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In this work, we evaluate annotator disagreement in Word-in-Context (WiC) tasks exploring the relationship between contextual meaning and disagreement as part of the CoMeDi shared task competition. While prior studies have modeled disagreement by analyzing annotator attributes with single-sentence inputs, this shared task incorporates WiC to bridge the gap between sentence-level semantic representation and annotator judgment variability. We describe three different methods that we developed for the shared task, including a feature enrichment approach that combines concatenation, element-wise differences, products, and cosine similarity, Euclidean and Manhattan distances to extend contextual embedding representations, a transformation by Adapter blocks to obtain task-specific representations of contextual embeddings, and classifiers of varying complexities, including ensembles. The comparison of our methods demonstrates improved performance for methods that include enriched and task-specfic features. While the performance of our method falls short in comparison to the best system in subtask 1 (OGWiC), it is competitive to the official evaluation results in subtask 2 (DisWiC)., Comment: Accepted to CoMeDi Shared Task at COLING 2025

Self-rewetting fluids (SRFs), such as long-chain aqueous alcohols, are a special class of liquids with surface tension that is anomalously dependent quadratically on temperature, resulting in thermocapillary flows that differ significantly from those in normal fluids (NFs). This study focuses on investigating the behavior of SRF drops interacting with a liquid layer under nonuniform heating conditions. In this regard, we employ a robust central moment-based lattice Boltzmann method with a phase-field model expressed in an axisymmetric formulation to capture three-dimensional effects efficiently. We investigate the coalescence and pinch-off processes in SRFs and compare them to those in NFs. Our simulations reveal that SRFs undergo pinch-off earlier than NFs. We also observe that increasing the Ohnesorge number Oh suppresses the pinch-off process, highlighting the relative role of viscous forces, which is modulated by gravity effects or the Bond number Bo. Furthermore, we explore how varying the dimensionless linear and quadratic sensitivity coefficients of surface tension on temperature, M1 and M2, respectively, and the dimensionless heat flux Q influence the coalescence/pinch-off behavior. Interestingly, contrary to the behavior in NFs, in SRFs increasing M2, or Q reduces the time required to pinch-off and widens the region in the Oh-Bo regime map where pinch-off occurs, when compared to the unheated cases. These differences are shown to be due to the variations in the thermocapillary forces on the interface. Overall, we find that under nonuniform heating, the SRFs enhance the pinch-off process compared to NFs across a wider range of conditions., Comment: 44 pages, 20 figures. arXiv admin note: text overlap with arXiv:2210.07501

We find a generating function for interval-closed sets of the product of two chains poset by constructing a bijection to certain bicolored Motzkin paths. We also find a functional equation for the generating function of interval-closed sets of truncated rectangle posets, including the type $A$ root poset, by constructing a bijection to certain quarter-plane walks., Comment: 21 pages, 11 figures, one table

We present a broadband and polarization-insensitive unidirectional imager that operates at the visible part of the spectrum, where image formation occurs in one direction while in the opposite direction, it is blocked. This approach is enabled by deep learning-driven diffractive optical design with wafer-scale nano-fabrication using high-purity fused silica to ensure optical transparency and thermal stability. Our design achieves unidirectional imaging across three visible wavelengths (covering red, green and blue parts of the spectrum), and we experimentally validated this broadband unidirectional imager by creating high-fidelity images in the forward direction and generating weak, distorted output patterns in the backward direction, in alignment with our numerical simulations. This work demonstrates the wafer-scale production of diffractive optical processors, featuring 16 levels of nanoscale phase features distributed across two axially aligned diffractive layers for visible unidirectional imaging. This approach facilitates mass-scale production of ~0.5 billion nanoscale phase features per wafer, supporting high-throughput manufacturing of hundreds to thousands of multi-layer diffractive processors suitable for large apertures and parallel processing of multiple tasks. Our design can seamlessly integrate into conventional optical systems, broadening its applicability in fields such as security, defense, and telecommunication. Beyond broadband unidirectional imaging in the visible spectrum, this study establishes a pathway for artificial-intelligence-enabled diffractive optics with versatile applications, signaling a new era in optical device functionality with industrial-level massively scalable fabrication., Comment: 23 Pages, 6 Figures

