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The Annual Literacy Survey is a survey for teachers and other school staff, asking about their attitudes to literacy as a whole, as well as reading, writing and speaking and listening. There were responses from 1,535 teachers from across primary, secondary and other settings. As outlined in report, the survey particularly interested in how this would differ across primary and secondary settings, as well as by different roles and levels of seniority. Teaching writing, as suggested by the report on children and young people's writing in 2023, plays a key role in supporting writing enjoyment (Clark, Bonafede, Picton & Cole, 2023). In this report, open-ended comments showed appreciation of prescriptive structures and support provided by teachers.

In 2022, for the first time since 2015, as part of the Annual Literacy Survey, the National Literacy Trust ran a survey for teachers and other school staff, asking about their attitudes to literacy as a whole, as well as reading, writing and speaking and listening. They received responses from 1,535 teachers from across primary, secondary and other settings. The survey was anonymous, and asked teachers to tell about the sorts of provision they had in their schools, and also about their own attitudes to literacy and what they saw their role to be. The authors were particularly interested in how this would differ across primary and secondary settings, as well as by different roles and levels of seniority. The below findings outline how teachers feel about and interact with a range of literacy issues. The hope is that these findings will help inform literacy provision and practice, providing support and inspiration for teachers and other educators. This report forms part of a series, alongside reading and writing, that outline the findings in relation to these specific areas.

In 2022, for the first time since 2015, as part of the Annual Literacy Survey, the National Literacy Trust ran a survey for teachers and other school staff, asking about their attitudes to literacy as a whole, as well as reading, writing and speaking and listening. They received responses from 1,535 teachers from across primary, secondary and other settings. The survey was anonymous, and asked teachers to tell about the sorts of provision they had in their schools, and also about their own attitudes to literacy and what they saw their role to be. The authors were particularly interested in how this would differ across primary and secondary settings, as well as by different roles and levels of seniority. Children and young people's reading for enjoyment was at an all-time low in 2023, with just 2 in 5 (43.4%) 8- to 18-year-olds saying that they enjoyed reading (Clark, Picton & Galway, 2023). As the release of the new Reading Framework (DfE, 2023) signposted, teachers have a key role to play in addressing this, both as reading role models and through explicit teaching of reading strategies. To achieve this, teachers should consider themselves as readers (Cremin et al, 2009) as well as receiving training on reading instruction. With this in mind, the survey asked teachers about their own reading habits and enjoyment, as well as looking at the training they had received and what whole-school reading approaches might be in place. This report forms part of a series, alongside writing and whole-school literacy, that outline the findings in relation to these specific areas.

Recent advancements in large language model(LLM) performance on medical multiple choice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. Particularly in low-and middle-income countries (LMICs) facing acute physician shortages and lack of specialists, LLMs offer a potentially scalable pathway to enhance healthcare access and reduce costs. However, their effectiveness in the Global South, especially across the African continent, remains to be established. In this work, we introduce AfriMed-QA, the first large scale Pan-African English multi-specialty medical Question-Answering (QA) dataset, 15,000 questions (open and closed-ended) sourced from over 60 medical schools across 16 countries, covering 32 medical specialties. We further evaluate 30 LLMs across multiple axes including correctness and demographic bias. Our findings show significant performance variation across specialties and geographies, MCQ performance clearly lags USMLE (MedQA). We find that biomedical LLMs underperform general models and smaller edge-friendly LLMs struggle to achieve a passing score. Interestingly, human evaluations show a consistent consumer preference for LLM answers and explanations when compared with clinician answers.

We present a detailed study of the large-scale shock front in Stephan's Quintet, a byproduct of past and ongoing interactions. Using integral-field spectroscopy from the new William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE), recent 144 MHz observations from the LOFAR Two-metre Sky Survey (LoTSS), and archival data from the Very Large Array and James Webb Space Telescope (JWST), we obtain new measurements of key shock properties and determine its impact on the system. Harnessing the WEAVE large integral field unit's (LIFU) field of view (90 $\times$ 78 arcsec$^{2}$), spectral resolution ($R\sim2500$) and continuous wavelength coverage across the optical band, we perform robust emission line modeling and dynamically locate the shock within the multi-phase intergalactic medium (IGM) with higher precision than previously possible. The shocking of the cold gas phase is hypersonic, and comparisons with shock models show that it can readily account for the observed emission line ratios. In contrast, we demonstrate that the shock is relatively weak in the hot plasma visible in X-rays (with Mach number of $\mathcal{M} \sim 2 - 4$), making it inefficient at producing the relativistic particles needed to explain the observed synchrotron emission. Instead, we propose that it has led to an adiabatic compression of the medium, which has increased the radio luminosity ten-fold. Comparison of the Balmer line-derived extinction map with the molecular gas and hot dust observed with JWST suggests that pre-existing dust may have survived the collision, allowing the condensation of H$_{2}$ - a key channel for dissipating the shock energy., Comment: 23 pages, 15 figures, accepted for publication in MNRAS

