-affine sets, are winning in our game. We prove that sets with sufficiently strong winning conditions are non-empty, provide a lower bound for their Hausdorff dimension, show that they have good intersection properties, and provide conditions under which, given $M \in \N$, they contain a homothetic copy of every set with at most $M$ elements. The applications of our game to self-affine sets are new and complement the recent work of Yavicoli et al (Math. Z. 2022 and Int. Math. Res. Not. IMRN 2023) for self-similar sets.

In this research, we explored the efficacy of various warning label designs for AI-generated content on social media platforms e.g., deepfakes. We devised and assessed ten distinct label design samples that varied across the dimensions of sentiment, color/iconography, positioning, and level of detail. Our experimental study involved 911 participants randomly assigned to these ten label designs and a control group evaluating social media content. We explored their perceptions relating to 1. Belief in the content being AI-generated, 2. Trust in the labels and 3. Social Media engagement perceptions of the content. The results demonstrate that the presence of labels had a significant effect on the users belief that the content is AI generated, deepfake, or edited by AI. However their trust in the label significantly varied based on the label design. Notably, having labels did not significantly change their engagement behaviors, such as like, comment, and sharing. However, there were significant differences in engagement based on content type: political and entertainment. This investigation contributes to the field of human computer interaction by defining a design space for label implementation and providing empirical support for the strategic use of labels to mitigate the risks associated with synthetically generated media.

We study the evolution of the bar fraction in disc galaxies between $0.5 < z < 4.0$ using multi-band coloured images from JWST CEERS. These images were classified by citizen scientists in a new phase of the Galaxy Zoo project called GZ CEERS. Citizen scientists were asked whether a strong or weak bar was visible in the host galaxy. After considering multiple corrections for observational biases, we find that the bar fraction decreases with redshift in our volume-limited sample (n = 398); from $25^{+6}_{-4}$% at 0.5 &lt; z &lt; 1.0 to $3^{+6}_{-1}$% at 3.0 &lt; z &lt; 4.0. However, we argue it is appropriate to interpret these fractions as lower limits. Disentangling real changes in the bar fraction from detection biases remains challenging. Nevertheless, we find a significant number of bars up to $z = 2.5$. This implies that discs are dynamically cool or baryon-dominated, enabling them to host bars. This also suggests that bar-driven secular evolution likely plays an important role at higher redshifts. When we distinguish between strong and weak bars, we find that the weak bar fraction decreases with increasing redshift. In contrast, the strong bar fraction is constant between 0.5 &lt; z &lt; 2.5. This implies that the strong bars found in this work are robust long-lived structures, unless the rate of bar destruction is similar to the rate of bar formation. Finally, our results are consistent with disc instabilities being the dominant mode of bar formation at lower redshifts, while bar formation through interactions and mergers is more common at higher redshifts.

We present morphological classifications of over 41,000 galaxies out to $z_{\rm phot}\sim2.5$ across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey. Galaxy Zoo citizen scientists play a crucial role in the examination of large astronomical data sets through crowdsourced data mining of extragalactic imaging. This iteration, Galaxy Zoo: Cosmic Dawn (GZCD), saw tens of thousands of volunteers and the deep learning foundation model Zoobot collectively classify objects in ultra-deep multiband Hyper Suprime-Cam (HSC) imaging down to a depth of $m_{HSC-i} = 21.5$. Here, we present the details and general analysis of this iteration, including the use of Zoobot in an active learning cycle to improve both model performance and volunteer experience, as well as the discovery of 51 new gravitational lenses in the EDFN. We also announce the public data release of the classifications for over 45,000 subjects, including more than 41,000 galaxies (median $z_{\rm phot}$ of $0.42\pm0.23$), along with their associated image cutouts. This data set provides a valuable opportunity for follow-up imaging of objects in the EDFN as well as acting as a truth set for training deep learning models for application to ground-based surveys like that of the newly operational Vera C. Rubin Observatory.

Recent JWST/NIRSpec observations have revealed strong methane emission at 3.326 microns in the $\approx$482 K brown dwarf CWISEP J193518.59$-$154620.3 (W1935). Atmospheric modeling suggests the presence of a $\approx$300 K thermal inversion in its upper atmosphere, potentially driven by auroral activity. We present an extension of the retrieved spectra of W1935 with and without inversion spanning 1--20 microns, to identify thermal inversion-sensitive spectral features and explore the origin of the object's peculiar characteristics. Our analysis indicates that atmospheric heating contributes approximately 15% to the bolometric luminosity. The model with inversion predicts an additional similar-strength methane emission feature at 7.7 microns and tentative ammonia emission features in the mid-infrared. Wavelengths beyond $\sim$2 microns are significantly influenced by the inversion, except for the 4.1--5.0 microns CO$_2$ and CO features that originate from atmospheric layers deeper than the region where the inversion occurs. W1935 appears as an outlier in Spitzer/IRAC mid-infrared color-magnitude diagrams (CMDs) based on the $m_{\rm Ch1}-m_{\rm Ch2}$ (IRAC 3.6 microns $-$ 4.5 microns) color, but exhibits average behavior in all other combinations that trace clear sequences. This anomaly is likely due to the Ch2 filter probing vertical mixing-sensitive CO$_2$ and CO features that do not correlate with temperature or spectral type. We find that the thermal inversion tends to produce bluer $m_{\rm Ch1}-m_{\rm Ch2}$ colors, so the overluminous and/or redder position of W1935 in diagrams involving this color cannot be explained by the thermal inversion. This analysis provides insights into the intriguing dispersion of cold brown dwarfs in mid-infrared CMDs and sheds light on their spectral diversity.