This letter presents the measurement of the energy-dependent neutrino-nucleon cross section in tungsten and the differential flux of muon neutrinos and anti-neutrinos. The analysis is performed using proton-proton collision data at a center-of-mass energy of $13.6 \, {\rm TeV}$ and corresponding to an integrated luminosity of $(65.6 \pm 1.4) \, \mathrm{fb^{-1}}$. Using the active electronic components of the FASER detector, $338.1 \pm 21.0$ charged current muon neutrino interaction events are identified, with backgrounds from other processes subtracted. We unfold the neutrino events into a fiducial volume corresponding to the sensitive regions of the FASER detector and interpret the results in two ways: We use the expected neutrino flux to measure the cross section, and we use the predicted cross section to measure the neutrino flux. Both results are presented in six bins of neutrino energy, achieving the first differential measurement in the TeV range. The observed distributions align with Standard Model predictions. Using this differential data, we extract the contributions of neutrinos from pion and kaon decays.

Some neutron stars known as magnetars possess very strong magnetic fields, with surface fields as large as $10^{15}\,\rm G$ and internal fields that are possibly stronger. Recent observations of the radio pulsar GLEAM-X J1627 suggest it may have a surface field as strong as $10^{16} \,\rm G$. In the presence of a strong magnetic field, the energy levels of electrons and protons are quantized and the Direct Urca process allows neutron stars to cool rapidly, even at low density. For the case of magnetic fields $B \geq 10^{16}\,\rm G$, we find features in the emissivity due to energy quantization that are not captured by the frequently employed quasiclassical approximation where energy levels are treated as nearly continuous. Resonances can result in amplification of the neutrino emissivity at specific densities compared to a calculation that neglects quantization, particularly at low temperature. These effects are not important for the thermal evolution of an entire neutron star, but may be relevant for phenomena that depend on behavior at specific densities. We present a fully relativistic calculation of the Direct Urca rate in a strong magnetic field using the standard V-A weak Lagrangian incorporating mean field nuclear effects and discuss approaches to the numerical challenge the modified wavefunctions present and a new semi-analytic approximation. These tools are also applicable to calculating neutrino opacities in strong magnetic fields in the ejecta of binary neutron star mergers. We calculate the opacities for neutrinos capturing on free nucleons at sub-saturation densities and temperatures exceeding an MeV. We find an enhancement to capture processes of the lowest energy neutrinos by an order of magnitude or more due to suppression of electron Pauli blocking in the case of capture on neutrons, and from the effect of the nucleon magnetic moments in the case of capture on protons., Comment: 14 pages, 6 figures

Diffuse Galactic cirrus, or Diffuse Galactic Light (DGL), can be a prominent component in the background of deep wide-field imaging surveys. The DGL provides unique insights into the physical and radiative properties of dust grains in our Milky Way, and it also serves as a contaminant on deep images, obscuring the detection of background sources such as low surface brightness galaxies. However, it is challenging to disentangle the DGL from other components of the night sky. In this paper, we present a technique for the photometric characterization of Galactic cirrus, based on (1) extraction of its filamentary or patchy morphology and (2) incorporation of color constraints obtained from Planck thermal dust models. Our decomposition method is illustrated using a $\sim$10 deg$^2$ imaging dataset obtained by the Dragonfly Telephoto Array, and its performance is explored using various metrics which characterize the flatness of the sky background. As a concrete application of the technique, we show how removal of cirrus allows low surface brightness galaxies to be identified on cirrus-rich images. We also show how modeling the cirrus in this way allows optical DGL intensities to be determined with high radiometric precision., Comment: 35 pages, 17 figures, accepted for publication in ApJ

