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Computer algebra systems are really good at factoring polynomials, i.e. writing f as a product of irreducible factors. It is relatively easy to verify that we have a factorisation, but verifying that these factors are irreducible is a much harder problem. This paper reports work-in-progress to do such verification in Lean.

This paper explores the concept of external magnetic control for vine robots to enable their high curvature steering and navigation for use in endoluminal applications. Vine robots, inspired by natural growth and locomotion strategies, present unique shape adaptation capabilities that allow passive deformation around obstacles. However, without additional steering mechanisms, they lack the ability to actively select the desired direction of growth. The principles of magnetically steered growing robots are discussed, and experimental results showcase the effectiveness of the proposed magnetic actuation approach. We present a 25 mm diameter vine robot with integrated magnetic tip capsule, including 6 Degrees of Freedom (DOF) localization and camera and demonstrate a minimum bending radius of 3.85 cm with an internal pressure of 30 kPa. Furthermore, we evaluate the robot's ability to form tight curvature through complex navigation tasks, with magnetic actuation allowing for extended free-space navigation without buckling. The suspension of the magnetic tip was also validated using the 6 DOF localization system to ensure that the shear-free nature of vine robots was preserved. Additionally, by exploiting the magnetic wrench at the tip, we showcase preliminary results of vine retraction. The findings contribute to the development of controllable vine robots for endoluminal applications, providing high tip force and shear-free navigation., Comment: 13 pages, 10 figures

Tidal disruption events (TDEs) can generate non-spherical, relativistic and optically thick outflows. Simulations show that the radiation we observe is reprocessed by these outflows. According to a unified model suggested by these simulations, the spectral energy distributions (SEDs) of TDEs depend strongly on viewing angle: low [high] optical-to-X-ray ratios (OXRs) correspond to face-on [edge-on] orientations. Post-processing with radiative transfer codes have simulated the emergent spectra, but have so far been carried out only in a quasi-1D framework, with three atomic species (H, He and O). Here, we present 2.5D Monte Carlo radiative transfer simulations which model the emission from a non-spherical outflow, including a more comprehensive set of cosmically abundant species. While the basic trend of OXR increasing with inclination is preserved, the inherently multi-dimensional nature of photon transport through the non-spherical outflow significantly affects the emergent SEDs. Relaxing the quasi-1D approximation allows photons to preferentially escape in (polar) directions of lower optical depth, resulting in a greater variation of bolometric luminosity as a function of inclination. According to our simulations, inclination alone may not fully explain the large dynamic range of observed TDE OXRs. We also find that including metals, other than Oxygen, changes the emergent spectra significantly, resulting in stronger absorption and emission lines in the extreme ultraviolet, as well a greater variation in the OXR as a function of inclination. Whilst our results support previously proposed unified models for TDEs, they also highlight the critical importance of multi-dimensional ionization and radiative transfer., Comment: 17 pages, 15 figures. Submitted to MNRAS

Van-der-Waals materials have been shown to support numerous exotic polaritonic phenomena originating from their layered structures and associated vibrational and electronic properties. This includes emergent polaritonic phenomena, including hyperbolicity and exciton-polariton formation. However, many van-der-Waals materials' unique properties are most prominent at cryogenic temperatures. This presents a particular challenge for polaritonics research, as reliable optical constant data is required for understanding light-matter coupling. For infrared polaritonics (3-100um), the small size of exfoliated flakes makes conventional ellipsometry impossible. This paper presents a cryogenic Fourier transform infrared microscope design constructed entirely from off-the-shelf components and fitting procedures for determining optical constants. We use this microscope to present the first temperature-dependent characterization of the optical properties of hexagonal boron nitride grown with isotopically pure boron. We show that Fabry Perot-type resonances close to the transverse optical phonon show the key temperature-dependent tuning of several parameters. Our full analysis of the infrared dielectric function shows small but significant tuning of the optical constants, which is highly consistent with Raman data from the literature. We then use this dielectric data to perform and analyze the polariton propagation properties, which agree extremely well with published cryogenic scattering-type nearfield microscopy results. In addition to the insights gained into hyperbolic polaritons in hBN, our paper represents a transferable framework for characterizing exfoliated infrared polaritonic materials and other infrared devices. This could accelerate discoveries in other material systems, especially those that are spatially inhomogeneous or cannot be prepared as large single crystals.

