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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 [1] we describe the early post-merger of a binary Neutron Star (BNS) merger event with a non-vanishing ambient fraction of dark matter. For this we mimic the dynamics of two oscillating Neutron Star (NS) masses in the gravitational potential well as they merge. We parametrize the ejected baryonic matter fraction in the coalescence event forming a surrounding debris disk containing a non-vanishing dark matter fraction. In order to analyze the dark contribution, we start from a dark-matter free modelization using Monte Carlo Markov Chain (MCMC) techniques to replicate the gravitational waveforms obtained from simulations in the CoRe database for three selected equations of state for NS matter (DD2, BHBlp, SFHo). Later, we explore the impact of a dark viscous fluid under a prescribed velocity dependent force and obtain the waveforms and spectral properties originating in the first few ms in the BNS post-merger. Finally we discuss our findings in light of the prospects of detectability of a non-vanishing fraction of dark matter arising in the waveforms and power spectral distribution in present or future experimental settings., Comment: 14 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. 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. 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 that requires detailed image understanding, integration of multiple inputs, including comparison with prior imaging, and precise language generation. This makes it ideal for the development and use of generative multimodal models. Here, we extend report generation to include the localisation of individual findings on the image - a task we call grounded report generation. Prior work indicates that grounding is important for clarifying image understanding and interpreting AI-generated text. Therefore, grounded reporting stands to improve the utility and transparency of automated report drafting. To enable evaluation of grounded reporting, we propose a novel evaluation framework - RadFact - leveraging the reasoning capabilities of large language models (LLMs). RadFact assesses the factuality of individual generated sentences, as well as correctness of generated spatial localisations when present. We introduce MAIRA-2, a large multimodal model combining a radiology-specific image encoder with a LLM, and trained for the new task of grounded report generation on chest X-rays. MAIRA-2 uses more comprehensive inputs than explored previously: the current frontal image, the current lateral image, the prior frontal image and prior report, as well as the Indication, Technique and Comparison sections of the current report. We demonstrate that these additions significantly improve report quality and reduce hallucinations, establishing a new state of the art on findings generation (without grounding) on MIMIC-CXR while demonstrating the feasibility of grounded reporting as a novel and richer task., Comment: 44 pages, 20 figures

We perform microscopic many-body simulations of the thermal relaxation of a realistic warm Yukawa fluid by solving the dynamics of an ensemble of N particles in a cubic box acting as a heat reservoir kept at fixed temperature. For specific conditions of density, ion size and potential interaction range we find novel anomalous thermal relaxation for these systems, exhibiting faster cooling from increasingly larger temperature values. Our findings show that the interplay among finite particle size, Yukawa interaction range and density are key to understanding the features of the cooling curve $T(t)$. The non-Newtonian thermal behavior is analyzed in terms of a time-dependent relaxation time $\tau(t)$ and a generalized Langevin equation. We compare our results to existing Mpemba-like phenomena and interpret temperature oscillation patterns arising from a non-trivial memory function. Further, we consider the impact of composition admixtures in the simulated system. We discuss these results and the more general implications for non-Markovian systems where Yukawa-like potentials are involved, such as astrophysical plasmas., Comment: 6 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)

Kilonovae are a rare class of astrophysical transients powered by the radioactive decay of nuclei heavier than iron, synthesized in the merger of two compact objects. Over the first few days, the kilonova evolution is dominated by a large number of radioactive isotopes contributing to the heating rate. On timescales of weeks to months, its behavior is predicted to differ depending on the ejecta composition and merger remnant. However, late-time observations of known kilonovae are either missing or limited. Here we report observations of a luminous red transient with a quasi-thermal spectrum, following an unusual gamma-ray burst of long duration. We classify this thermal emission as a kilonova and track its evolution up to two months after the burst. At these late times, the recession of the photospheric radius and the rapidly-decaying bolometric luminosity ($L_{\rm bol}\propto t^{-2.7\pm 0.4}$) support the recombination of lanthanide-rich ejecta as they cool., Comment: 47 pages, 14 figures, 9 tables; submitted; a minor typo fixed

The NASA/DART (Double Asteroid Redirection Test) spacecraft successfully crashed on Dimorphos, the secondary component of the binary (65803) Didymos system. Following the impact, a large dust cloud was released, and a long-lasting dust tail was developed. We have extensively monitored the dust tail from the ground and from the Hubble Space Telescope (HST). We provide a characterization of the ejecta dust properties, i.e., particle size distribution and ejection speeds, ejection geometric parameters, and mass, by combining both observational data sets, and by using Monte Carlo models of the observed dust tail. The differential size distribution function that best fits the imaging data was a broken power-law, having a power index of --2.5 for particles of r$\le$ 3 mm, and of --3.7 for larger particles. The particles range in sizes from 1 $\mu$m up to 5 cm. The ejecta is characterized by two components, depending on velocity and ejection direction. The northern component of the double tail, observed since October 8th 2022, might be associated to a secondary ejection event from impacting debris on Didymos, although it is also possible that this feature results from the binary system dynamics alone. The lower limit to the total dust mass ejected is estimated at $\sim$6$\times$10$^6$ kg, half of this mass being ejected to interplanetary space., Comment: Accepted by Planetary Science Journal, July 7th, 2023