When malware employs an unseen zero-day exploit, traditional security measures such as vulnerability scanners and antivirus software can fail to detect them. This is because these tools rely on known patches and signatures, which do not exist for new zero-day attacks. Furthermore, existing machine learning methods, which are trained on specific and occasionally outdated malware samples, may struggle to adapt to features in new malware. To address this issue, there is a need for a more robust machine learning model that can identify relationships between malware samples without being trained on a particular malware feature set. This is particularly crucial in the field of cybersecurity, where the number of malware samples is limited and obfuscation techniques are widely used. Current approaches using stacked autoencoders aim to remove the noise introduced by obfuscation techniques through reconstruction of the input. However, this approach ignores the semantic relationships between features across different malware samples. To overcome this limitation, we propose a novel Siamese Neural Network (SNN) that uses relation-aware embeddings to calculate more accurate similarity probabilities based on semantic details of different malware samples. In addition, by using entropy images as inputs, our model can extract better structural information and subtle differences in malware signatures, even in the presence of obfuscation techniques. Evaluations on two large malware sample sets using the N-shot and N-way methods show that our proposed model is highly effective in predicting previously unseen malware, even in the presence of obfuscation techniques.

Strongly magnetized plasmas, characterized by having a gyrofrequency larger than the plasma frequency ($\beta = \omega_c/\omega_p \gg 1$), are known to exhibit novel transport properties. Previous works studying pure electron plasmas have shown that strong magnetization significantly inhibits energy exchange between parallel and perpendicular directions, leading to a prolonged time for relaxation of a temperature anisotropy. Recent work studying repulsive electron-ion interactions showed that strong magnetization increases both the parallel and perpendicular temperature relaxation rates of ions, but in differing magnitudes, resulting in the formation of temperature anisotropy during equilibration. This previous study treated electrons as a heat bath and assumed weak magnetization of ions. Here, we broaden this analysis and compute the full temperature and temperature anisotropy evolution over a broad range of magnetic field strengths. It is found that when electrons are strongly magnetized ($\beta_e \gg 1$) and ions are weakly magnetized ($\beta_i \ll 1$), the magnetic field strongly suppresses the perpendicular energy exchange rate of electrons, whereas the parallel exchange rate slightly increases in magnitude compared to the value at weak magnetization. In contrast, the ion perpendicular and parallel energy exchange rates both increase in magnitude compared to the values at weak magnetization. Consequently, equilibration causes the electron parallel temperature to rapidly align with the ion temperature, while the electron perpendicular temperature changes much more slowly. It is also shown that when both ions and electrons are strongly magnetized ($\beta_i, \beta_e \gg 1$) the ion-electron perpendicular relaxation rate dramatically decreases with magnetization strength as well.

We present 22 GHz imaging of regions of massive star formation within the Local Wolf-Rayet Galaxy Sample (LWRGS), a NSF's Karl G. Jansky Very Large Array (VLA) survey of 30 local galaxies showing spectral features of Wolf-Rayet (WR) stars. These spectral features are present in galaxies with young super star clusters (SSCs), and are an indicator of large concentrations of massive stars. We present a catalog of 92 individually-identified regions of likely free-free emission associated with potential young SSCs located in these WR galaxies. The free-free fluxes from these maps allow extinction-free estimates of the Lyman continuum rates, masses, and luminosities of the emission regions. 39 of these regions meet the minimum Lyman continuum rate to contain at least once SSC, and 29 of these regions could contain individual SSCs massive enough to test specific theories on star formation and feedback inhibition in SSCs, requiring follow-up observations at higher spatial resolution. The resulting catalog provides sources for future molecular line and infrared studies into the properties of super star cluster formation., Comment: 26 pages, 8 figures; accepted for publication in ApJ

Causal disentanglement aims to learn about latent causal factors behind data, holding the promise to augment existing representation learning methods in terms of interpretability and extrapolation. Recent advances establish identifiability results assuming that interventions on (single) latent factors are available; however, it remains debatable whether such assumptions are reasonable due to the inherent nature of intervening on latent variables. Accordingly, we reconsider the fundamentals and ask what can be learned using just observational data. We provide a precise characterization of latent factors that can be identified in nonlinear causal models with additive Gaussian noise and linear mixing, without any interventions or graphical restrictions. In particular, we show that the causal variables can be identified up to a layer-wise transformation and that further disentanglement is not possible. We transform these theoretical results into a practical algorithm consisting of solving a quadratic program over the score estimation of the observed data. We provide simulation results to support our theoretical guarantees and demonstrate that our algorithm can derive meaningful causal representations from purely observational data.

