Search

We present a timing study of the gamma and X-ray observations and analysis of a sample of bright gamma-ray bursts (GRBs; i.e. GRB 180720B, GRB 181222B, GRB 211211A and GRB 220910A), including the very bright and long GRB 211211A (a.k.a. kilonova candidate). They have been detected and observed by the Atmosphere-Space Interactions Monitor (ASIM) installed on the International Space Station (ISS) and the Gamma-ray Burst Monitor (GBM) on-board the Fermi mission. The early (T-T0=s) and high-energy (0.3-20 MeV) ASIM High Energy Detector (HED) and (150 keV-30 GeV) Fermi (BGO) light curves show well-defined peaks with a low quasi-periodic oscillation (QPO) frequency between 2.5-3.5 Hz that could be identified with the spin of the neutron star in the binary mergers (coinciding with the orbital frequency of the binary merger) originating these GRBs. These QPOs consist on the first detection of low-frequency QPOs (<10 Hz) detected in magnetars so far. We also detect a strong QPO at 21.8-22 Hz in GRB 181222B together with its (less significant) harmonics. The low-frequency QPO would correspond to the signal of the orbiting neutron star (NS) previous to the final coalescence giving rise to the gravitational-wave (GW) signal., Comment: Submitted to MNRAS Letters (21-Nov.-2024)

There is growing interest in applying AI to radiology report generation, particularly for chest X-rays (CXRs). This paper investigates whether incorporating pixel-level information through segmentation masks can improve fine-grained image interpretation of multimodal large language models (MLLMs) for radiology report generation. We introduce MAIRA-Seg, a segmentation-aware MLLM framework designed to utilize semantic segmentation masks alongside CXRs for generating radiology reports. We train expert segmentation models to obtain mask pseudolabels for radiology-specific structures in CXRs. Subsequently, building on the architectures of MAIRA, a CXR-specialised model for report generation, we integrate a trainable segmentation tokens extractor that leverages these mask pseudolabels, and employ mask-aware prompting to generate draft radiology reports. Our experiments on the publicly available MIMIC-CXR dataset show that MAIRA-Seg outperforms non-segmentation baselines. We also investigate set-of-marks prompting with MAIRA and find that MAIRA-Seg consistently demonstrates comparable or superior performance. The results confirm that using segmentation masks enhances the nuanced reasoning of MLLMs, potentially contributing to better clinical outcomes., Comment: Accepted as Proceedings Paper at ML4H 2024

We explore a family of paraxial beams constructed by the linear superposition of Laguerre-Gaussian beams, representing an optical analogue to generalized $SU(2)$ Lie group coherent states. A single complex parameter controls a smooth transition between Laguerre-Gaussian and Hermite-Gaussian beams, with intermediate beams that merge characteristics of both families. Our beams exhibit propagation-invariant properties, up to a scaling factor, a highly desirable feature for optical applications, validated via holographic experimental results., Comment: 12 pages, 6 figures

Tensor network methods have proved to be highly effective in addressing a wide variety of physical scenarios, including those lacking an intrinsic one-dimensional geometry. In such contexts, it is possible for the problem to exhibit a weak form of permutational symmetry, in the sense that entanglement behaves similarly across any arbitrary bipartition. In this paper, we show that translationally-invariant (TI) matrix product states (MPS) with this property are trivial, meaning that they are either product states or superpositions of a few of them. The results also apply to non-TI generic MPS, as well as further relevant examples of MPS including the W state and the Dicke states in an approximate sense. Our findings motivate the usage of ans\"atze simpler than tensor networks in systems whose structure is invariant under permutations., Comment: 15 pages

Astrophysical compact objects are usually studied using a perfect fluid model. However, in astrophysical processes out-of-equilibrium, dissipative effects become important to describe the dynamics of the system. In this work, we obtain gauge-invariant non-spherical perturbations of a self-gravitating non-perfect fluid in spherical spacetime. We use the Gerlach-Sengupta formalism to work with gauge-invariant metric perturbations, and the Gundlach-Mart\'in-Garc\'ia approach to transform the equations of tensor perturbations into scalar equations. We calculate the dynamics of the dissipative contributions, e.g., viscosity and heat flux, using the M\"uller-Israel-Stewart equations in the gauge-invariant formalism. We obtain a set of field equations for the evolution of matter and metric perturbations in the polar and axial sectors. Specifically, in the former we find two wave equations sourced by the anisotropic contributions, and the evolution of all matter perturbations for radiative modes ($l\geq 2$). In the axial sector, we find one wave equation coupled to the evolution of matter perturbations. Finally, we comment on the contribution of dissipative effects in the lower-order multipoles ($l=0,1$) for both sectors., Comment: 25 pages

