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Student dropout is a significant concern for educational institutions due to its social and economic impact, driving the need for risk prediction systems to identify at-risk students before enrollment. We explore the accuracy of such systems in the context of higher education by predicting degree completion before admission, with potential applications for prioritizing admissions decisions. Using a large-scale dataset from Danish higher education admissions, we demonstrate that advanced sequential AI models offer more precise and fair predictions compared to current practices that rely on either high school grade point averages or human judgment. These models not only improve accuracy but also outperform simpler models, even when the simpler models use protected sociodemographic attributes. Importantly, our predictions reveal how certain student profiles are better matched with specific programs and fields, suggesting potential efficiency and welfare gains in public policy. We estimate that even the use of simple AI models to guide admissions decisions, particularly in response to a newly implemented nationwide policy reducing admissions by 10 percent, could yield significant economic benefits. However, this improvement would come at the cost of reduced human oversight and lower transparency. Our findings underscore both the potential and challenges of incorporating advanced AI into educational policymaking.

Folktales are linguistically very rich and culturally significant in understanding the source language. Historically, only human translation has been used for translating folklore. Therefore, the number of translated texts is very sparse, which limits access to knowledge about cultural traditions and customs. We have created a new Ukrainian-To-English parallel corpus of familiar Ukrainian folktales based on available English translations and suggested several new ones. We offer a combined domain-specific approach to building and augmenting this corpus, considering the nature of the domain and differences in the purpose of human versus machine translation. Our corpus is word and sentence-aligned, allowing for the best curation of meaning, specifically tailored for use as training data for machine translation models.

Social networks shape individuals' lives, influencing everything from career paths to health. This paper presents a registry-based, multi-layer and temporal network of the entire Danish population in the years 2008-2021 (roughly 7.2 mill. individuals). Our network maps the relationships formed through family, households, neighborhoods, colleagues and classmates. We outline key properties of this multiplex network, introducing both an individual-focused perspective as well as a bipartite representation. We show how to aggregate and combine the layers, and how to efficiently compute network measures such as shortest paths in large administrative networks. Our analysis reveals how past connections reappear later in other layers, that the number of relationships aggregated over time reflects the position in the income distribution, and that we can recover canonical shortest path length distributions when appropriately weighting connections. Along with the network data, we release a Python package that uses the bipartite network representation for efficient analysis.

An image is often said to be worth a thousand words, and certain images can tell rich and insightful stories. Can these stories be told via image captioning? Images from folklore genres, such as mythology, folk dance, cultural signs, and symbols, are vital to every culture. Our research compares the performance of four popular vision-language models (GPT-4V, Gemini Pro Vision, LLaVA, and OpenFlamingo) in identifying culturally specific information in such images and creating accurate and culturally sensitive image captions. We also propose a new evaluation metric, Cultural Awareness Score (CAS), dedicated to measuring the degree of cultural awareness in image captions. We provide a dataset MOSAIC-1.5k, labeled with ground truth for images containing cultural background and context, as well as a labeled dataset with assigned Cultural Awareness Scores that can be used with unseen data. Creating culturally appropriate image captions is valuable for scientific research and can be beneficial for many practical applications. We envision that our work will promote a deeper integration of cultural sensitivity in AI applications worldwide. By making the dataset and Cultural Awareness Score available to the public, we aim to facilitate further research in this area, encouraging the development of more culturally aware AI systems that respect and celebrate global diversity.

In this study, we employ a classification approach to show that different categories of literary "quality" display unique linguistic profiles, leveraging a corpus that encompasses titles from the Norton Anthology, Penguin Classics series, and the Open Syllabus project, contrasted against contemporary bestsellers, Nobel prize winners and recipients of prestigious literary awards. Our analysis reveals that canonical and so called high-brow texts exhibit distinct textual features when compared to other quality categories such as bestsellers and popular titles as well as to control groups, likely responding to distinct (but not mutually exclusive) models of quality. We apply a classic machine learning approach, namely Random Forest, to distinguish quality novels from "control groups", achieving up to 77\% F1 scores in differentiating between the categories. We find that quality category tend to be easier to distinguish from control groups than from other quality categories, suggesting than literary quality features might be distinguishable but shared through quality proxies.