Systematic literature reviews (SLRs) play an essential role in summarising, synthesising and validating scientific evidence. In recent years, there has been a growing interest in using machine learning techniques to automate the identification of relevant studies for SLRs. However, the lack of standardised evaluation datasets makes comparing the performance of such automated literature screening systems difficult. In this paper, we analyse the citation screening evaluation datasets, revealing that many of the available datasets are either too small, suffer from data leakage or have limited applicability to systems treating automated literature screening as a classification task, as opposed to, for example, a retrieval or question-answering task. To address these challenges, we introduce CSMeD, a meta-dataset consolidating nine publicly released collections, providing unified access to 325 SLRs from the fields of medicine and computer science. CSMeD serves as a comprehensive resource for training and evaluating the performance of automated citation screening models. Additionally, we introduce CSMeD-FT, a new dataset designed explicitly for evaluating the full text publication screening task. To demonstrate the utility of CSMeD, we conduct experiments and establish baselines on new datasets.

We introduce SpectraPyle, a versatile spectral stacking pipeline developed for the Euclid mission's NISP spectroscopic surveys, aimed at extracting faint emission lines and spectral features from large galaxy samples in the Wide and Deep Surveys. Designed for computational efficiency and flexible configuration, SpectraPyle supports the processing of extensive datasets critical to Euclid's non-cosmological science goals. We validate the pipeline using simulated spectra processed to match Euclid's expected final data quality. Stacking enables robust recovery of key emission lines, including Halpha, Hbeta, [O III], and [N II], below individual detection limits. However, the measurement of galaxy properties such as star formation rate, dust attenuation, and gas-phase metallicity are biased at stellar mass below log10(M*/Msol) ~ 9 due to the flux-limited nature of Euclid spectroscopic samples, which cannot be overcome by stacking. The SFR-stellar mass relation of the parent sample is recovered reliably only in the Deep survey for log10(M*/Msol) > 10, whereas the metallicity-mass relation is recovered more accurately over a wider mass range. These limitations are caused by the increased fraction of redshift measurement errors at lower masses and fluxes. We examine the impact of residual redshift contaminants that arises from misidentified emission lines and noise spikes, on stacked spectra. Even after stringent quality selections, low-level contamination (< 6%) has minimal impact on line fluxes due to the systematically weaker emission of contaminants. Percentile-based analysis of stacked spectra provides a sensitive diagnostic for detecting contamination via coherent spurious features at characteristic wavelengths. While our simulations include most instrumental effects, real Euclid data will require further refinement of contamination mitigation strategies.

Transits in the planetary system WASP-4 were recently found to occur 80s earlier than expected in observations from the TESS satellite. We present 22 new times of mid-transit that confirm the existence of transit timing variations, and are well fitted by a quadratic ephemeris with period decay dP/dt = -9.2 +/- 1.1 ms/yr. We rule out instrumental issues, stellar activity and the Applegate mechanism as possible causes. The light-time effect is also not favoured due to the non-detection of changes in the systemic velocity. Orbital decay and apsidal precession are plausible but unproven. WASP-4b is only the third hot Jupiter known to show transit timing variations to high confidence. We discuss a variety of observations of this and other planetary systems that would be useful in improving our understanding of WASP-4 in particular and orbital decay in general.

Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. "#diffuse"), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100% accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly-labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled datasets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code Zoobot. Zoobot is accessible to researchers with no prior experience in deep learning.

The integration of Environmental, Social, and Governance (ESG) factors into corporate decision-making is a fundamental aspect of sustainable finance. However, ensuring that business practices align with evolving regulatory frameworks remains a persistent challenge. AI-driven solutions for automatically assessing the alignment of sustainability reports and non-financial disclosures with specific ESG activities could greatly support this process. Yet, this task remains complex due to the limitations of general-purpose Large Language Models (LLMs) in domain-specific contexts and the scarcity of structured, high-quality datasets. In this paper, we investigate the ability of current-generation LLMs to identify text related to environmental activities. Furthermore, we demonstrate that their performance can be significantly enhanced through fine-tuning on a combination of original and synthetically generated data. To this end, we introduce ESG-Activities, a benchmark dataset containing 1,325 labelled text segments classified according to the EU ESG taxonomy. Our experimental results show that fine-tuning on ESG-Activities significantly enhances classification accuracy, with open models such as Llama 7B and Gemma 7B outperforming large proprietary solutions in specific configurations. These findings have important implications for financial analysts, policymakers, and AI researchers seeking to enhance ESG transparency and compliance through advanced natural language processing techniques.