The study of undecidability in problems arising from physics has experienced a renewed interest, mainly in connection with quantum information problems. The goal of this review is to survey this recent development. After a historical introduction, we first explain the necessary results about undecidability in mathematics and computer science. Then we briefly review the first results about undecidability in physics which emerged mostly in the 80s and early 90s. Finally we focus on the most recent contributions, which we divide in two main categories: many body systems and quantum information problems., Comment: 77 pages, v1

Thanks to the rapidly increasing time-domain facilities, we are entering a golden era of research on gamma-ray bursts (GRBs). In this Letter, we report our observations of GRB 240529A with the Burst Optical Observer and Transient Exploring System, the 1.5-meter telescope at Observatorio Sierra Nevada, the 2.5-meter Wide Field Survey Telescope of China, the Large Binocular Telescope, and the Telescopio Nazionale Galileo. The prompt emission of GRB 240529A shows two comparable energetic episodes separated by a quiescence time of roughly 400 s. Combining all available data on the GRB Coordinates Network, we reveal the simultaneous apparent X-ray plateau and optical re-brightening around $10^3-10^4$ s after the burst. Rather than the energy injection from the magnetar as widely invoked for similar GRBs, the multi-wavelength emissions could be better explained as two shocks launched from the central engine separately. The optical peak time and our numerical modeling suggest that the initial bulk Lorentz factor of the later shock is roughly 50, which indicates that the later jet should be accretion-driven and have a higher mass loading than a typical one. The quiescence time between the two prompt emission episodes may be caused by the transition between different accretion states of a central magnetar or black hole, or the fall-back accretion process. A sample of similar bursts with multiple emission episodes in the prompt phase and sufficient follow-up could help to probe the underlying physics of GRB central engines., Comment: Resubmitted to ApJL after addressing the referee's comments; comments are welcome

CAR-T cell therapies have demonstrated significant success in treating B-cell leukemia in children and young adults. However, their effectiveness in treating B-cell lymphomas has been limited. Unlike leukemia, lymphoma often manifests as solid masses of cancer cells in lymph nodes, glands, or organs, making these tumors harder to access thus hindering treatment response. In this paper we present a mathematical model that elucidates the dynamics of diffuse large B-cell lymphoma and CAR-T cells in a lymph node. The mathematical model aids in understanding the complex interplay between the cell populations involved and proposes ways to identify potential underlying dynamical causes of treatment failure. We also study the phenomenon of immunosuppression induced by tumor cells and theoretically demonstrate its impact on cell dynamics. Through the examination of various response scenarios, we underscore the significance of product characteristics in treatment outcomes., Comment: 33 pages, 8 figures, ECMTB 2024

We explore Majorana stellar representation on the Riemann sphere for optical scalar beams and vector fields, leveraging the spin and orbital angular momentum of light. For scalar beams, the star constellations for the Laguerre-Gaussian beam basis reside at the poles of the sphere. For vector fields, these constellations represent well-defined circular polarization. The underlying $su(2)$ symmetry of angular momenta enables populating the sphere through generalized unitary rotations of the basis elements, resulting in optical analogues of Bloch and generalized $su(2)$ coherent states for both scalar beams and vector fields. Constellations for scalar Hermite-Gaussian beams and vector Hermite-Gaussian partial Poincar\'e fields, restricted to linear polarization, occupy the equator of the sphere as a result of these rotations. Additionally, we investigate scalar and vector analogues of cat codes and kings of quantumness, providing insights into the visualization and characterization of complex optical beams and fields with potential applications in optical communications., Comment: 19 pages, 8 figures