Augmented Reality assistance are increasingly popular for supporting users with tasks like assembly and cooking. However, current practice typically provide reactive responses initialized from user requests, lacking consideration of rich contextual and user-specific information. To address this limitation, we propose a novel AR assistance system, Satori, that models both user states and environmental contexts to deliver proactive guidance. Our system combines the Belief-Desire-Intention (BDI) model with a state-of-the-art multi-modal large language model (LLM) to infer contextually appropriate guidance. The design is informed by two formative studies involving twelve experts. A sixteen within-subject study find that Satori achieves performance comparable to an designer-created Wizard-of-Oz (WoZ) system without relying on manual configurations or heuristics, thereby enhancing generalizability, reusability and opening up new possibilities for AR assistance.

The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-world settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. We improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys. We demonstrate the simulator with a tailored example in which we track agents' political positions and show how partisan manipulation of agents can affect election results.

The first FASER search for a light, long-lived particle decaying into a pair of photons is reported. The search uses LHC proton-proton collision data at $\sqrt{s}=13.6~\text{TeV}$ collected in 2022 and 2023, corresponding to an integrated luminosity of $57.7\text{fb}^{-1}$. A model with axion-like particles (ALPs) dominantly coupled to weak gauge bosons is the primary target. Signal events are characterised by high-energy deposits in the electromagnetic calorimeter and no signal in the veto scintillators. One event is observed, compared to a background expectation of $0.44 \pm 0.39$ events, which is entirely dominated by neutrino interactions. World-leading constraints on ALPs are obtained for masses up to $300~\text{MeV}$ and couplings to the Standard Model W gauge boson, $g_{aWW}$, around $10^{-4}$ GeV$^{-1}$, testing a previously unexplored region of parameter space. Other new particle models that lead to the same experimental signature, including ALPs coupled to gluons or photons, U(1)$_B$ gauge bosons, up-philic scalars, and a Type-I two-Higgs doublet model, are also considered for interpretation, and new constraints on previously viable parameter space are presented in this paper., Comment: 37 pages, 22 figures

We investigate the Lyman-$\alpha$ (Ly$\alpha$) and Lyman continuum (LyC) properties of the Sunburst Arc, a $z=2.37$ gravitationally lensed galaxy with a multiply-imaged, compact region leaking LyC and a triple-peaked Ly$\alpha$ profile indicating direct Ly$\alpha$ escape. Non-LyC-leaking regions show a redshifted Ly$\alpha$ peak, a redshifted and central Ly$\alpha$ peak, or a triple-peaked Ly$\alpha$ profile. We measure the properties of the Ly$\alpha$ profile from different regions of the galaxy using $R\sim5000$ Magellan/MagE spectra. We compare the Ly$\alpha$ spectral properties to LyC and narrowband Ly$\alpha$ maps from Hubble Space Telescope (HST) imaging to explore the subgalactic Ly$\alpha-$LyC connection. We find strong correlations (Pearson correlation coefficient $r>0.6$) between the LyC escape fraction ($f_{\rm esc}^{\rm LyC}$) and Ly$\alpha$ (1) peak separation $v_{\rm{sep}}$, (2) ratio of the minimum flux density between the redshifted and blueshifted Ly$\alpha$ peaks to continuum flux density $f_{\rm{min}}/f_{\rm{cont}}$, and (3) equivalent width. We favor a complex \ion{H}{1} geometry to explain the Ly$\alpha$ profiles from non-LyC-leaking regions and suggest two \ion{H}{1} geometries that could diffuse and/or rescatter the central Ly$\alpha$ peak from the LyC-leaking region into our sightline across transverse distances of several hundred parsecs. Our results emphasize the complexity of Ly$\alpha$ radiative transfer and its sensitivity to the anisotropies of \ion{H}{1} gas on subgalactic scales. Large differences in the physical scales on which we observe spatially variable direct escape Ly$\alpha$, blueshifted Ly$\alpha$, and escaping LyC photons in the Sunburst Arc underscore the importance of resolving the physical scales that govern Ly$\alpha$ and LyC escape., Comment: Submitted to The Astrophysical Journal with revisions from the first referee report. Comments welcome