The Cosmic Gems arc is among the brightest and highly magnified galaxies observed at redshift $z\sim10.2$. However, it is an intrinsically UV faint galaxy, in the range of those now thought to drive the reionization of the Universe. Hitherto the smallest features resolved in a galaxy at a comparable redshift are between a few hundreds and a few tens of parsecs. Here we report JWST observations of the Cosmic Gems. The light of the galaxy is resolved into five star clusters located in a region smaller than 70 parsec. They exhibit minimal dust attenuation and low metallicity, ages younger than 50 Myr and intrinsic masses of $\sim10^6$ M$_{\odot}$. Their lensing-corrected sizes are approximately 1 pc, resulting in stellar surface densities near $10^5$~M$_{\odot}$/pc$^2$, three orders of magnitude higher than typical young star clusters in the local universe. Despite the uncertainties inherent to the lensing model, they are consistent with being gravitationally bound stellar systems, i.e., proto-globular clusters. We conclude that star cluster formation and feedback likely contributed to shape the properties of galaxies during the epoch of reionization. [Abridged], Comment: Accepted for publication

The nanoscale contrast in scattering-type scanning near-field optical microscopy (s-SNOM) is determined by the optical properties of the sample immediately under the apex of the tip of the atomic force microscope (AFM). There are several models that describe the optical scattering of an incident field by the tip near a surface, and these models have been successful in relating the measured scattering signal to the dielectric function of the sample under the tip. Here, we address a situation that is normally not considered in the existing interaction models, namely the near-field signal arising from thin, highly conductive films in the terahertz (THz) frequency range. According to established theoretical models, highly conductive thin films should show insignificant contrast in the THz range for small variations in conductivity, therefore hindering the use of s-SNOM for nanoscale characterisation. We experimentally demonstrate unexpected but clear and quantifiable layer contrast in the THz s-SNOM signal from few-layer exfoliated graphene as well as subtle nanoscale contrast variations within graphene layers. We use finite-element simulations to confirm that the observed contrast is described by the classical electromagnetics of the scattering mechanism, suggesting that the dipole models must be reformulated to correctly describe the interaction with conductive samples.

We present a spatially resolved stellar population analysis of 61 jellyfish galaxies and 47 control galaxies observed with ESO/MUSE attempting to understand the general trends of the stellar populations as a function of the stripping intensity and mass. This is the public sample from the GASP programme, with $0.01 < z < 0.15$ and $8.9 <\log(M_{\star}/M_{\odot}) < 12.0$. We apply the spectral population synthesis code FADO to fit self-consistently both the stellar and nebular contributions to the spectra of the sources. We present 2D morphological maps for mean stellar ages, metallicities, gas-phase oxygen abundances, and star formation rates for the galaxies with Integrated Nested Laplace Approximation (INLA), which is efficient in reconstructing spatial data of extended sources. We find that "extreme stripping" and "stripping" galaxies are typically younger than the other types. Regarding stellar and nebular metallicities, the "stripping" and "control passive" galaxies are the most metal-poor. Based on the phase space for jellyfish cluster members we find trends in ages, metallicities, and abundances with different regions of the diagram. We also compute radial profiles for the same quantities. We find that both the stripping and the stellar masses seem to influence the profiles, and we see differences between various groups and distinct mass bins. The radial profiles for different mass bins present relations already shown in the literature for undisturbed galaxies, i.e., profiles of ages and metallicities tend to increase with mass. However, beyond $\sim0.75$ effective radius, the ages of the most massive galaxies become similar to or lower than the ages of the lower mass ones., Comment: Accepted by MNRAS. 71 pages (13 of main text and 58 of supplementay material). 123 figures

\noindent \textbf{Background:} Detailed spectroscopy of neutron-rich, heavy, deformed nuclei is of broad interest for nuclear astrophysics and nuclear structure. Nuclei in the r-process path and following freeze-out region impact the resulting r-process abundance distribution, and the structure of nuclei midshell in both proton and neutron number helps to understand the evolution of subshell gaps and large deformation in these nuclei. \noindent \textbf{Purpose:} To improve the understanding of the nuclear structure of $^{160}$Gd, specifically the $K^\pi$=$4^+$ bands, as well as study the $\beta$-decay of $^{160}$Eu into $^{160}$Gd. \noindent \textbf{Methods:} High-statistics decay spectroscopy of $^{160}$Gd resulting from the $\beta$-decay of $^{160}$Eu was collected using the GRIFFIN spectrometer at the TRIUMF-ISAC facility. \noindent \textbf{Results:} Two new excited states and ten new transitions were observed in $^{160}$Gd. The $\beta$-decaying half-lives of the low- and high-spin isomer in $^{160}$Eu were determined, and the low-spin state's half-life was measured to be $t_{1/2}=26.0(8)$~s, $\sim$16\% shorter than previous measurements. Lifetimes of the two $K^\pi$=$4^+$ band-heads in $^{160}$Gd were measured for the first time, as well as $\gamma$-$\gamma$ angular correlations and mixing ratios of intense transitions out of those band-heads. \noindent \textbf{Conclusions:} Lifetimes and mixing ratios suggest that the hexadecapole phonon model of the $K^\pi$=$4^+$ band-heads in $^{160}$Gd is preferred over a simple two-state strong mixing scenario, although further theoretical calculations are needed to fully understand these states. Additionally, the 1999.0 keV state in $^{160}$Gd heavily populated in $\beta$-decay is shown to have positive parity, which raises questions regarding the structure of the high-spin $\beta$-decaying state in $^{160}$Eu.