We characterise the co-evolution of radio-loud AGN and their galaxies by mapping the dependence of radio-loud AGN activity on stellar mass and star-formation rate (SFR) across cosmic time (out to $z \sim 1.5$). Deep LOFAR radio observations are combined with large galaxy samples to study the incidence of radio-loud AGN across the galaxy population; the AGN are further split into low-excitation radio galaxies (LERGs) and high-excitation radio galaxies (HERGs). We find that LERG activity occurs over a wide range of SFRs, whereas HERGs are typically found in galaxies with ongoing star formation. The LERGs are then split based on their SFRs relative to the main sequence, across redshift. Within quiescent galaxies, LERG activity shows a steep stellar mass dependence with the same normalisation across the past $\sim$ 10 Gyr; this indicates that hot gas fuels LERGs in quiescent galaxies across cosmic time. In massive galaxies ($\log_{10}(M/\rm{M_{\odot}}) \gtrsim 11$), the incidence of LERGs is roughly constant across the galaxy population, suggesting that LERGs in massive galaxies may be fuelled by hot gas regardless of the star-formation activity. At lower masses, however, LERG activity is significantly more enhanced (by a factor of up to 10) in star-forming galaxies compared to quiescent galaxies; this suggests that an additional fuelling mechanism, likely associated with cold gas, may fuel the LERGs in galaxies with higher SFRs. We find that HERGs typically accrete above 1 per cent of the Eddington-scaled accretion rate, and the LERGs typically accrete below this level., Comment: Accepted for publication in MNRAS, 18 pages, 10 figures

Both star formation (SF) and Active Galactic Nuclei (AGN) play an important role in galaxy evolution. Statistically quantifying their relative importance can be done using radio luminosity functions. Until now these relied on galaxy classifications, where sources with a mixture of radio emission from SF and AGN are labelled as either a star-forming galaxy or an AGN. This can cause the misestimation of the relevance of AGN. Brightness temperature measurements at 144 MHz with the International LOFAR telescope can separate radio emission from AGN and SF. We use the combination of sub-arcsec and arcsec resolution imaging of 7,497 sources in the Lockman Hole and ELAIS-N1 fields to identify AGN components in the sub-arcsec resolution images and subtract them from the total flux density, leaving flux density from SF only. We construct, for the first time, radio luminosity functions by physical process, either SF or AGN activity, revealing a hidden AGN population at $L_{\textrm{144MHz}}$$<10^{24}$ W$\,$Hz$^{-1}$ . This population is 1.56$\pm$0.06 more than expected for $0.5

We introduce the LUNA neutron detector array developed for the investigation of the 13C(a,n)16O reaction towards its astrophysical s-process Gamow peak in the low-background environment of the Laboratori Nazionali del Gran Sasso (LNGS). Eighteen 3He counters are arranged in two different configurations (in a vertical and a horizontal orientation) to optimize neutron detection effciency, target handling and target cooling over the investigated energy range Ea;lab = 300 - 400 keV (En = 2.2 - 2.6 MeV in emitted neutron energy). As a result of the deep underground location, the passive shielding of the setup and active background suppression using pulse shape discrimination, we reached a total background rate of 1.23 +- 0.12 counts/hour. This resulted in an improvement of two orders of magnitude over the state of the art allowing a direct measurement of the 13C(a,n)16O cross-section down to Ea;lab = 300 keV. The absolute neutron detection efficiency of the setup was determined using the 51V(p,n)51Cr reaction and an AmBe radioactive source, and completed with a Geant4 simulation. We determined a (34+-3) % and (38+-3) % detection efficiency for the vertical and horizontal configurations, respectively, for En = 2.4 MeV neutrons., Comment: 8 pages, 5 figures, to be published in NIMA

The MeerKAT International GHz Tiered Extragalactic Exploration Survey (MIGHTEE) is one of the large survey projects using the MeerKAT telescope, covering four fields that have a wealth of ancillary data available. We present Data Release 1 of the MIGHTEE continuum survey, releasing total intensity images and catalogues over $\sim$20 deg$^2$, across three fields at $\sim$1.2-1.3 GHz. This includes 4.2 deg$^2$ over the Cosmic Evolution Survey (COSMOS) field, 14.4 deg$^2$ over the XMM Large-Scale Structure (XMM-LSS) field and deeper imaging over 1.5 deg$^2$ of the Extended Chandra Deep Field South (CDFS). We release images at both a lower resolution (7-9 arcsec) and higher resolution ($\sim 5$ arcsec). These images have central rms sensitivities of $\sim$1.3$-$2.7 $\mu $Jy beam$^{-1}$ ($\sim$1.2$-$3.6 $\mu $Jy beam$^{-1}$) in the lower (higher) resolution images respectively. We also release catalogues comprised of $\sim$144~000 ($\sim$114 000) sources using the lower (higher) resolution images. We compare the astrometry and flux-density calibration with the Early Science data in the COSMOS and XMM-LSS fields and previous radio observations in the CDFS field, finding broad agreement. Furthermore, we extend the source counts at the $\sim$10 $\mu$Jy level to these larger areas ($\sim 20$ deg$^2$) and, using the areal coverage of MIGHTEE we measure the sample variance for differing areas of sky. We find a typical sample variance of 10-20 per cent for 0.3 and 0.5 sq. deg. sub-regions at $S_{1.4} \leq 200$ $\mu $Jy, which increases at brighter flux densities, given the lower source density and expected higher galaxy bias for these sources., Comment: 25 pages, 13 Figures, Accepted to MNRAS

Active Galactic Nuclei (AGN) are objects located in the heart of galaxies which emit powerful and complex radiation across the electromagnetic spectrum. Understanding AGN has become a topic of interest due to their importance in galactic evolution and their ability to act as a probe to the distant Universe. Within the next few years, wide-field surveys such as the Legacy Survey of Space and Time (LSST) at the Rubin Vera Observatory are expected to increase the number of known AGN to $\mathcal{O} (10^{7})$ and the number of strongly lensed AGN to $\mathcal{O} (10^{4})$. In this paper we introduce \texttt{Amoeba}: an AGN Model of Optical Emission Beyond steady-state Accretion discs. The goal of \texttt{Amoeba} is to provide a modular and flexible modelling environment for AGN, in which all components can interact with each other. Through this work we describe the framework for major AGN components to vary self-consistently and keep flux distributions to connect these components to spatial dependent processes. We model properties beyond traditional single-component models, such as the reverberation of the corona's bending power law power spectrum through the accretion disc and broad line region (BLR). We simulate obscuration by the dusty torus and differential magnification of the disc and BLR due to microlensing. These features are joined together to create some of the most realistic light curve simulations to date. \texttt{Amoeba} takes a step forward in AGN modelling by joining the accretion disc, BLR, torus, intrinsic signal, and microlensing into a coherent model.

Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing to fit simulated LSST 10-year quasar light curves. The driving variability is reconstructed using a latent stochastic differential equation, a physically motivated, generative deep learning method that can model continuous-time stochastic dynamics. By embedding these physical processes into our network, we achieve a model that is more robust and interpretable. We also use transformers to scale our model to tens of millions of parameters. We demonstrate how our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wavebands, even for out-of-distribution driving signals. Our approach provides a powerful and scalable framework that can be adapted to solve other inverse problems in multivariate time series with irregular sampling., Comment: 31 pages, 19 figures

The first detection of the quasiperiodic ultrafast outflow in the ASASSN-20qc system was reported by Pasham et al. (2024). The outflow is revealed in the soft X-ray spectra as an absorption feature, which is periodically enhanced every $ \sim 8.3$ days. The periodic nature of the ultrafast outflow is best explained by an orbiting massive perturber, most likely an intermediate-mass black hole (IMBH), that is inclined with respect to the accretion flow around the primary supermassive black hole (SMBH). In this way, the orbiting body pushes the disc gas into the outflow funnel, where it is accelerated by the ordered magnetic field (Sukov\'a et al. 2021). Quasiperiodic ultrafast outflows (QPOuts) are thus a novel phenomenon that can help reveal new extreme-/intemediate-mass ratio inspiral (EMRI/IMRI) candidates that otherwise do not exhibit significant periodic patterns in the continuum flux density., Comment: 10 pages, 4 figures, submitted to the Proceedings of the IAU; based on the talk presented at IAUS 389 (XXXII IAU GA) in Cape Town, South Africa

We present the first results of the JWST Emission Line Survey (JELS). Utilising the first NIRCam narrow-band imaging at 4.7$\mu$m, over 63 arcmin$^{2}$ in the PRIMER/COSMOS field, we identified 609 emission line galaxy candidates. From these, we robustly selected 35 H$\alpha$ star-forming galaxies at $z \sim 6.1$, with H$\alpha$ star-formation rates ($\rm{SFR_{H\alpha}}$) $\sim0.9-15\ \rm{M_{\odot} \ yr^{-1}}$. Combining our unique H$\alpha$ sample with the exquisite panchromatic data in the field, we explored their physical properties and star-formation histories, and compared these to a broad-band selected sample at $z\sim 6$ which offered vital new insights into the nature of high-redshift galaxies. UV-continuum slopes ($\beta$) were considerably redder for our H$\alpha$ sample ($\langle\beta\rangle\sim-1.92$) compared to the broad-band sample ($\langle\beta\rangle\sim-2.35$). This was not due to dust attenuation as our H$\alpha$ sample was relatively dust-poor (median $A_V=0.23$); instead, we argued the reddened slopes could be due to nebular continuum. We compared $\rm{SFR_{H\alpha}}$ and the UV-continuum-derived $\rm{SFR_{UV}}$ to SED-fitted measurements averaged over canonical timescales of 10 and 100 Myr ($\rm{SFR_{10}}$ and $\rm{SFR_{100}}$). We found an increase in recent SFR for our sample of H$\alpha$ emitters, particularly at lower stellar masses ($<10^9 \ \rm{M_{\odot}}$). We also found $\rm{SFR_{H\alpha}}$ strongly traced SFR averaged over 10 Myr timescales, whereas the UV-continuum over-predicts SFR on 100 Myr timescales at low stellar masses. These results point to our H$\alpha$ sample undergoing `bursty' star formation. Our F356W $z \sim 6$ sample showed a larger scatter in $\rm{SFR_{10}/SFR_{100}}$ across all stellar masses, which highlighted how narrow-band photometric selections of H$\alpha$ emitters are key to quantifying the burstiness of star-formation activity., Comment: Submitted to MRNAS; 27 pages, 19 figures. Accompanying data will be publicly released upon acceptance of this manuscript

Located 10.888 pc from Earth, COCONUTS-2b is a planetary-mass companion to a young (150-800 Myr) M3 star, with a wide orbital separation (6471 au) and a low companion-to-host mass ratio ($0.021\pm0.005$). We have studied the atmospheric properties of COCONUTS-2b using newly acquired 1.0-2.5 $\mu$m spectroscopy from Gemini/Flamingos-2. The spectral type of COCONUTS-2b is refined to T$9.5 \pm 0.5$ based on comparisons with T/Y dwarf spectral templates. We have conducted an extensive forward-modeling analysis, comparing the near-infrared spectrum and mid-infrared broadband photometry with sixteen state-of-the-art atmospheric model grids developed for brown dwarfs and self-luminous exoplanets near the T/Y transition. The PH$_{3}$-free ATMO2020++, ATMO2020++, and Exo-REM models best match the specific observations of COCONUTS-2b, regardless of variations in the input spectrophotometry. This analysis suggests the presence of disequilibrium chemistry, along with a diabatic thermal structure and/or clouds, in the atmosphere of COCONUTS-2b. All models predict fainter $Y$-band fluxes than observed, highlighting uncertainties in the alkali chemistry models and opacities. We determine a bolometric luminosity of $\log{(L_{\rm bol}/L_{\odot})}=-6.18$ dex, with a 0.5 dex-wide range of $[-6.43,-5.93]$ dex that accounts for various assumptions of models. Using thermal evolution models, we derive an effective temperature of $T_{\rm eff}=483^{+44}_{-53}$ K, a surface gravity of $\log{(g)}=4.19^{+0.18}_{-0.13}$ dex, a radius of $R=1.11^{+0.03}_{-0.04}$ R$_{\rm Jup}$, and a mass of $M=8 \pm 2$ M$_{\rm Jup}$. Various atmospheric model grids consistently indicate that COCONUTS-2b's atmosphere has sub- or near-solar metallicity and C/O. These findings provide valuable insights into COCONUTS-2b's formation history and the potential outward migration to its current wide orbit., Comment: Accepted for publication in AJ. Main text: Pages 1-25, Figures 1-11, Tables 1-4; Appendix: Pages 26-43, Figures 12-15. Mostly unchanged from the previous version, except for footnotes 6-15, which were updated based on suggestions from the AJ data editor. The Gemini/F2 spectrum of COCONUTS-2b is accessible via https://doi.org/10.5281/zenodo.13975825