Using an effective Lagrangian model inspired by Takami et al. 2015 we qualitatively study the early post-merger of a nearly symmetric binary Neutron Star (BNS) merger event with a non-vanishing ambient fraction of dark matter. For this we first mimic the dynamics of two oscillating Neutron Star (NS) masses in the gravitational potential well as they merge. We parametrize the dynamics and ejecta properties in the coalescence event allowing the formation of a surrounding debris disk that may be containing a non-vanishing dark matter fraction. In order to analyze the possible novel dark contribution, we start from a dark-matter free modellization as a benchmark. Using Monte Carlo Markov Chain (MCMC) techniques we approximately recover the gravitational waveforms, restricted to early post-merger time interval from existing simulations in the CoRe database. Later, we explore the impact of an additional dark viscous fluid under a prescribed velocity dependent force in the Lagrangian and obtain the resulting waveforms and some spectral features originating in the first few ms in the BNS post-merger. Finally we discuss our qualitative findings and its range of validity in light of the prospects of detectability in present or future experimental settings., Comment: 13 pages, 5 figures

We introduce regular language states, a family of quantum many-body states. They are built from a special class of formal languages, called regular, which has been thoroughly studied in the field of computer science. They can be understood as the superposition of all the words in a regular language and encompass physically relevant states such as the GHZ-, W- or Dicke-states. By leveraging the theory of regular languages, we develop a theoretical framework to describe them. First, we express them in terms of matrix product states, providing efficient criteria to recognize them. We then develop a canonical form which allows us to formulate a fundamental theorem for the equivalence of regular language states, including under local unitary operations. We also exploit the theory of tensor networks to find an efficient criterion to determine when regular languages are shift-invariant., Comment: 12 pages, 1 figure

Matrix-product unitaries (MPU) are 1D tensor networks describing time evolution and unitary symmetries of quantum systems, while their action on states by construction preserves the entanglement area law. MPU which are formed by a single repeated tensor are known to coincide with 1D quantum cellular automata (QCA), i.e., unitaries with an exact light cone. However, this correspondence breaks down for MPU with open boundary conditions, even if the resulting operator is translation-invariant. Such unitaries can turn short- to long-range correlations and thus alter the underlying phase of matter. Here we make the first steps towards a theory of MPU with uniform bulk but arbitrary boundary. In particular, we study the structure of a subclass with a direct-sum form which maximally violates the QCA property. We also consider the general case of MPU formed by site-dependent (nonuniform) tensors and show a correspondence between MPU and locally maximally entanglable states.

Radiology reporting is a complex task requiring detailed medical image understanding and precise language generation, for which generative multimodal models offer a promising solution. However, to impact clinical practice, models must achieve a high level of both verifiable performance and utility. We augment the utility of automated report generation by incorporating localisation of individual findings on the image - a task we call grounded report generation - and enhance performance by incorporating realistic reporting context as inputs. We design a novel evaluation framework (RadFact) leveraging the logical inference capabilities of large language models (LLMs) to quantify report correctness and completeness at the level of individual sentences, while supporting the new task of grounded reporting. We develop MAIRA-2, a large radiology-specific multimodal model designed to generate chest X-ray reports with and without grounding. MAIRA-2 achieves state of the art on existing report generation benchmarks and establishes the novel task of grounded report generation., Comment: 72 pages, 21 figures. v2 updates the model and adds results on the PadChest-GR dataset

We perform microscopic simulations of the thermal relaxation of warm neutral plasmas of astrophysical importance. Using Molecular Dynamics we study the thermal relaxation of a hot neutral fluid of finite-size neutron-rich ions kept in a fixed-wall cool box. We show how the interplay among particle size, Yukawa interaction range and density are key to understand the features of the time-dependent thermal relaxation curve $T(t)$. We show that, under certain conditions, these systems exhibit faster cooling from increasingly larger initial temperature values. They also display non-Newtonian thermal behavior, including oscillations, that can be {\it effectively} interpreted as the consequence of the existence of a non-trivial system memory function. Finally, we consider the impact of multicomponent admixtures in the simulated system. We discuss these results and their possible extensions to astrophysical scenarios, where screened plasmas with Yukawa-like electrostatic potentials are usually involved., Comment: 9 pages, 4 figures