Fusion is a potentially transformational energy technology, which promises limitless clean energy. Yet, it requires continued scientific and technological development to realize its potential. The conditions necessary for fusion energy gain in terms of the product of plasma pressure $P$ and confinement time $\tau$ have been known for many decades. An underappreciated fact is that pulsed magnetic fusion has demonstrated $P \tau$ performance on par with laser-driven ICF and tokamaks despite receiving only a small fraction of investment relative to those concepts. In light of this demonstrated performance, well-established scaling relations, and opportunities for further innovations, here we advocate for pulsed magnetic fusion as the most attractive path towards commercialization of fusion energy.

Self-rewetting fluids (SRFs) (e.g., aqueous solutions of long-chain alcohols) show anomalous nonlinear (quadratic) variations of surface tension with temperature involving a positive gradient, leading to different thermocapillary convection compared to normal fluids (NFs). Moreover, surface-active materials or surfactants can significantly alter interfacial dynamics by their adsorption on fluid interfaces. The coupled effects of temperature- and surfactant-induced Marangoni stresses on migration bubbles in SRFs remain unexplored. We use a robust lattice Boltzmann (LB) method based on central moments to simulate the two-fluid motions, capture interfaces, and compute the transport of energy and surfactant concentration fields, and systematically study the surfactant-laden bubble dynamics in SRFs. When compared to motion of bubbles in normal fluids, in which they continuously migrate without a stationary behavior, our results show that they exhibit dramatically different characteristics in SRFs. Not only is the bubble motion directed towards the minimum temperature location in SRFs, but, more importantly, the bubble attains an equilibrium position. In the absence of surfactants, such an equilibrium position arises at the minimum reference temperature occurring at the center of the domain. The addition of surfactants moves the equilibrium location further upstream, which is controlled by the magnitude of the Gibbs elasticity parameter that determines the magnitude of the surface tension variation with surfactants. The quadratic sensitivity coefficient of surface tension on temperature associated with the SRF modulates this behavior. The lower this quantity, the greater is the role of surfactants modifying the equilibrium position of the bubble in SRF. These findings provide new means to potentially manipulate the bubble dynamics, and especially to tune its equilibrium states., Comment: 57 pages, 30 figures

Let $R$ be the class of regular cardinals which are not hyperinaccessible. We show that $L[R]$, and similar inner models in the $\alpha$-inaccessible hierarchy, can be generated by iterating a small "machete" mouse up through all the ordinals, and then taking a generic extension by a hyperclass Magidor iteration of Prikry forcings. We then show that such simple mice are themselves elements of $L[\mathsf{Reg}]$., Comment: 21 pages

In this work, the novel, open-source humanoid robot, PANDORA, is presented where a majority of the structural elements are manufactured using 3D-printed compliant materials. As opposed to contemporary approaches that incorporate the elastic element into the actuator mechanisms, PANDORA is designed to be compliant under load, or in other words, structurally elastic. This design approach lowers manufacturing cost and time, design complexity, and assembly time while introducing controls challenges in state estimation, joint and whole-body control. This work features an in-depth description on the mechanical and electrical subsystems including details regarding additive manufacturing benefits and drawbacks, usage and placement of sensors, and networking between devices. In addition, the design of structural elastic components and their effects on overall performance from an estimation and control perspective are discussed. Finally, results are presented which demonstrate the robot completing a robust balancing objective in the presence of disturbances and stepping behaviors., Comment: This work has been submitted to the IEEE for possible publication