The existence of a population of massive quiescent galaxies with little to no star-formation poses a challenge to our understanding of galaxy evolution. The physical process that quenched the star formation in these galaxies is debated, but the most popular possibility is that feedback from supermassive black holes lifts or heats the gas that would otherwise be used to form stars. In this paper, we evaluate this idea in two ways. First, we compare the cumulative growth in the cosmic inventory of the total stellar mass in quiescent galaxies to the corresponding growth in the amount of kinetic energy carried by radio jets. We find that these two inventories are remarkably well-synchronized, with about half of the total amounts being created in the epoch from z of 1 to 2. We also show that these agree extremely well with the corresponding growth in the cumulative number of major mergers that result in massive (over 100 billion solar masses) galaxies. We therefore argue that major mergers trigger the radio jets and also transform the galaxies from disks to spheroids. Second, we evaluate the total amount of kinetic energy delivered by jets and compare it to the baryonic binding energy of the galaxies. We find the jet kinetic energy is more than sufficient to quench star-formation, and the quenching process should be more effective in more massive galaxies. We show that these results are quantitatively consistent with recent measurements of the Sunyaev-Zeldovich effect seen in massive galaxies at z of 1., Comment: Submitted to ApJ

We present the JWST Emission Line Survey (JELS), a Cycle 1 JWST imaging programme exploiting the wavelength coverage and sensitivity of NIRCam to extend narrow-band rest-optical emission line selection into the epoch of reionization (EoR) for the first time, and to enable unique studies of the resolved ionised gas morphology in individual galaxies across cosmic history. The primary JELS observations comprise $\sim4.7\mu$m narrow-band imaging over $\sim63$ arcmin$^{2}$ designed to enable selection of H$\alpha$ emitters at $z\sim6.1$, as well as the selection of a host of novel emission-line samples including [OIII] at $z\sim8.3$ and Pa $\alpha/\beta$ at $z\sim1.5/2.8$. For the prime F466N and F470N narrow-band observations, the emission-line sensitivities achieved are up to $\sim2\times$ more sensitive than current slitless spectroscopy surveys (5$\sigma$ limits of 1.1-1.6$\times10^{-18}\text{erg s}^{-1}\text{cm}^{-2}$), corresponding to unobscured H$\alpha$ star-formation rates (SFRs) of 1-1.6 $\text{M}_{\odot}\,\text{yr}^{-1}$ at $z\sim6.1$ and extending emission-line selections in the EoR to fainter populations. Simultaneously, JELS also obtained F200W broadband and F212N narrow-band imaging (H$\alpha$ at $z\sim2.23$) that probes SFRs $\gtrsim5\times$ fainter than previous ground-based narrow-band studies ($\sim0.2 \text{M}_{\odot}\text{yr}^{-1}$), offering an unprecedented resolved view of star formation at cosmic noon. In this paper we describe the detailed JELS survey design, key data processing steps specific to the survey observations, and demonstrate the exceptional data quality and imaging sensitivity achieved. We then summarise the key scientific goals of JELS and present some early science results, including examples of spectroscopically confirmed H$\alpha$ and [OIII] emitters discovered by JELS that illustrate the novel parameter space probed., Comment: Submitted to MRNAS; 13 pages, 8 figures. Accompanying data will be publicly released upon acceptance of this and companion paper (Pirie et al., arXiv:2410.11808). V2 updated to reflect revised author list and cross-citation to Pirie et al

We formalize a complete proof of the regular case of Fermat's Last Theorem in the Lean4 theorem prover. Our formalization includes a proof of Kummer's lemma, that is the main obstruction to Fermat's Last Theorem for regular primes. Rather than following the modern proof of Kummer's lemma via class field theory, we prove it by using Hilbert's Theorems 90-94 in a way that is more amenable to formalization.

To address the discrepancy where disk sizes exceed those predicted by standard models, we explore two extensions to disk size estimates within the UV/optical wavelength range: disk winds and color correction. We provide detailed, self-consistent derivations and analytical formulas, including those based on a power-law temperature approximation, offering efficient tools for analyzing observational data. Applying our model to four type I AGNs with intensive reverberation mapping observations, we find a shallower temperature slope ($T\propto R^{-0.66}$, compared to $R^{-3/4}$ traditionally) and a color correction factor ($f_{\rm col} \approx 1.6$), consistent with previous studies. We observe a positive correlation between accretion rate and color correction with black hole mass. However, the small sample size limits our conclusions. The strong degeneracy between the temperature slope and accretion rate suggests that incorporating flux spectra or spectral energy distributions could improve fitting accuracy. Our simulation approach rapidly generates quasar light curves while accommodating various observational scenarios for reverberation mapping, making it well-suited for training machine learning algorithms., Comment: 18 pages, 10 figures