We use the spatial degree of freedom of light modes to construct optical analogues of generalized quantum coherent states for Hermite- and Laguerre-Gauss modes. Our optical analogues preserve the statistical properties of their quantum counterparts, encoded in their amplitude and phase distributions. We explore three basic symmetries that provide generalized displaced, rotated, and squeezed coherent states. Given the substantial interest in squeezed states for probing matter, we believe that the optical analogues introduced here have significant implications for optical sensing. Specifically, the single-particle nature of our spatial modes makes them robust candidates for sensing photosensitive materials. Overall, our approach opens the door to optical metrology and sensing protocols that mimic those already existing in the quantum realm, and facilitates further exploration of the quantum state zoo through classical optical analogues., Comment: 42 pages, 14 figures

In this manuscript, we put forward two new types of structured light beams, the vortex Pearcey-Gauss (VPeG) beam, with a homogeneous polarisation distribution, and the vector vortex Pearcey-Gauss (VVPeG) beam, with a non-homogeneous polarisation distribution. The later generated as a non-separable superposition of the spatial and polarisation degrees of freedom. We also achieve their experimental realization through the combination of a spatial light modulator, which creates a scalar Pearcey-Gauss beam, and a q-plate which transforms it into a vortex or a vortex vector beam, depending on its input polarisation state. Their intensity and polarisation distribution was performed through Stokes polarimetry, along the propagation direction, which was compared with numerical simulations. As demonstrated, the VVPeG beam evolves from a pure vector beam into a vector mode of quasi-homogeneous polarisation distribution. The proposed vector beams add to the already extensive family of non-separable states of light. We anticipate that both types of beams will find applications in fields as diverse as optical metrology, optical communications, and optical tweezers, amongst others., Comment: 10 pages, 4 figures

Vector modes are well-defined field distributions with spatially varying polarisation states, rendering them irreducible to the product of a single spatial mode and a single polarisation state. Traditionally, the spatial degree of freedom of vector modes is constructed using two orthogonal modes from the same family. In this letter, we introduce a novel class of vector modes whose spatial degree of freedom is encoded by combining modes from both the Hermite- and Laguerre-Gaussian families. This particular superposition is not arbitrary, and we provide a detailed explanation of the methodology employed to achieve it. Notably, this new class of vector modes, which we term Hybrid Hermite-Laguerre-Gaussian (HHLG) vector modes, gives rise to subsets of modes exhibiting polarisation dependence on propagation due to the difference in mode orders between the constituent Hermite- and Laguerre-Gaussian modes. To the best of our knowledge, this is the first demonstration of vector modes composed of two scalar modes originating from different families. We anticipate diverse applications for HHLG vector modes in fields such as free-space communications, information encryption, optical metrology, and beyond., Comment: 4 pages, 5 figures

Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication. If not placed correctly, they can cause serious harm, even death to patients. Recent AI developments demonstrate the feasibility of robustly detecting NGT placement from Chest X-ray images to reduce risks of sub-optimally or critically placed NGTs being missed or delayed in their detection, but gaps remain in clinical practice integration. In this study, we present a human-centered approach to the problem and describe insights derived following contextual inquiry and in-depth interviews with 15 clinical stakeholders. The interviews helped understand challenges in existing workflows, and how best to align technical capabilities with user needs and expectations. We discovered the trade-offs and complexities that need consideration when choosing suitable workflow stages, target users, and design configurations for different AI proposals. We explored how to balance AI benefits and risks for healthcare staff and patients within broader organizational and medical-legal constraints. We also identified data issues related to edge cases and data biases that affect model training and evaluation; how data documentation practices influence data preparation and labelling; and how to measure relevant AI outcomes reliably in future evaluations. We discuss how our work informs design and development of AI applications that are clinically useful, ethical, and acceptable in real-world healthcare services.