While preliminary findings indicate that multilingual LLMs exhibit reduced bias compared to monolingual ones, a comprehensive understanding of the effect of multilingual training on bias mitigation, is lacking. This study addresses this gap by systematically training six LLMs of identical size (2.6B parameters) and architecture: five monolingual models (English, German, French, Italian, and Spanish) and one multilingual model trained on an equal distribution of data across these languages, all using publicly available data. To ensure robust evaluation, standard bias benchmarks were automatically translated into the five target languages and verified for both translation quality and bias preservation by human annotators. Our results consistently demonstrate that multilingual training effectively mitigates bias. Moreover, we observe that multilingual models achieve not only lower bias but also superior prediction accuracy when compared to monolingual models with the same amount of training data, model architecture, and size., Comment: 19 pages, 8 figures, C3NLP 2024

Empathetic response generation is a desirable aspect of conversational agents, crucial for facilitating engaging and emotionally intelligent multi-turn conversations between humans and machines. Leveraging large language models for this task has shown promising results, yet challenges persist in ensuring both the empathetic quality of the responses and retention of the generalization performance of the models. We propose a novel approach where we construct theory-driven preference datasets based on emotion grounding and use them to align LLMs with preference optimization algorithms to address these challenges. To evaluate empathetic response generation, we employ the EmpatheticDialogues dataset, assessing empathy with the diff-Epitome and BERTscore metrics and with multi-dimensional human evaluation. Additionally, we measure diversity and emotional valence using feature-based methods. We also evaluate the impact of training on the generalization performance using the MMLU benchmark and tasks from the Open LLM Leaderboard. The results show that LLMs can be aligned for empathetic response generation by preference optimization while retaining their general performance and that emotion grounding can guide preference dataset creation. We make all datasets, source code, and models publicly available. https://github.com/justtherightsize/empo, Comment: v02, 8 pages long paper, EMNLP ACL style

Observations of gravitationally lensed, high-mass stars at redshifts $\gtrsim1$ occasionally reveal spectral energy distributions that contain two components with different effective temperatures. Given that two separate stars are involved, it suggests that both stars have simultaneously reached very high magnification, as expected for two stars in a binary system close to the caustic curve of the foreground galaxy-cluster lens. The inferred effective temperatures and luminosities of these stars are, however, difficult to reconcile with known binaries, or even with isolated stars of the same age. Here, we explore three alternative explanations for these cases: circumstellar dust around the cooler of the two stars; age differences of a few Myr among stars in the same star cluster, and a scenario in which the stars originate in two separate star clusters of different age along the lensing caustic. While all of these scenarios are deemed plausible in principle, dust solutions would require more circumstellar extinction than seen in local observations of the relevant super/hypergiant stars. Hence, we argue that age differences between the two stars are the most likely scenario, given the current data., Comment: 7 pages, 7 figures, 1 table, submitted to A&A

In an era characterized by rapid societal changes and complex challenges, institutions' traditional methods of problem-solving in the public sector are increasingly proving inadequate. In this study, we present an innovative and effective model for how institutions can use artificial intelligence to enable groups of people to generate effective solutions to urgent problems more efficiently. We describe a proven collective intelligence method, called Smarter Crowdsourcing, which is designed to channel the collective intelligence of those with expertise about a problem into actionable solutions through crowdsourcing. Then we introduce Policy Synth, an innovative toolkit which leverages AI to make the Smarter Crowdsourcing problem-solving approach both more scalable, more effective and more efficient. Policy Synth is crafted using a human-centric approach, recognizing that AI is a tool to enhance human intelligence and creativity, not replace it. Based on a real-world case study comparing the results of expert crowdsourcing alone with expert sourcing supported by Policy Synth AI agents, we conclude that Smarter Crowdsourcing with Policy Synth presents an effective model for integrating the collective wisdom of human experts and the computational power of AI to enhance and scale up public problem-solving processes. While many existing approaches view AI as a tool to make crowdsourcing and deliberative processes better and more efficient, Policy Synth goes a step further, recognizing that AI can also be used to synthesize the findings from engagements together with research to develop evidence-based solutions and policies. The study offers practical tools and insights for institutions looking to engage communities effectively in addressing urgent societal challenges., Comment: 51 pages, 23 figures, Submitted for ACM Collective Intelligence Conference 2024, Parallel Track Presentation, Boston, MA, USA