Exploration is a critical challenge in robotics, centered on understanding unknown environments. In this work, we focus on robots exploring structured indoor environments which are often predictable and composed of repeating patterns. Most existing approaches, such as conventional frontier approaches, have difficulty leveraging the predictability and explore with simple heuristics such as `closest first'. Recent works use deep learning techniques to predict unknown regions of the map, using these predictions for information gain calculation. However, these approaches are often sensitive to the predicted map quality or do not reason over sensor coverage. To overcome these issues, our key insight is to jointly reason over what the robot can observe and its uncertainty to calculate probabilistic information gain. We introduce MapEx, a new exploration framework that uses predicted maps to form probabilistic sensor model for information gain estimation. MapEx generates multiple predicted maps based on observed information, and takes into consideration both the computed variances of predicted maps and estimated visible area to estimate the information gain of a given viewpoint. Experiments on the real-world KTH dataset showed on average 12.4% improvement than representative map-prediction based exploration and 25.4% improvement than nearest frontier approach., Comment: 7 pages

We present an analysis of 152,355 radio sources identified in the second data release of the LOFAR Two Metre Sky Survey (LoTSS-DR2) with Sloan Digital Sky Survey (SDSS) spectroscopic redshifts in the range 0.00 < z < 0.57. Using Monte Carlo simulations we determine the reliability of each source exhibiting an excess in radio luminosity relative to that predicted from their Ha emission, and, for a subset of 124,023 sources we combine this measurement with a full BPT analysis. Using these two independent diagnostics we determine the reliability of each source hosting a supermassive black hole of high or low Eddington-scaled accretion rate, and combine the measurements to determine the reliability of sources belonging to each of four physical classes of objects: star forming galaxies (SFGs), radio-quiet active galactic nuclei (RQAGN), and high- or low-excitation radio galaxies (HERGs or emission-line LERGs). The result is a catalogue which enables user-defined samples of radio sources with a reliability threshold suited to their science goal e.g. prioritising purity or completeness. Here we select high-confidence samples of radio sources (>90% reliability) to report: 38,588 radio-excess AGN in the LoTSS DR2 sample (362 HERGs, and 12,648 emission-line LERGs), together with 38,729 SFGs, and 18,726 RQAGN. We validate these results through comparison to literature using independent emission-line measurements, and to widely-adopted WISE photometric selection techniques. While our use of SDSS spectroscopy limits our current analysis to ~4 percent of the LoTSS-DR2 catalogue, our method is directly applicable to data from the forthcoming WEAVE-LOFAR survey which will obtain over a million spectra of 144 MHz selected sources., Comment: Accepted for publication in MNRAS

Upcoming wide field surveys such as the Rubin Observatory's Legacy Survey of Space and Time (LSST) will monitor thousands of strongly lensed quasars over a 10-year period. Many of these monitored quasars will undergo high magnification events (HMEs) through microlensing as the accretion disk crosses a caustic, places of infinite magnification. Microlensing allows us to map the inner regions of the accretion disk as it crosses a caustic, even at large cosmological distances. The observational cadences of LSST are not ideal for probing the inner regions of the accretion disk, so there is a need to predict HMEs as early as possible to trigger high-cadence multi-band or spectroscopic follow-up observations. Here we simulate a diverse and realistic sample of 10-year quasar microlensing light curves to train a recurrent neural network (RNN) to predict HMEs before they occur by classifying the location of the peaks at each time step. This is the first deep learning approach to predict HMEs. We give estimates at how well we expect to predict HME peaks during LSST and benchmark how our metrics change with different cadence strategies. With LSST-like observations, we can predict approximately 55% of HME peaks corresponding to tens to hundreds per year and a false positive rate of around 20% compared to the number of HMEs. Our network can be continuously applied throughout the LSST survey, providing crucial alerts to optimize follow-up resources., Comment: 16 pages, 9 figures, submitted to ApJ

The gravitational wave (GW) interferometers LISA and ET are expected to be functional in the next decade(s), possibly around the same time. They will operate over different frequency ranges, with similar integrated sensitivities to the amplitude of a stochastic GW background (SGWB). We investigate the synergies between these two detectors, in terms of a multi-band detection of a cosmological SGWB characterised by a large amplitude, and a broad frequency spectrum. By investigating various examples of SGWBs, such as those arising from cosmological phase transition, cosmic string, primordial inflation, we show that LISA and ET operating together will have the opportunity to assess more effectively the characteristics of the GW spectrum produced by the same cosmological source, but at separate frequency scales. Moreover, the two experiments in tandem can be sensitive to features of early universe cosmic expansion before big-bang nucleosynthesis (BBN), which affects the SGWB frequency profile, and which would not be possible to detect otherwise, since two different frequency ranges correspond to two different pre-BBN (or post-inflationary) epochs. Besides considering the GW spectrum, we additionally undertake a preliminary study of the sensitivity of LISA and ET to soft limits of higher order tensor correlation functions. Given that these experiments operate at different frequency bands, their synergy constitutes an ideal direct probe of squeezed limits of higher order GW correlators, which can not be measured operating with a single instrument only., Comment: 44 pages, 10 figures