The orbital Hall effect (OHE) typically is evaluated using an intuitive approach based on semiclassical considerations. Here we provide a full quantum mechanical evaluation of the orbital current, including all matrix elements of the position operator. We recover previous results and find an additional quantum correction due to the non-commutativity of the position and velocity operators. We evaluate the full OHE for the topological antiferromagnet CuMnAs showing that the quantum correction dominates the response and changes its sign. We discuss implications for experiment., Comment: Submitted to Phys.Rev.Lett

Obtaining sub-chemical accuracy (1 kJ mol${}^{-1}$) for reaction energies of medium-sized gas-phase molecules is a longstanding challenge in the field of thermochemical modeling. The perturbative triples correction to CCSD, CCSD(T), constitutes an important component of all high-accuracy composite model chemistries that obtain this accuracy, but can be a roadblock in the calculation of medium to large systems due to its $\mathcal{O}(N^7)$ scaling, particularly in HEAT-like model chemistries that eschew separation of core and valance correlation. This study extends the work of Lesiuk [J. Chem. Phys. 156, 064103 (2022)] with new approximate methods and assesses the accuracy of five different approximations of (T) in the context of a subset of molecules selected from the W4-17 dataset. It is demonstrated that all of these approximate methods can achieve sub-0.1 kJ mol${}^{-1}$ accuracy with respect to canonical, density-fitted (T) contributions with a modest number of projectors. The approximation labeled $\tilde{Z}T$ appears to offer the best trade-off between cost and accuracy and shows significant promise in an order-of-magnitude reduction in the computational cost of the CCSD(T) component of high-accuracy model chemistries.

Machine learning techniques have recently been of great interest for solving differential equations. Training these models is classically a data-fitting task, but knowledge of the expression of the differential equation can be used to supplement the training objective, leading to the development of physics-informed scientific machine learning. In this article, we focus on one class of models called nonlinear vector autoregression (NVAR) to solve ordinary differential equations (ODEs). Motivated by connections to numerical integration and physics-informed neural networks, we explicitly derive the physics-informed NVAR (piNVAR) which enforces the right-hand side of the underlying differential equation regardless of NVAR construction. Because NVAR and piNVAR completely share their learned parameters, we propose an augmented procedure to jointly train the two models. Then, using both data-driven and ODE-driven metrics, we evaluate the ability of the piNVAR model to predict solutions to various ODE systems, such as the undamped spring, a Lotka-Volterra predator-prey nonlinear model, and the chaotic Lorenz system.

When developing robust preconditioners for multiphysics problems, fractional functions of the Laplace operator often arise and need to be inverted. Rational approximation in the uniform norm can be used to convert inverting those fractional operators into inverting a series of shifted Laplace operators. Care must be taken in the approximation so that the shifted Laplace operators remain symmetric positive definite, making them better conditioned. In this work, we study two greedy algorithms for finding rational approximations to such fractional operators. The first algorithm improves the orthogonal greedy algorithm discussed in [Li et al., SISC, 2024] by adding one minimization step in the uniform norm to the procedure. The second approach employs the weak Chebyshev greedy algorithm in the uniform norm. Both methods yield non-increasing error. Numerical results confirm the effectiveness of our proposed algorithms, which are also flexible and applicable to other approximation problems. Moreover, with effective rational approximations to the fractional operator, the resulting algorithms show good performance in preconditioning a Darcy-Stokes coupled problem.