We consider the number of various partitions of $n$ with parts separated by parity and prove combinatorially several inequalities between these numbers. For example, we show that for $n\geq 5$ we have $p_{od}^{eu}(n)

A Ferrers rook graph is a graph whose vertices correspond to the dots in a Ferrers diagram, and where two vertices are adjacent if they are in the same row or the same column. We propose a conjectural formula for the gonality of Ferrers rook graphs, and prove this conjecture for a few infinite families of Ferrers diagrams. We also prove the conjecture for all Ferrers diagrams $F$ with $|F| \leq 8$.

"Godzilla" is a peculiar object within the gravitationally lensed Sunburst Arc at $z=2.37$. Despite being very bright, it appears in only one of the twelve lensed images of the source galaxy, and shows exotic spectroscopic properties not found in any other clumps. We use JWST's unique combination of spatial resolution and spectroscopic sensitivity to provide a unified, coherent explanation of the physical nature of Godzilla. We measure fluxes and kinematic properties of rest-optical emission lines in Godzilla and surrounding regions. Using standard line ratio-based diagnostic methods in combination with NIRCam imaging and ground based rest-UV spectra, we characterize Godzilla and its surroundings. Among around 60 detected lines, we find a cascade of strong O I lines pumped by intense Ly$\beta$ emission, as well as Ly$\alpha$-pumped rest-optical Fe II lines, reminiscent of the Weigelt blobs in the local LBV star Eta Carinae. Spectra and images of Godzilla and two faint adjacent images, and the detection of a low-surface brightness foreground galaxy in the NIRCam data, support the interpretation that Godzilla is an extremely magnified object due to the alignment with lensing caustics. We find that Godzilla is part of a previously identified clump, comprising $\sim10 - 25$ % of it, with magnifications in the range of $\approx600-25,000$ depending on the models and images in comparison. The unique O I source in Godzilla is well explained by a non-erupting LBV accompanied by a hotter companion and/or gas condensations exposed to more intense radiation compared to the Weigelt blobs. If Godzilla is confirmed to contain an LBV star, it expands the distance to the furthest known LBV from a dozen Mpc to several Gpc., Comment: 22 pages, 19 figures. Submitted to A&A

We measure the gas-phase abundances of the elements He, N, O, Ne, S, Ar, and Fe in the Lyman-continuum emitting region of the Sunburst Arc, a highly magnified galaxy at redshift $z=2.37$. We detect the temperature-sensitive auroral lines [SII]$\lambda\lambda4069,4076$, [OII]$\lambda\lambda7320,7330$, [SIII]$\lambda6312$, [OIII]$\lambda4363$, and [NeIII]$\lambda3343$ in a stacked spectrum of 5 multiple images of the Lyman-continuum emitter (LCE), from which we directly measure the electron temperature in the low, intermediate, and high ionization zones. We also detect the density-sensitive doublets of [OII]$\lambda\lambda3727,3729$, [SII]$\lambda\lambda6717,6731$, and [ArIV]$\lambda\lambda4713,4741$, which constrain the density in both the low- and high-ionization gas. With these temperature and density measurements, we measure gas-phase abundances with similar rigor as studies of local galaxies. We measure a gas-phase metallicity for the LCE of $12+\log(\textrm{O}/\textrm{H}) = 7.97 \pm 0.05$, and find an enhanced nitrogen abundance $\log(\textrm{N}/\textrm{O}) = -0.65^{+0.16}_{-0.25}$. This nitrogen abundance is consistent with enrichment from a population of Wolf-Rayet stars, additional signatures of which are reported in a companion paper. Abundances of sulfur, argon, neon, and iron are consistent with local low-metallicity HII regions and low-redshift galaxies. This study represents the most complete chemical abundance analysis of a galaxy at Cosmic Noon to date, which enables direct comparisons between local HII regions and those in the distant universe., Comment: 15 pages, 4 figures, 3 tables. Submitted to ApJ