The role of radio mode AGN feedback on galaxy evolution is still under debate. In this study, we utilize a combination of radio continuum observations and optical integral field spectroscopic (IFS) data to explore the impact of radio AGN on the evolution of their host galaxies at both global and sub-galactic scales. We construct a comprehensive radio-IFS sample comprising 5578 galaxies with redshift z < 0.15 by cross-matching the LOFAR Two-Metre Sky Survey (LoTSS) with the MaNGA survey. We revisit the tight linear radio continuum - star formation relation and quantify its intrinsic scatter, then use the relation to classify 616 radio-excess AGN with excessive radio luminosities over that expected from their star formation rate. Massive radio AGN host galaxies are predominantly quiescent systems, but the quenching level shows no correlation with the jet luminosity. The mass assembly histories derived from the stellar population synthesis model fitting agree with the cosmological simulations incorporating radio-mode AGN feedback models. We observe that radio AGN hosts grow faster than a control sample of galaxies matched in stellar mass, and the quenching age (~ 5 Gyr) is at larger lookback times than the typical radio jet age (< 1 Gyr). By stacking the spectra in different radial bins and comparing results for radio AGN hosts and their controls, we find emission line excess features in the nuclear region of radio AGN hosts. This indicates that radio AGN are ionizing the surrounding interstellar medium in the vicinity of the nucleus. We also find that ongoing star formation in the outer regions of the galaxy is weaker if a radio jet is detected. Our findings support the scenario that the observed present-day radio AGN activity is not responsible for the past quenching of their hosts, but may help the host galaxies maintain quiescence through ionizing and heating the surrounding gas., Comment: 16 pages, 15 figures, submitted to A&A

The CNO cycle is one of the most important nuclear energy sources in stars. At temperatures of hydrostatic H-burning (20 MK $<$ T $<$ 80 MK) the $^{17}$O(p,$\gamma$)$^{18}$F reaction rate is dominated by the poorly constrained 64.5~keV resonance. Here we report on the first direct measurements of its resonance strength and of the direct capture contribution at 142 keV, performed with a new high sensitivity setup at LUNA. The present resonance strength of $\omega\gamma_{(p, \gamma)}$\textsuperscript{bare} = (30 $\pm$ 6\textsubscript{stat} $\pm$ 2\textsubscript{syst})~peV is about a factor of 2 higher than the values in literature, leading to a $\Gamma$\textsubscript{p}\textsuperscript{bare} = (34 $\pm$ 7\textsubscript{stat} $\pm$ 3\textsubscript{syst})~neV, in agreement with LUNA result from the (p,$\alpha$) channel. Such agreement strengthen our understanding of the oxygen isotopic ratios measured in red giant stars and in O-rich presolar grains.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Biomolecular condensates form by phase separation of biological polymers. The cellular functions of the resulting membraneless organelles are closely linked to their physical properties over a wide range of length- and timescales: From the nanosecond dynamics of individual molecules and their interactions, to the microsecond translational diffusion of molecules in the condensates, to their viscoelastic properties at the mesoscopic scale. However, it has remained unclear how to quantitatively link these properties across scales. Here we address this question by combining single-molecule fluorescence, correlation spectroscopy, microrheology, and large-scale molecular dynamics simulations on different condensates that are formed by complex coacervation and span about two orders of magnitude in viscosity and their dynamics at the molecular scale. Remarkably, we find that the absolute timescale of protein chain dynamics in the dense phases can be quantitatively and accurately related to translational diffusion and condensate viscosities by Rouse theory of polymer solutions including entanglement. The simulations indicate that the observed wide range of dynamics arises from different contact lifetimes between amino acid residues, which in the mean-field description of the polymer model cause differences in the friction acting on the chains. These results suggest that remarkably simple physical principles can relate the mesoscale properties of biomolecular condensates to their dynamics at the nanoscale.

The effects of thermal treatments on metallic glass nanolaminates (NLs), with a composition of Zr$_{24}$Cu$_{76}$ and Zr$_{61}$Cu$_{39}$ and a bilayer period of 50 nm, were explored to control their mechanical properties through annealing-induced atomic structure modifications, structural relaxation, and partial crystallisation. Annealing treatments up to 330 {\deg}C ($T$ < $T_g$, the glass transition temperature) maintain the amorphous structure of the NLs, while inducing atomic structural relaxation, densification, and free volume annihilation, reducing the formation of corrugations on fracture surfaces. Atom probe tomography measurements reveal that annealing at 330 {\deg}C for 60 mins also causes intermixing between layers, altering their compositions to Zr$_{44}$Cu$_{56}$ and Zr$_{55}$Cu$_{45}$ with increased mixing enthalpy. Moreover, the NLs exhibit superior thermal stability against crystallisation compared to their monolithic counterparts, remaining amorphous up to 420 {\deg}C, while the monolithic Zr$_{24}$Cu$_{76}$ and Zr$_{61}$Cu$_{39}$ films are crystalline at 390 {\deg}C, as a result of chemical interdiffusion and the heterogeneous NL structure of delaying the onset of crystallisation. Annealing treatments $T$ > $T_g$ ($\sim$420 {\deg}C) induce partial crystallisation, forming Cu-Zr-based intermetallic and Zr-oxide phases, whereas annealing $T$ < $T_g$ (330 {\deg}C) retains a visible layer structure. Nanoindentation analyses show a progressive increase in elastic modulus and hardness for higher annealing temperatures as a result of structural relaxation and likely nanocrystal formation, with a maximum hardness equal to 7.6 $\pm$ 0.2 GPa obtained after heat treatment at 420 {\deg}C for 60 mins and exceeding the rule-of-mixtures. These results highlight the potential of thermal treatments to tailor the structural, mechanical and thermal properties of metallic glass NLs.