In this paper, we revisit the data for the galaxy cluster ZwCl 0024.0+1652 provided by the GLACE survey and study the mass--metallicity function and its relationship with the environment. Here we describe an alternative way to reduce the data from OSIRIS tunable filters. This method gives us better uncertainties in the fluxes of the emission lines and the derived quantities. We present an updated catalogue of cluster galaxies with emission in H$\alpha$ and [N\,{\sc{ii}}] $\lambda\lambda$6548,6583. We also discuss the biases of these new fluxes and describe the way in which we calculated the mass--metallicity relationship and its uncertainties. We generated a new catalogue of 84 emission-line galaxies with reliable fluxes in [N\,{\sc{ii}}] and H$\alpha$ lines from a list of 174 galaxies. We find a relationship between the clustercentric radius and the density of galaxies. We derived the mass--metallicity relationship for ZwCl 0024.0+1652 and compared it with clusters and field galaxies from the literature. We find a difference in the mass--metallicity relationship when compared to more massive clusters, with the latter showing on average higher values of abundance. This could be an effect of the quenching of the star formation, which seems to be more prevalent in low-mass galaxies in more massive clusters. We find little to no difference between ZwCl 0024.0+1652 galaxies and field galaxies located at the same redshift., Comment: 13 pages, 17 figures, accepted for publication in Astronomy and Astrophysics

Chimeric Antigen Receptor T (CAR-T) cell therapy has been proven to be successful against different leukaemias and lymphomas. This paper makes an analytical and numerical study of a mathematical model describing the competition of CAR-T, leukaemias tumor and B cells. Considering its significance in sustaining anti-CD19 CAR T-cell stimulation, we integrate a B-cell source term into the model. Through stability and bifurcation analyses, we reveal the potential for tumor eradication contingent on the continuous influx of B-cells, uncovering a transcritical bifurcation at a critical B-cell input. Additionally, we identify an almost heteroclinic cycle between equilibrium points, providing a theoretical basis for understanding disease relapse. Analyzing the oscillatory behavior of the system, we approximate the time-dependent dynamics of CAR T-cells and leukemic cells, shedding light on the impact of initial tumor burden on therapeutic outcomes. In conclusion, our study provides insights into CAR T-cell therapy dynamics for acute lymphoblastic leukemias, offering a theoretical foundation for clinical observations and suggesting avenues for future immunotherapy modeling research.

Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare applications. Focusing on the domain of radiology, vision-language models (VLMs) achieve good performance results for tasks such as generating radiology findings based on a patient's medical image, or answering visual questions (e.g., 'Where are the nodules in this chest X-ray?'). However, the clinical utility of potential applications of these capabilities is currently underexplored. We engaged in an iterative, multidisciplinary design process to envision clinically relevant VLM interactions, and co-designed four VLM use concepts: Draft Report Generation, Augmented Report Review, Visual Search and Querying, and Patient Imaging History Highlights. We studied these concepts with 13 radiologists and clinicians who assessed the VLM concepts as valuable, yet articulated many design considerations. Reflecting on our findings, we discuss implications for integrating VLM capabilities in radiology, and for healthcare AI more generally., Comment: to appear at CHI 2024

Fibrous dysplasia (FD) is a mosaic non-inheritable genetic disorder of the skeleton in which normal bone is replaced by structurally unsound fibro-osseous tissue. There is no curative treatment for FD, partly because its pathophysiology is not yet fully known. We present a simple mathematical model of the disease incorporating its basic known biology, to gain insight on the dynamics of the involved bone-cell populations, and shed light on its pathophysiology. Our mathematical models account for the dynamic evolution over time of several interacting populations of bone cells averaged over a volume of bone of sufficient size in order to obtain consistent results. We develop an analytical study of the model and study its basic properties. The existence and stability of steady states are studied, an analysis of sensitivity on the model parameters is done, and different numerical simulations provide findings in agreement with the analytical results. We discuss the model dynamics match with known facts on the disease, and how some open questions could be addressed using the model.