We present JWST/NIRSpec high-resolution spectroscopy G395H/F290LP of MACS0647-JD, a gravitationally lensed galaxy merger at $z=10.167$. The new spectroscopy, which is acquired for the two lensed images (JD1 and JD2), detects and resolves emission lines in the rest-frame ultraviolet (UV) and blue optical, including the resolved [OII]3726,3729 doublet, [NeIII]3870, [HeI]3890, H$\delta$, H$\gamma$, and [OIII]4363. This is the first observation of the resolved [OII]3726,3729 doublet for a galaxy at $z>8$. We measure a line flux ratio [OII]3729/3726 $= 0.9 \pm 0.3$, which corresponds to an estimated electron density of $\log(n_{e} / \rm{cm}^{-3}) = 2.9 \pm 0.5$. This is significantly higher than the electron densities of local galaxies reported in the literature. We compile the measurements from the literature and further analyze the redshift evolution of $n_{e}$. We find that the redshift evolution follows the power-law form of $n_{e} = A\times (1+z)^{p}$ with $A=54^{+31}_{-23}$ cm$^{-3}$ and $p=1.2^{+0.4}_{-0.4}$. This power-law form may be explained by a combination of metallicity and morphological evolution of galaxies, which become, on average, more metal-poor and more compact with increasing redshift., Comment: 20 pages, 11 figures. Accepted for publication in ApJ

JWST spectroscopy has revolutionized our understanding of galaxies in the early universe. Covering wavelengths up to $5.3\,{\rm \mu m}$, NIRSpec can detect rest-frame optical emission lines H$\alpha$ out to $z = 7$ and [O III] to $z = 9.5$. Observing these lines in more distant galaxies requires longer wavelength spectroscopy with MIRI. Here we present MIRI MRS IFU observations of the lensed galaxy merger MACS0647$-$JD at $z = 10.165$. With exposure times of 4.2 hours in each of two bands, we detect H$\alpha$ at $9\sigma$, [O III]$\,\lambda5008$ at $11\sigma$, and [O III]$\,\lambda4960$ at $3\sigma$. Combined with previously reported NIRSpec spectroscopy that yields seven emission lines including the auroral line [O III]$\,\lambda4363$, we present the first direct metallicity measurement of a $z > 10$ galaxy: $12+{\rm log(O/H)}= 7.79\pm0.09$, or $0.13^{+0.02}_{-0.03}\,Z_{\odot}$. This is similar to galaxies at $z \sim 4 - 9$ with direct metallicity measurements, though higher than expected given the high specific star formation rate ${\rm log(sSFR / yr^{-1})} = -7.4 \pm 0.3$. We further constrain the ionization parameter ${\rm log}(U)$ = $-1.9 \pm 0.1$, ionizing photon production efficiency ${\rm log}(\xi_{\rm ion})$ = $25.3\pm0.1$, and star formation rate $5.0\pm0.6\,M_{\odot}/{\rm yr}$ within the past $10\,{\rm Myr}$. These observations demonstrate the combined power of JWST NIRSpec and MIRI for studying galaxies in the first $500$ million years., Comment: 16 pages, 8 figures, accepted for publication in ApJ

This position paper presents the AR Language Map, a speculative artifact designed to enhance understanding of linguistic justice among middle and high school students through augmented reality (AR) that allows students to map their linguistic experiences. Through a social justice-oriented academic outreach program aimed at linguistically, economically, and racially minoritized students, academic concepts on language, culture, race, and power are introduced to California middle school and high school students. The curriculum has activities for each lesson plan drawn from students' culturally relevant experiences. By enabling interactive exploration of linguistic justice, this tool aims to foster empathy, challenge linguistic racism, and valorize linguistic diversity. We discuss its conceptualization within the broader context of AR in social justice education. The AR Language Map not only deepens students' understanding of these critical issues but also enables them to become co-creators of their learning experiences., Comment: Presented at CHI 2024 (arXiv:2404.05889)