We present the deepest wide-field 115-166 MHz image at sub-arcsecond resolution spanning an area of 2.5 by 2.5 degrees centred at the ELAIS-N1 deep field. To achieve this, we improved the calibration for the International LOFAR Telescope. This enhancement enabled us to efficiently process 32 hrs of data from four different 8-hr observations using the high-band antennas (HBAs) of all 52 stations, covering baselines up to approximately 2,000 km across Europe. The DI calibration was improved by using an accurate sky model and refining the series of calibration steps on the in-field calibrator, while the DD calibration was improved by adopting a more automated approach for selecting the DD calibrators and inspecting the self-calibration on these sources. We also added an additional round of self-calibration for the Dutch core and remote stations in order to refine the solutions for shorter baselines. To complement our highest resolution at 0.3", we also made intermediate resolution wide-field images at 0.6" and 1.2". Our resulting wide-field images achieve a central noise level of 14 muJy/beam at 0.3", doubling the depth and uncovering four times more objects than the Lockman Hole deep field image at comparable resolution but with only 8 hrs of data. Compared to LOFAR imaging without the international stations, we note that due to the increased collecting area and the absence of confusion noise, we reached a point-source sensitivity comparable to a 500-hr ELAIS-N1 6" image with 16 times less observing time. Importantly, we have found that the computing costs for the same amount of data are almost halved (to about 139,000 CPU hrs per 8 hrs of data) compared to previous efforts, though they remain high. Our work underscores the value and feasibility of exploiting all Dutch and international LOFAR stations to make deep wide-field images at sub-arcsecond resolution., Comment: Images and catalogues will be available on https://lofar-surveys.org/hd-en1.html

The aim of this study is to probe the sub-mJy polarized source population with LOFAR. We present the method used to stack LOFAR polarization datasets, the resulting catalog of polarized sources, and the derived polarized source counts. The ELAIS-N1 field was selected for a polarimetric study at 114.9-177.4 MHz. A total area of 25 deg2 was imaged at 6"- resolution in the Stokes Q and U parameters. Alignment of polarization angles was done both in frequency and in Faraday space before stacking datasets from 19 eight-hour-long epochs. A search for polarized sources was carried out in the final, stacked dataset, and the properties of the detected sources were examined. The depolarization level of sources known to be polarized at 1.4 GHz was quantified. A one-sigma noise level of 19 {\mu}Jy/beam was reached in the central part of the field after stacking. Twenty-five polarized sources were detected above 8\sigma, five of which had not been detected in polarization at any other radio frequencies before. Seven additional polarized components were found by lowering the threshold to 6\sigma at positions corresponding to sources known to be polarized at 1.4 GHz. In two radio galaxies, polarization was detected from both radio lobes, so the final number of associated radio continuum sources is 31. The detected sources are weakly polarized, with a median degree of polarization of 1.75% for the sample of sources detected in polarized emission. The sources previously detected in polarization at 1.4 GHz are significantly depolarized at 150 MHz. The catalog is used to derive the polarized source counts at 150 MHz. This is the deepest and highest-resolution polarization study at 150 MHz to date., Comment: 18 pages, 10 figures, accepted for publication in A&A

The TEMPO (Transiting Exosatellites, Moons, and Planets in Orion) Survey is a proposed 30-day observational campaign using the Nancy Grace Roman Space Telescope. By providing deep, high-resolution, short-cadence infrared photometry of a dynamic star-forming region, TEMPO will investigate the demographics of exosatellites orbiting free-floating planets and brown dwarfs -- a largely unexplored discovery space. Here, we present the simulated detection yields of three populations: extrasolar moon analogs orbiting free-floating planets, exosatellites orbiting brown dwarfs, and exoplanets orbiting young stars. Additionally, we outline a comprehensive range of anticipated scientific outcomes accompanying such a survey. These science drivers include: obtaining observational constraints to test prevailing theories of moon, planet, and star formation; directly detecting widely separated exoplanets orbiting young stars; investigating the variability of young stars and brown dwarfs; constraining the low-mass end of the stellar initial mass function; constructing the distribution of dust in the Orion Nebula and mapping evolution in the near-infrared extinction law; mapping emission features that trace the shocked gas in the region; constructing a dynamical map of Orion members using proper motions; and searching for extragalactic sources and transients via deep extragalactic observations reaching a limiting magnitude of $m_{AB}=29.7$\,mag (F146 filter)., Comment: 15 pages, 6 figures, submitted to OJAp

Most robotic hands and grippers rely on actuators with large gearboxes and force sensors for controlling gripping force. However, this might not be ideal for tasks that require the robot to interact with an unstructured and unknown environment. In this paper, we introduce a novel quasi-direct-drive two-fingered robotic hand with variable impedance control in the joint space and Cartesian space. The hand has a total of four degrees of freedom, backdrivable differential gear trains, and four brushless direct current (BLDC) motors. Motor torque is controlled through Field-Oriented Control (FOC) with current sensing. Variable impedance control enables the robotic hand to execute dexterous manipulation tasks safely during environment-robot and human-robot interactions. The quasi-direct-drive actuators eliminate the need for complex tactile/force sensors or precise motion planning when handling environmental contact. A majority-3D-printed assembly makes this a low-cost research platform built with affordable, readily available off-the-shelf components. Experimental validation demonstrates the robotic hand's capability for stable force-closure and form-closure grasps in the presence of disturbances, reliable in-hand manipulation, and safe dynamic manipulations despite contact with the environment., Comment: 8 pages