Coupled cluster theory has had a momentous impact on the ab initio prediction of molecular properties, and remains a staple ingratiate in high-accuracy thermochemical model chemistries. However, these methods require inclusion of at least some connected quadruple excitations, which generally scale at best as $\mathcal{O}(N^9)$ with the number of basis functions. It very difficult to predict, a priori, the effect correlation past CCSD(T) has on a give reaction energies. The purpose of this work is to examine cost-effective quadruple corrections based on the factorization theorem of many-body perturbation theory that may address these challenges. We show that the $\mathcal{O}(N^7)$, factorized CCSD(TQ${}_\text{f}$) method introduces minimal error to predicted correlation and reaction energies as compared to the $\mathcal{O}(N^9)$ CCSD(TQ). Further, we examine the performance of Goodson's continued fraction method in the estimation of CCSDT(Q)${}_\Lambda$ contributions to reaction energies, as well as a "new" method related to %TAE[(T)] that we refer to as a scaled perturbation estimator. We find that the scaled perturbation estimator based upon CCSD(TQ${}_\text{f}$)/cc-pVDZ is capable of predicting CCSDT(Q)${}_\Lambda$/cc-pVDZ contributions to reaction energies with an average error of 0.07 kcal mol${}^{-1}$ and a RMST of 0.52 kcal mol${}^{-1}$ when applied to a test-suite of nearly 3000 reactions. This offers a means by which to reliably ballpark how important post-CCSD(T) contributions are to reaction energies while incurring no more than CCSD(T) formal cost and a little mental math.

Many high-state cataclysmic variables (CVs) exhibit blue-shifted absorption features in their ultraviolet (UV) spectra -- a smoking-gun signature of outflows. However, the impact of these outflows on {\em optical} spectra remains much more uncertain. During its recent outburst, the eclipsing dwarf nova V455 And displayed strong optical emission lines whose cores were narrower than expected from a Keplerian disc. Here, we explore whether disc + wind models developed for matching UV observations of CVs can also account for these optical spectra. Importantly, V455~And was extremely bright at outburst maximum: the accretion rate implied by fitting the optical continuum with a standard disc model is $\dot{M}_{\rm acc} \simeq 10^{-7}~{\rm M}_\odot~{\rm yr^{-1}}$. Allowing for continuum reprocessing in the outflow helps to relax this constraint. A disk wind can also broadly reproduce the optical emission lines, but only if the wind is (i) highly mass-loaded, with a mass-loss rate reaching $\dot{M}_{\rm wind} \simeq 0.4 \dot{M}_{\rm acc}$, and/or (ii) clumpy, with a volume filling factor $f_V \simeq 0.1$. The same models can describe the spectral evolution across the outburst, simply by lowering $\dot{M}_{\rm acc}$ and $\dot{M}_{\rm wind}$. Extending these models to lower inclinations and into the UV produces spectra consistent with those observed in face-on high-state CVs. We also find, for the first time in simulations of this type, P-Cygni-like absorption features in the Balmer series, as have been observed in both CVs and X-ray binaries. Overall, dense disc winds provide a promising framework for explaining multiple observational signatures seen in high-state CVs, but theoretical challenges persist., Comment: 13 pages, 9 figures, 3 tables. Accepted for publication in MNRAS

The Extreme Universe Space Observatory on a Super Pressure Balloon 2 (EUSO-SPB2) flew on May 13$^{\text{th}}$ and 14$^{\text{th}}$ of 2023. Consisting of two novel optical telescopes, the payload utilized next-generation instrumentation for the observations of extensive air showers from near space. One instrument, the fluorescence telescope (FT) searched for Ultra-High Energy Cosmic Rays (UHECRs) by recording the atmosphere below the balloon in the near-UV with a 1~$\mu$s time resolution using 108 multi-anode photomultiplier tubes with a total of 6,912 channels. Validated by pre-flight measurements during a field campaign, the energy threshold was estimated around 2~EeV with an expected event rate of approximately 1 event per 10 hours of observation. Based on the limited time afloat, the expected number of UHECR observations throughout the flight is between 0 and 2. Consistent with this expectation, no UHECR candidate events have been found. The majority of events appear to be detector artifacts that were not rejected properly due to a shortened commissioning phase. Despite the earlier-than-expected termination of the flight, data were recorded which provide insights into the detectors stability in the near-space environment as well as the diffuse ultraviolet emissivity of the atmosphere, both of which are impactful to future experiments.