Evolvability is defined as the ability of a population to generate heritable variation to facilitate its adaptation to new environments or selection pressures. In this article, we consider evolvability as a phenotypic trait subject to evolution and discuss its implications in the adaptation of cell populations. We explore the evolutionary dynamics of an actively proliferating population of cells subject to changes in their proliferative potential and their evolvability using a stochastic individual-based model and its deterministic continuum counterpart through numerical simulations of these models. We find robust adaptive trajectories that rely on cells with high evolvability rapidly exploring the phenotypic landscape and reaching the proliferative potential with the highest fitness. The strength of selection on the proliferative potential, and the cost associated with evolvability, can alter these trajectories such that, if both are sufficiently constraining, highly evolvable populations can become extinct in our individual-based model simulations. We explore the impact of this interaction at various scales, discussing its effects in undisturbed environments and also in disrupted contexts, such as cancer., Comment: 26 pages, 6 figures, 1 table

The accessibility of the Hawking-Page transition in AdS$_5$ through a 1d Heisenberg spin chain is demonstrated. We use the random matrix formulation of the Loschmidt echo for a set of spin chains, and randomize the ferromagnetic spin interaction. It is shown that the thermal Loschmidt echo, when averaged, detects the predicted increase in entropy across the Hawking-Page transition. This suggests that a 1d spin chain exhibits characteristics of black hole physics in 4+1 dimensions. We show that this approach is equally applicable to free fermion systems with a general dispersion relation., Comment: 5+13 pages, 2+3 figures. v2: minor improvements, references added, published version

Language-supervised pre-training has proven to be a valuable method for extracting semantically meaningful features from images, serving as a foundational element in multimodal systems within the computer vision and medical imaging domains. However, resulting features are limited by the information contained within the text. This is particularly problematic in medical imaging, where radiologists' written findings focus on specific observations; a challenge compounded by the scarcity of paired imaging-text data due to concerns over leakage of personal health information. In this work, we fundamentally challenge the prevailing reliance on language supervision for learning general purpose biomedical imaging encoders. We introduce RAD-DINO, a biomedical image encoder pre-trained solely on unimodal biomedical imaging data that obtains similar or greater performance than state-of-the-art biomedical language supervised models on a diverse range of benchmarks. Specifically, the quality of learned representations is evaluated on standard imaging tasks (classification and semantic segmentation), and a vision-language alignment task (text report generation from images). To further demonstrate the drawback of language supervision, we show that features from RAD-DINO correlate with other medical records (e.g., sex or age) better than language-supervised models, which are generally not mentioned in radiology reports. Finally, we conduct a series of ablations determining the factors in RAD-DINO's performance; notably, we observe that RAD-DINO's downstream performance scales well with the quantity and diversity of training data, demonstrating that image-only supervision is a scalable approach for training a foundational biomedical image encoder.

We investigate the link between quantum position-verification (QPV) and holography established in [MPS19] using holographic quantum error correcting codes as toy models. By inserting the "temporal" scaling of the AdS metric by hand via the bulk Hamiltonian interaction strength, we recover a toy model with consistent causality structure. This leads to an interesting implication between two topics in quantum information: if position-based verification is secure against attacks with small entanglement then there are new fundamental lower bounds for resources required for one Hamiltonian to simulate another., Comment: (v2: updated presentation)

We present microscopic Molecular Dynamics simulations including the efficient Ewald sum procedure to study warm and dense stellar plasmas consisting of finite-size ion charges immerse in a relativistic neutralizing electron gas. For densities typical of Supernova matter and crust in a proto-neutron star, we select a representative single ion composition and obtain the virialized equation of state (vEoS). We scrutinize the finite-size and screening corrections to the Coulomb potential appearing in the virial coefficients $B_2, B_3$ and $B_4$ as a function of temperature. In addition, we study the thermal heat capacity at constant volume, $C_V$, and the generalized Mayer's relation i.e. the difference $C_P-C_V$ with $C_P$ being the heat capacity at constant pressure, obtaining clear features signaling the onset of the liquid-gas phase transition. Our findings show that microscopic simulations reproduce the discontinuity in $C_V$, whose value lies between that of idealized gas and crystallized configurations. We study the pressure isotherms marking the boundary of the metastable region before the gaseous transition takes place. The resulting vEoS displays a behaviour where effective virial coefficients include extra density dependence showing a generalized density-temperature form. As an application we parametrize pressure as a function of density and temperature under the form of an artificial neural network showing the potential of machine learning for future regression analysis in more refined multicomponent approaches. This is of interest to size the importance of these corrections in the liquid-gas phase transition in warm and dense plasma phases contributing to the cooling behaviour of early Supernova phases and proto-neutron stars., Comment: 11 pages, 8 figures, published in MNRAS

Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. This work proposes using generative image editing to simulate dataset shifts and diagnose failure modes of biomedical vision models; this can be used in advance of deployment to assess readiness, potentially reducing cost and patient harm. Existing editing methods can produce undesirable changes, with spurious correlations learned due to the co-occurrence of disease and treatment interventions, limiting practical applicability. To address this, we train a text-to-image diffusion model on multiple chest X-ray datasets and introduce a new editing method RadEdit that uses multiple masks, if present, to constrain changes and ensure consistency in the edited images. We consider three types of dataset shifts: acquisition shift, manifestation shift, and population shift, and demonstrate that our approach can diagnose failures and quantify model robustness without additional data collection, complementing more qualitative tools for explainable AI.

We investigate the structure of admixed neutron stars with a regular hadronic component and a fraction of fermionic self-interacting dark matter. Using two limiting equations of state for the dense baryonic interior, constructed from piecewise generalised polytropes, and an asymmetric self-interacting fermionic dark component, we analyse different scenarios of admixed neutron stars depending on the mass of dark fermions $m_\chi$, interaction mediators $m_\phi$, and self-interacting strengths $g$. We find that the contribution of dark matter to the masses and radii of neutron stars leads to tension with mass estimates of the pulsar J0453+1559, the least massive neutron star, and with the constraints coming from the GW170817 event. We discuss the possibilities of constraining dark matter model parameters $g$ and $y \equiv m_\chi/m_\phi$, using current existing knowledge on neutron star estimations of mass, radius, and tidal deformability, along with the accepted cosmological dark matter freeze-out values and self-interaction cross-section to mass ratio, $\sigma_\mathrm{SI}/m_\chi$, fitted to explain Bullet, Abell, and dwarf galaxy cluster dynamics. By assuming the most restrictive upper limit, $\sigma_\mathrm{SI}/m_\chi < 0.1$ cm$^2$/g, along with dark matter freeze-out range values, the allowed $g$-$y$ region is $0.01 \lesssim g \lesssim 0.1$, with $0.5 \lesssim y \lesssim 200$. For the first time, the combination of updated complementary restrictions is used to set constraints on self-interacting dark matter., Comment: 12 pages, 9 figures. Accepted for publication in MNRAS. Comments are welcome

We present a radiology-specific multimodal model for the task for generating radiological reports from chest X-rays (CXRs). Our work builds on the idea that large language model(s) can be equipped with multimodal capabilities through alignment with pre-trained vision encoders. On natural images, this has been shown to allow multimodal models to gain image understanding and description capabilities. Our proposed model (MAIRA-1) leverages a CXR-specific image encoder in conjunction with a fine-tuned large language model based on Vicuna-7B, and text-based data augmentation, to produce reports with state-of-the-art quality. In particular, MAIRA-1 significantly improves on the radiologist-aligned RadCliQ metric and across all lexical metrics considered. Manual review of model outputs demonstrates promising fluency and accuracy of generated reports while uncovering failure modes not captured by existing evaluation practices. More information and resources can be found on the project website: https://aka.ms/maira., Comment: 18 pages, 9 tables, 5 figures. v2 adds test IDs and image encoder citation. v3 fixes error in NPV/specificity

In this manuscript, we propose the generation of complex vector beams with high quality and stability based on a novel approach that relies on the combination of two techniques that seem incompatible at first glance. The first is Complex Amplitude Modulation (CAM), which produces scalar structured light fields in phase and amplitude with high accuracy. The second is on-axis modulation for the generation of vector beams, a method that requires phase-only holograms, therefore yielding beams of reduced quality. More precisely, the idea behind our technique is to send the shaped light produced by CAM co-axially to the zeroth order, rather than to the first order, as commonly done. We describe our technique, explaining the generation of the hologram and experimental setup to isolate the desired vector mode, and then present experimental results that corroborate our approach. We first address the quality of the generated beams using Stokes polarimetry to reconstruct their transverse polarisation distribution, and then compare their stability against the same mode produced using a popular interferometric method. Our vector beams are of good quality and remarkably stable, two qualities that we expect will appeal to the community working with vector modes., Comment: 9 pages, 4 figures