This white paper explores the potential for strategic synergies between the JWST and the Ariel telescopes, two flagship observatories poised to revolutionise the study of exoplanet atmospheres. Both telescopes have the potential to address common fundamental questions about exoplanets-especially concerning their nature and origins-and serve a growing scientific community. With their operations now anticipated to overlap, starting from 2030, there is a unique opportunity to enhance the scientific outputs of both observatories through coordinated efforts. In this report, authored by the Ariel-JWST Synergy Working Group, part of the Ariel Consortium Science Team, we summarise the capabilities of JWST and Ariel; we highlight their key differences, similarities, synergies, and distinctive strengths. Ariel is designed to conduct a broad survey of exoplanet atmospheres but remains highly flexible, allowing the mission to integrate insights from JWST's discoveries. Findings from JWST, including data from initiatives shaped by NASA's decadal survey priorities and community-driven research themes, will inform the development of Ariel's core survey strategy. Conversely, Ariel's ability to perform broad-wavelength coverage observations for bright targets provides complementary avenues for exoplanet researchers, particularly those interested in time-domain observations and large-scale atmospheric studies. This paper identifies key pathways for fostering JWST-Ariel synergies, many of which can be initiated even before Ariel's launch. Leveraging their complementary designs and scopes, JWST and Ariel can jointly address fundamental questions about the nature, formation, and evolution of exoplanets. Such strategic collaboration has the potential to maximise the scientific returns of both observatories and lay the foundation for future facilities in the roadmap to exoplanet exploration.

Numerous methods and pipelines have recently emerged for the automatic extraction of knowledge graphs from documents such as scientific publications and patents. However, adapting these methods to incorporate alternative text sources like micro-blogging posts and news has proven challenging as they struggle to model open-domain entities and relations, typically found in these sources. In this paper, we propose an enhanced information extraction pipeline tailored to the extraction of a knowledge graph comprising open-domain entities from micro-blogging posts on social media platforms. Our pipeline leverages dependency parsing and classifies entity relations in an unsupervised manner through hierarchical clustering over word embeddings. We provide a use case on extracting semantic triples from a corpus of 100 thousand tweets about digital transformation and publicly release the generated knowledge graph. On the same dataset, we conduct two experimental evaluations, showing that the system produces triples with precision over 95% and outperforms similar pipelines of around 5% in terms of precision, while generating a comparatively higher number of triples.

We report high-resolution, high-signal-to-noise, observations of the extremely metal-poor double-lined spectroscopic binary CS 22876-032. The system has a long period : P = 424.7 $\pm$ 0.6 days. It comprises two main sequence stars having effective temperatures 6300 K and 5600 K, with a ratio of secondary to primary mass of 0.89 $\pm$ 0.04. The metallicity of the system is [Fe/H] = -3.71 $\pm$ 0.11 $\pm$ 0.12 (random and systematic errors) -- somewhat higher than previous estimates. We find [Mg/Fe] = 0.50, typical of values of less extreme halo material. [Si/Fe], [Ca/Fe], and [Ti/Fe], however, all have significantly lower values, ~ 0.0-0.1, suggesting that the heavier elements might have been underproduced relative to Mg in the material from which this object formed. In the context of the hypothesis that the abundance patterns of extremely metal-poor stars are driven by individual enrichment events and the models of Woosley and Weaver (1995), the data for CS 22876-032 are consistent with its having been enriched by a zero-metallicity supernova of mass 30 M$_{\odot}$. As the most metal-poor near-main-sequence-turnoff star currently known, the primary of the system has the potential to strongly constrain the primordial lithium abundance. We find A(Li) (= log(N(Li)/N(H)) + 12.00) = 2.03 $\pm$ 0.07, which is consistent with the finding of Ryan et al. (1999) that for stars of extremely low metallicity A(Li) is a function of [Fe/H].

Given graphs $G_1,\ldots,G_s$ all on the same vertex set and a graph $H$ with $e(H) \leq s$, a copy of $H$ is transversal or rainbow if it contains at most one edge from each $G_c$. When $s=e(H)$, such a copy contains exactly one edge from each $G_i$. We study the case when $H$ is spanning and explore how the regularity blow-up method, that has been so successful in the uncoloured setting, can be used to find transversals. We provide the analogues of the tools required to apply this method in the transversal setting. Our main result is a blow-up lemma for transversals that applies to separable bounded degree graphs $H$. Our proofs use weak regularity in the $3$-uniform hypergraph whose edges are those $xyc$ where $xy$ is an edge in the graph $G_c$. We apply our lemma to give a large class of spanning $3$-uniform linear hypergraphs $H$ such that any sufficiently large uniformly dense $n$-vertex $3$-uniform hypergraph with minimum vertex degree $\Omega(n^2)$ contains $H$ as a subhypergraph. This extends work of Lenz, Mubayi and Mycroft.