Understanding the links between different phases of outflows from active galactic nuclei is a key goal in extragalactic astrophysics. Here we compare [OIII] $\lambda\lambda$4960,5008 outflow signatures in quasars with and without Broad Absorption Lines (BALs), aiming to test how the broad absorption troughs seen in the rest-frame ultraviolet are linked to the narrow line region outflows seen in the rest-frame optical. We present new near-infrared spectra from Magellan/FIRE which cover [OIII] in 12 quasars with 2.1 < z < 2.3, selected to have strong outflow signatures in CIV $\lambda$1550. Combining with data from the literature, we build a sample of 73 BAL, 115 miniBAL and 125 non-BAL QSOs with 1.5 < z < 2.6. The strength and velocity width of [OIII] correlate strongly with the CIV emission properties, but no significant difference is seen in the [OIII] emission-line properties between the BALs, non-BALs and miniBALs once the dependence on CIV emission is taken into account. A weak correlation is observed between the velocities of CIV BALs and [OIII] emission, which is accounted for by the fact that both outflow signatures correlate with the underlying CIV emission properties. Our results add to the growing evidence that BALs and non-BALs are drawn from the same parent population and are consistent with a scenario wherein BAL troughs are intermittent tracers of persistent quasar outflows, with a part of such outflow becoming optically thick along our line-of-sight for sporadic periods of time within which BALs are observed., Comment: 14 pages, 6 figures. Accepted for publication in MNRAS

The negatively charged boron vacancy ($\mathrm{V}_{\mathrm{B}}^-$) in hexagonal boron nitride (hBN) has garnered significant attention among defects in two-dimensional materials. This owes, in part, to its deterministic generation, well-characterized atomic structure, and optical polarizability at room temperature. We investigate the latter through extensive measurements probing both the ground and excited state polarization dynamics. We develop a semiclassical model based on these measurements that predicts a near-unity degree of spin polarization, surpassing other solid-state spin defects under ambient conditions. Building upon our model, we include the presence of nuclear spin degrees of freedom adjacent to the $\mathrm{V}_{\mathrm{B}}^-$ and perform a comprehensive set of Lindbladian numerics to investigate the hyperfine-induced polarization of the nuclear spins. Our simulations predict a number of important features that emerge as a function of magnetic field which are borne out by experiment.

Black hole (BH) superradiance can provide strong constraints on the properties of ultralight bosons (ULBs). Since most of the previous work has focused on the theoretical predictions, here we investigate the most suitable statistical framework to constrain ULB masses and self-interactions. We argue that a Bayesian approach provides a clear statistical interpretation, deals with limitations regarding the reproducibility of existing BH analyses, incorporates the full information from BH data, and allows us to include additional nuisance parameters or to perform hierarchical modelling with BH populations in the future. We demonstrate the feasibility of our approach using mass and spin posterior samples for the X-ray binary BH M33 X-7 and, for the first time in this context, the supermassive BH IRAS 09149-6206. We explain the differences to existing ULB constraints in the literature and illustrate the effects of various assumptions about the superradiance process (equilibrium regime vs cloud collapse, higher occupation levels). As a result, our procedure yields the most rigorous ULB constraints available in the literature, with important implications for the QCD axion and axion-like particles. We encourage all groups analysing BH data to publish likelihood functions or posterior samples as supplementary material to facilitate this type of analysis., Comment: 13+2 pages, 6 figures, software code available at https://github.com/sebhoof/bhsr