The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domains and applications. In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models. Exploring various prompting strategies, we evaluated GPT-4 on a diverse range of common radiology tasks and we found GPT-4 either outperforms or is on par with current SOTA radiology models. With zero-shot prompting, GPT-4 already obtains substantial gains ($\approx$ 10% absolute improvement) over radiology models in temporal sentence similarity classification (accuracy) and natural language inference ($F_1$). For tasks that require learning dataset-specific style or schema (e.g. findings summarisation), GPT-4 improves with example-based prompting and matches supervised SOTA. Our extensive error analysis with a board-certified radiologist shows GPT-4 has a sufficient level of radiology knowledge with only occasional errors in complex context that require nuanced domain knowledge. For findings summarisation, GPT-4 outputs are found to be overall comparable with existing manually-written impressions., Comment: EMNLP 2023 main

The impact of the DART spacecraft into Dimorphos, moon of the asteroid Didymos, changed Dimorphos' orbit substantially, largely from the ejection of material. We present results from twelve Earth-based facilities involved in a world-wide campaign to monitor the brightness and morphology of the ejecta in the first 35 days after impact. After an initial brightening of ~1.4 magnitudes, we find consistent dimming rates of 0.11-0.12 magnitudes/day in the first week, and 0.08-0.09 magnitudes/day over the entire study period. The system returned to its pre-impact brightness 24.3-25.3 days after impact through the primary ejecta tail remained. The dimming paused briefly eight days after impact, near in time to the appearance of the second tail. This was likely due to a secondary release of material after re-impact of a boulder released in the initial impact, through movement of the primary ejecta through the aperture likely played a role., Comment: 16 pages, 5 Figures, accepted in the Astrophysical Journal Letters (ApJL) on October 16, 2023

Large quantum photonic systems hold promise for surpassing classical computational limits, yet their state preparation remains a challenge. We propose an alternative approach to study multiparticle dynamics by mapping the excitation mode of these systems to physical properties of the Laguerre-Gauss modes. We construct coherent states establishing a direct link between excitation number dynamics and the evolution of the Laguerre-Gauss mode. This highlights the photon transverse spatial degree of freedom as a versatile platform for testing fundamental aspects of quantum multiparticle systems., Comment: 14 pages, 7 figures

Dark GRBs constitute a significant fraction of the GRB population. In this paper, we present the multiwavelength analysis of an intense two-episodic GRB 150309A observed early on to ~114 days post-burst. Despite the strong gamma-ray emission, no optical afterglow was detected for this burst. However, we discovered near-infrared afterglow ($K_{\rm S}$-band), ~5.2 hours post burst, with the CIRCE instrument mounted at the 10.4m GTC. We used Fermi observations of GRB 150309A to understand the prompt emission mechanisms and jet composition. We performed the early optical observations using the BOOTES robotic telescope and late-time afterglow observations using the GTC. A potential faint host galaxy is also detected at optical wavelength using the GTC. We modelled the potential host galaxy of GRB 150309A in order to explore the environment of the burst. The time-resolved spectral analysis of Fermi data indicates a hybrid jet composition consisting of a matter-dominated fireball and magnetic-dominated Poynting flux. GTC observations of the afterglow revealed that the counterpart of GRB 150309A was very red, with H-$K_{\rm S}$ > 2.1 mag (95 $\%$ confidence). The red counterpart was not discovered in any bluer filters of Swift UVOT, indicative of high redshift origin. This possibility was discarded based on multiple arguments, such as spectral analysis of X-ray afterglow constrain z < 4.15 and a moderate redshift value obtained using spectral energy distribution modelling of the potential galaxy. The broadband afterglow SED implies a very dusty host galaxy with deeply embedded GRB (suggesting $A_{\rm V}$ $\gtrsim$ 35 mag). The environment of GRB 150309A demands a high extinction towards the line of sight, demanding dust obscuration is the most probable origin of optical darkness and the very red afterglow of GRB 150309A. This result makes GRB 150309A the highest extinguished GRB known to date., Comment: 14 pages, 10 figures, 3 tables, accepted for publication in Astronomy & Astrophysics (A&A)