Modern high-sensitivity radio telescopes are discovering an increased number of resolved sources with intricate radio structures and fainter radio emissions. These sources often present a challenge because source detectors might identify them as separate radio sources rather than components belonging to the same physically connected radio source. Currently, there are no reliable automatic methods to determine which radio components are single radio sources or part of multi-component sources. We propose a deep learning classifier to identify those sources that are part of a multi-component system and require component association on data from the LOFAR Two-Metre Sky Survey (LoTSS). We combine different types of input data using multi-modal deep learning to extract spatial and local information about the radio source components: a convolutional neural network component that processes radio images is combined with a neural network component that uses parameters measured from the radio sources and their nearest neighbours. Our model retrieves 94 per cent of the sources with multiple components on a balanced test set with 2,683 sources and achieves almost 97 per cent accuracy in the real imbalanced data (323,103 sources). The approach holds potential for integration into pipelines for automatic radio component association and cross-identification. Our work demonstrates how deep learning can be used to integrate different types of data and create an effective solution for managing modern radio surveys., Comment: 20 pages, 14 figures, accepted for publication in MNRAS

We synthesize JWST NIRCam photometry for the F164N, F187N, F212N narrow filters, F140M, F162M, F182M, F210M medium filters, and F115W, F150W, F200W wide filters, Euclid Near Infrared Spectrometer and Photometer (NISP) photometry for the $Y_E J_E H_E$ filters, and Roman Wide Field Instrument (WFI) photometry for the F106, F129, F146, F158, F184 and F213 filters using SpeX prism spectra and parallaxes of 688 field-age and 151 young ($\lesssim$ 200 Myr) ultracool dwarfs (spectral types M6-T9). We derive absolute magnitude-spectral type polynomial relations that enable the calculation of photometric distances for ultracool dwarfs observed with JWST, and to be observed with Euclid and Roman, in the absence of parallax measurements. Additionally, using the synthesized photometry to generate color-color figures can help distinguish high-redshift galaxies from brown dwarf interlopers in survey datasets. In particular, anticipating the upcoming Euclid Early Release Observations, we provide synthetic Euclid colors for ultracool dwarfs in our sample., Comment: 6 pages, 3 figures, 1 table

In stars that lie on the main sequence in the Hertzsprung-Russel diagram, like our sun, hydrogen is fused to helium in a number of nuclear reaction chains and series, such as the proton-proton chain and the carbon-nitrogen-oxygen cycles. Precisely determined thermonuclear rates of these reactions lie at the foundation of the standard solar model. This review, the third decadal evaluation of the nuclear physics of hydrogen-burning stars, is motivated by the great advances made in recent years by solar neutrino observatories, putting experimental knowledge of the proton-proton chain neutrino fluxes in the few-percent precision range. The basis of the review is a one-week community meeting held in July 2022 in Berkeley, California, and many subsequent digital meetings and exchanges. The relevant reactions of solar and stellar hydrogen burning are reviewed here, from both theoretical and experimental perspectives. Recommendations for the state of the art of the astrophysical S-factor and its uncertainty are formulated for each of them. Several other topics of paramount importance for the solar model are reviewed, as well: recent and future neutrino experiments, electron screening, radiative opacities, and current and upcoming experimental facilities. In addition to reaction-specific recommendations, also general recommendations are formed., Comment: 101 pages, 25 figures, including supplementary material. Revised version submitted to Reviews of Modern Physics

Advances in underwater imaging enable the collection of extensive seafloor image datasets that are necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering expedient mobilization of this crucial environmental information. Recent machine learning approaches provide opportunities to increase the efficiency with which seafloor image datasets are analyzed, yet large and consistent datasets necessary to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 2.6 million annotations translated to the CATAMI scheme, which span 190,000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for use by the scientific community at https://doi.org/10.20383/103.0614.

Spectral energy distribution (SED) fitting has been extensively used to determine the nature of the faint radio source population. Recent efforts have combined fits from multiple SED-fitting codes to account for the host galaxy and any active nucleus that may be present. We show that it is possible to produce similar-quality classifications using a single energy-balance SED fitting code, Prospector, to model up to 26 bands of UV$-$far-infrared aperture-matched photometry for $\sim$31,000 sources in the ELAIS-N1 field from the LOFAR Two-Metre Sky Survey (LoTSS) Deep fields first data release. One of a new generation of SED-fitting codes, Prospector accounts for potential contributions from radiative active galactic nuclei (AGN) when estimating galaxy properties, including star formation rates (SFRs) derived using non-parametric star formation histories. Combining this information with radio luminosities, we classify 92 per cent of the radio sources as a star-forming galaxy, high/low-excitation radio galaxy, or radio-quiet AGN and study the population demographics as a function of 150 MHz flux density, luminosity, SFR, stellar mass, redshift and apparent $r$-band magnitude. Finally, we use Prospector SED fits to investigate the SFR$-$150 MHz luminosity relation for a sample of $\sim$$133,000~3.6~\mu$m-selected $z<1$ sources, finding that the stellar mass dependence is significantly weaker than previously reported, and may disappear altogether at $\log_{10} (\mathrm{SFR}/M_\odot~\mathrm{yr}^{-1}) > 0.5$. This approach makes it significantly easier to classify radio sources from LoTSS and elsewhere, and may have important implications for future studies of star-forming galaxies at radio wavelengths., Comment: 20 pages, 13 figures, accepted for publication in MNRAS

Macroscopic intelligibility models predict the expected human word-error-rate for a given speech-in-noise stimulus. In contrast, microscopic intelligibility models aim to make fine-grained predictions about listeners' perception, e.g. predicting phonetic or lexical responses. State-of-the-art macroscopic models use transfer learning from large scale deep learning models for speech processing, whereas such methods have rarely been used for microscopic modeling. In this paper, we study the use of transfer learning from Whisper, a state-of-the-art deep learning model for automatic speech recognition, for microscopic intelligibility prediction at the level of lexical responses. Our method outperforms the considered baselines, even in a zero-shot setup, and yields a relative improvement of up to 66\% when fine-tuned to predict listeners' responses. Our results showcase the promise of large scale deep learning based methods for microscopic intelligibility prediction.