The most popular Cross-Document Event Coreference Resolution (CDEC) datasets fail to convey the true difficulty of the task, due to the lack of lexical diversity between coreferring event triggers (words or phrases that refer to an event). Furthermore, there is a dearth of event datasets for figurative language, limiting a crucial avenue of research in event comprehension. We address these two issues by introducing ECB+META, a lexically rich variant of Event Coref Bank Plus (ECB+) for CDEC on symbolic and metaphoric language. We use ChatGPT as a tool for the metaphoric transformation of sentences in the documents of ECB+, then tag the original event triggers in the transformed sentences in a semi-automated manner. In this way, we avoid the re-annotation of expensive coreference links. We present results that show existing methods that work well on ECB+ struggle with ECB+META, thereby paving the way for CDEC research on a much more challenging dataset. Code/data: https://github.com/ahmeshaf/llms_coref, Comment: Short Paper, ACL 2024

Jersey number recognition is an important task in sports video analysis, partly due to its importance for long-term player tracking. It can be viewed as a variant of scene text recognition. However, there is a lack of published attempts to apply scene text recognition models on jersey number data. Here we introduce a novel public jersey number recognition dataset for hockey and study how scene text recognition methods can be adapted to this problem. We address issues of occlusions and assess the degree to which training on one sport (hockey) can be generalized to another (soccer). For the latter, we also consider how jersey number recognition at the single-image level can be aggregated across frames to yield tracklet-level jersey number labels. We demonstrate high performance on image- and tracklet-level tasks, achieving 91.4% accuracy for hockey images and 87.4% for soccer tracklets. Code, models, and data are available at https://github.com/mkoshkina/jersey-number-pipeline.

Electrical readout of magnetic states is a key to realize novel spintronics devices for efficient computing and data storage. Unidirectional magnetoresistance (UMR) in bilayer systems, consisting of a spin source material and a magnetic layer, refers to a change in the longitudinal resistance upon the reversal of magnetization, which typically originates from the interaction of spin-current and magnetization at the interface. Because of UMR s linear dependence on applied charge current and magnetization, it can be used to electrically read the magnetization state. However, in conventional spin source materials, the spin polarization of an electric field induced spin current is restricted to be in the film plane and hence the ensuing UMR can only respond to the in plane component of the magnetization. On the other hand, magnets with perpendicular magnetic anisotropy (PMA) are highly desired for magnetic memory and spin-logic devices, while the electrical read out of PMA magnets through UMR is critically missing. Here, we report the discovery of an unconventional UMR in bilayer heterostructures of a topological semimetal (WTe2) and a PMA ferromagnetic insulator (Cr2Ge2Te6, CGT), which allows to electrically read the up and down magnetic states of the CGT layer by measuring the longitudinal resistance. Our theoretical calculations based on a tight binding model show that the unconventional UMR originates from the interplay of crystal symmetry breaking in WTe2 and magnetic exchange interaction across the WTe2 and CGT interface. Combining with the ability of WTe2 to obtain magnetic field free switching of the PMA magnets, our discoveries open an exciting pathway to achieve two terminal magnetic memory devices that operate solely on the spin orbit torque and UMR, which is critical for developing next-generation non volatile and low power consumption data storage technologies.

This paper is dedicated to the design and evaluation of the first AMR parser tailored for clinical notes. Our objective was to facilitate the precise transformation of the clinical notes into structured AMR expressions, thereby enhancing the interpretability and usability of clinical text data at scale. Leveraging the colon cancer dataset from the Temporal Histories of Your Medical Events (THYME) corpus, we adapted a state-of-the-art AMR parser utilizing continuous training. Our approach incorporates data augmentation techniques to enhance the accuracy of AMR structure predictions. Notably, through this learning strategy, our parser achieved an impressive F1 score of 88% on the THYME corpus's colon cancer dataset. Moreover, our research delved into the efficacy of data required for domain adaptation within the realm of clinical notes, presenting domain adaptation data requirements for AMR parsing. This exploration not only underscores the parser's robust performance but also highlights its potential in facilitating a deeper understanding of clinical narratives through structured semantic representations., Comment: Accepted to the 6th Clinical NLP Workshop at NAACL, 2024

One of the main interests of 2D materials is their ability to be assembled with many degrees of freedom for tuning and manipulating excitonic properties. There is a need to understand how the structure of the interfaces between atomic layers influences exciton properties. Here we use cathodoluminescence (CL) and time-resolved CL experiments to study how excitons interact with the interface between two twisted hexagonal boron nitride (hBN) crystals with various angles. An efficient capture of free excitons by the interface is demonstrated, which leads to a population of long lived and interface-localized (2D) excitons. Temperature dependent experiments indicate that for high twist angles, these excitons localized at the interface further undergo a self-trapping. It consists in a distortion of the lattice around the exciton on which the exciton traps itself. Our results suggest that this exciton-interface interaction causes a broad optical emission of highly twisted hBN-hBN structures around 300 nm (4 eV). Exciton self-trapping is finally discussed as a common feature of sp2 hybridized boron nitride polytypes and nanostructures due to the ionic nature of the B-N bond and their compact excitons., Comment: 15 pages, 13 figures

Molecular dynamics (MD) simulations have transformed our understanding of the nanoscale, driving breakthroughs in materials science, computational chemistry, and several other fields, including biophysics and drug design. Even on exascale supercomputers, however, runtimes are excessive for systems and timescales of scientific interest. Here, we demonstrate strong scaling of MD simulations on the Cerebras Wafer-Scale Engine. By dedicating a processor core for each simulated atom, we demonstrate a 179-fold improvement in timesteps per second versus the Frontier GPU-based Exascale platform, along with a large improvement in timesteps per unit energy. Reducing every year of runtime to two days unlocks currently inaccessible timescales of slow microstructure transformation processes that are critical for understanding material behavior and function. Our dataflow algorithm runs Embedded Atom Method (EAM) simulations at rates over 270,000 timesteps per second for problems with up to 800k atoms. This demonstrated performance is unprecedented for general-purpose processing cores., Comment: 10 pages, 10 figures, 5 tables

The spin-Hall effect underpins some of the most active topics in modern physics, including spin torques and the inverse spin-Hall effect, yet it lacks a proper theoretical description. This makes it difficult to differentiate the SHE from other mechanisms, as well as differentiate band structure and disorder contributions. Here, by exploiting recent analytical breakthroughs in the understanding of the intrinsic spin-Hall effect, we devise a density functional theory method for evaluating the conserved (proper) spin current in a generic system. Spin non-conservation makes the conventional spin current physically meaningless, while the conserved spin current has been challenging to evaluate since it involves the position operator between Bloch bands. The novel method we introduce here can handle band structures with arbitrary degeneracies and incorporates all matrix elements of the position operator, including the notoriously challenging diagonal elements, which are associated with Fermi surface, group velocity, and dipolar effects but often diverge if not treated correctly. We apply this method to the most important classes of spin-Hall materials: topological insulators, 2D quantum spin-Hall insulators, non-collinear antiferromagnets, and strongly spin-orbit coupled metals. We demonstrate that the torque dipole systematically suppresses contributions to the conventional spin current such that, the proper spin current is generally smaller in magnitude and often has a different sign. Remarkably, its energy-dependence is relatively flat and featureless, and its magnitude is comparable in all classes of materials studied. These findings will guide the experiment in characterizing charge-to-spin interconversion in spintronic and orbitronic devices. We also discuss briefly a potential generalisation of the method to calculate extrinsic spin currents generated by disorder scattering.

Artificial neural networks trained on large, expert-labelled datasets are considered state-of-the-art for a range of medical image recognition tasks. However, categorically labelled datasets are time-consuming to generate and constrain classification to a pre-defined, fixed set of classes. For neuroradiological applications in particular, this represents a barrier to clinical adoption. To address these challenges, we present a self-supervised text-vision framework that learns to detect clinically relevant abnormalities in brain MRI scans by directly leveraging the rich information contained in accompanying free-text neuroradiology reports. Our training approach consisted of two-steps. First, a dedicated neuroradiological language model - NeuroBERT - was trained to generate fixed-dimensional vector representations of neuroradiology reports (N = 50,523) via domain-specific self-supervised learning tasks. Next, convolutional neural networks (one per MRI sequence) learnt to map individual brain scans to their corresponding text vector representations by optimising a mean square error loss. Once trained, our text-vision framework can be used to detect abnormalities in unreported brain MRI examinations by scoring scans against suitable query sentences (e.g., 'there is an acute stroke', 'there is hydrocephalus' etc.), enabling a range of classification-based applications including automated triage. Potentially, our framework could also serve as a clinical decision support tool, not only by suggesting findings to radiologists and detecting errors in provisional reports, but also by retrieving and displaying examples of pathologies from historical examinations that could be relevant to the current case based on textual descriptors., Comment: Under Review

This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%., Comment: To appear in CVPRW2024

Event coreference resolution (ECR) is the task of determining whether distinct mentions of events within a multi-document corpus are actually linked to the same underlying occurrence. Images of the events can help facilitate resolution when language is ambiguous. Here, we propose a multimodal cross-document event coreference resolution method that integrates visual and textual cues with a simple linear map between vision and language models. As existing ECR benchmark datasets rarely provide images for all event mentions, we augment the popular ECB+ dataset with event-centric images scraped from the internet and generated using image diffusion models. We establish three methods that incorporate images and text for coreference: 1) a standard fused model with finetuning, 2) a novel linear mapping method without finetuning and 3) an ensembling approach based on splitting mention pairs by semantic and discourse-level difficulty. We evaluate on 2 datasets: the augmented ECB+, and AIDA Phase 1. Our ensemble systems using cross-modal linear mapping establish an upper limit (91.9 CoNLL F1) on ECB+ ECR performance given the preprocessing assumptions used, and establish a novel baseline on AIDA Phase 1. Our results demonstrate the utility of multimodal information in ECR for certain challenging coreference problems, and highlight a need for more multimodal resources in the coreference resolution space., Comment: To appear at LREC-COLING 2024

The physical link between AGN activity and the suppression of star formation in their host galaxies is one of the major open questions of AGN feedback. The Spitzer space mission revealed a subset of nearby radio galaxies with unusually bright line emission from warm ($T\ge 100$ K) H$_2$, while typical star-formation tracers were exceptionally faint or undetected. We present JWST NIRSpec and MIRI IFU observations of 3C 326 N at z=0.09 and identify 19 ro-vibrational H$_2$ emission lines that probe hot ($T\sim 1000$ K) gas as well as the rotational lines of H$_2$ 0--0 S(3), S(5), and S(6) which probe most of the $2\times 10^9$ M$_\odot$ of warm H$_2$ in this galaxy. CO band heads show a stellar component consistent with a "slow-rotator", typical of a massive $3\times10^{11}$ M$_\odot$ galaxy, and provide us with a reliable redshift of $z=0.08979\pm 0.0003$. Extended line emission shows a bipolar bubble expanding through the molecular disk at velocities of up to 380 km s$^{-1}$, delineated by several bright clumps along the Northern outer rim, potentially from gas fragmentation. Throughout the disk, the H$_2$ is very broad, FWHM ~100-1300 km s$^{-1}$, and shows dual-component Gaussian line profiles. [FeII]$\lambda$1.644 and Pa$\alpha$ follow the same morphology, however [NeIII]$\lambda$15.56 is more symmetric about the nucleus. We show that the gas, with the exception of [NeIII]$\lambda$15.56, is predominantly heated by shocks driven by the radio jet and that the accompanying line broadening is sufficient to suppress star formation. We also compare the rotational and ro-vibrational lines, finding that the latter can be a good proxy to the global morphology and kinematic properties of the former in strongly turbulent environments. This enables studies of turbulence in galaxies at intermediate and high redshifts while most rotational lines are redshifted out of the MIRI bandpass for $z$>1.5., Comment: Accepted with comments by A&A, 14 Figures, 5 Tables

Event Coreference Resolution (ECR) as a pairwise mention classification task is expensive both for automated systems and manual annotations. The task's quadratic difficulty is exacerbated when using Large Language Models (LLMs), making prompt engineering for ECR prohibitively costly. In this work, we propose a graphical representation of events, X-AMR, anchored around individual mentions using a \textbf{cross}-document version of \textbf{A}bstract \textbf{M}eaning \textbf{R}epresentation. We then linearize the ECR with a novel multi-hop coreference algorithm over the event graphs. The event graphs simplify ECR, making it a) LLM cost-effective, b) compositional and interpretable, and c) easily annotated. For a fair assessment, we first enrich an existing ECR benchmark dataset with these event graphs using an annotator-friendly tool we introduce. Then, we employ GPT-4, the newest LLM by OpenAI, for these annotations. Finally, using the ECR algorithm, we assess GPT-4 against humans and analyze its limitations. Through this research, we aim to advance the state-of-the-art for efficient ECR and shed light on the potential shortcomings of current LLMs at this task. Code and annotations: \url{https://github.com/ahmeshaf/gpt_coref}, Comment: LREC-COLING 2024 main conference

This paper presents a novel Cross-document Abstract Meaning Representation (X-AMR) annotation tool designed for annotating key corpus-level event semantics. Leveraging machine assistance through the Prodigy Annotation Tool, we enhance the user experience, ensuring ease and efficiency in the annotation process. Through empirical analyses, we demonstrate the effectiveness of our tool in augmenting an existing event corpus, highlighting its advantages when integrated with GPT-4. Code and annotations: https://github.com/ahmeshaf/gpt_coref, Comment: EACL 2024 System Demonstration

A kilonova, the electromagnetic emission produced by compact binary mergers, is formed through a delicate interplay of physical processes, involving r-process nucleosynthesis and interactions between heavy elements and photons through radiative transfer. This complexity makes it difficult to achieve a comprehensive understanding of kilonova spectra. In this study, we aim to enhance our understanding and establish connections between physical parameters and observables through radiative-transfer simulations. Specifically, we investigate how ejecta temperature and element mass influence the resulting kilonova spectrum. For each species, the strength of its line features depends on these parameters, leading to the formation of a distinct region in the parameter space, dubbed the Resonance Island, where the line signature of that species is notably evident in the kilonova spectrum. We explore its origin and applications. Among explored r-process elements (31$\leq$Z$\leq$92), we find that four species -- Sr$_{\rm II}$, Y$_{\rm II}$, Ba$_{\rm II}$, and Ce$_{\rm II}$ -- exhibit large and strong resonance islands, suggesting their significant contributions to kilonova spectra at specific wavelengths. In addition, we discuss potential challenges and future perspectives in observable heavy elements and their masses in the context of the resonance island., Comment: 13 pages, 10 figures, 1 table, ApJ published

Cloud radiative feedback impacts early tropical cyclone (TC) intensification, but limitations in existing diagnostic frameworks make them unsuitable for studying asymmetric or transient radiative heating. We propose a linear Variational Encoder-Decoder (VED) to learn the hidden relationship between radiation and the surface intensification of realistic simulated TCs. Limiting VED model inputs enables using its uncertainty to identify periods when radiation has more importance for intensification. A close examination of the extracted 3D radiative structures suggests that longwave radiative forcing from inner core deep convection and shallow clouds both contribute to intensification, with the deep convection having the most impact overall. We find that deep convection downwind of the shallow clouds is critical to the intensification of Haiyan. Our work demonstrates that machine learning can discover thermodynamic-kinematic relationships without relying on axisymmetric or deterministic assumptions, paving the way towards the objective discovery of processes leading to TC intensification in realistic conditions., Comment: 15 pages, 6 figures (main text)