Recently, a resonant state of four neutrons (tetraneutron) with an energy of $E_{4n}=2.37\pm 0.38 \rm{(stat)} \pm 0.44 \rm{(sys)}$ MeV and a width of $\Gamma=1.75\pm 0.22 \rm{(stat)} \pm 0.30 \rm{(sys)}$ MeV was reported. In this work, we analyse the effect of including such an exotic state on the yields of other light clusters, that not only form in astrophysical sites, such as core-collapse supernovae and neutron star mergers, but also in heavy-ion collisions. To this aim, we use a relativistic mean-field formalism, where we consider in-medium effects in a two-fold way, via the couplings of the clusters to the mesons, and via a binding energy shift, to compute the low-density equation of state for nuclear matter at finite temperature and fixed proton fraction. We consider five light clusters, deuterons, tritons, heliums, $\alpha$-particles, and $^6$He, immersed in a gas of protons and neutrons, and we calculate their abundances and chemical equilibrium constants with and without the tetraneutron. We also analyse how the associated energy of the tetraneutron would influence such results. We find that the low-temperature, neutron-rich systems, are the ones most affected by the presence of the tetraneutron, making neutron stars excellent environments for their formation. Moreover, its presence in strongly asymmetric matter may increase considerably the proton and the $\alpha$-particle fractions. This may have an influence on the dissolution of the accretion disk of the merger of two neutron stars., Comment: 10 pages, 6 figures

Vector beams display varying polarisation over planes transversal to their direction of propagation. The variation of polarisation implies that the electric field cannot be expressed as a product of a spatial mode and its polarisation. This non-separability has been analysed for particular vector beams in terms of non--quantum entanglement between the spatial and the polarisation-degrees of freedom, and equivalently, with respect to the degree of polarisation of light. Here we demonstrate theoretically and experimentally that Mathieu-Gauss vector modes violate a Bell-like inequality known as the Clauser-Horn-Shimony-Holt-Bell (CHSH-Bell) inequality. This demonstration provides new insights into that fact that a more general class of vector modes with elliptical symmetry also violate Bell inequalities., Comment: 8 pages, 6 figures

In this work, we propose and demonstrate experimentally a new family of vector beams, the Helico-Conical Vector Beams (HCVB), whose spatial degree of freedom is encoded in the Helico-Conical Optical Beams. We use Stokes polarimetry to study their properties and find that upon propagation their transverse polarisation distribution evolves from non-homogeneous to quasi-homogeneous, such that even though their global degree of nonseparability remains constant, locally it decreases to a minimum value as z tends to infinit. We corroborated this quantitatively using the Hellinger distance, a novel metric for vectorness that applies to spatially-disjoint vector modes. To the best of our knowledge, HCVBs are the second family of vector beams in which this behaviour has been observed, paving the way for applications in optical tweezing or information encryption., Comment: 4 pages, 4 figures

Tensor networks are factorizations of high-dimensional tensors into networks of smaller tensors. They have applications in physics and mathematics, and recently have been proposed as promising machine learning architectures. To ease the integration of tensor networks in machine learning pipelines, we introduce TensorKrowch, an open source Python library built on top of PyTorch. Providing a user-friendly interface, TensorKrowch allows users to construct any tensor network, train it, and integrate it as a layer in more intricate deep learning models. In this paper, we describe the main functionality and basic usage of TensorKrowch, and provide technical details on its building blocks and the optimizations performed to achieve efficient operation., Comment: 20 pages, 2 figures. The TensorKrowch GitHub repository is in https://github.com/joserapa98/tensorkrowch and the TensorKrowch documentation is in https://joserapa98.github.io/tensorkrowch. V3: Accepted version, corrected acknowledgments

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

We introduce a novel Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR) that demonstrates the feasibility of medical image retrieval with similar anatomical regions. RegionMIR addresses two major challenges for medical image retrieval i) standardization of clinically relevant searching criteria (e.g., anatomical, pathology-based), and ii) localization of anatomical area of interests that are semantically meaningful. In this work, we propose an ROI image retrieval image network that retrieves images with similar anatomy by extracting anatomical features (via bounding boxes) and evaluate similarity between pairwise anatomy-categorized features between the query and the database of images using contrastive learning. ROI queries are encoded using a contrastive-pretrained encoder that was fine-tuned for anatomy classification, which generates an anatomical-specific latent space for region-correlated image retrieval. During retrieval, we compare the anatomically encoded query to find similar features within a feature database generated from training samples, and retrieve images with similar regions from training samples. We evaluate our approach on both anatomy classification and image retrieval tasks using the Chest ImaGenome Dataset. Our proposed strategy yields an improvement over state-of-the-art pretraining and co-training strategies, from 92.24 to 94.12 (2.03%) classification accuracy in anatomies. We qualitatively evaluate the image retrieval performance demonstrating generalizability across multiple anatomies with different morphology.

Within the setting of the AdS/CFT correspondence, we ask about the power of computers in the presence of gravity. We show that there are computations on $n$ qubits which cannot be implemented inside of black holes with entropy less than $O(2^n)$. To establish our claim, we argue computations happening inside the black hole must be implementable in a programmable quantum processor, so long as the inputs and description of the unitary to be run are not too large. We then prove a bound on quantum processors which shows many unitaries cannot be implemented inside the black hole, and further show some of these have short descriptions and act on small systems. These unitaries with short descriptions must be computationally forbidden from happening inside the black hole.

We calculate the neutrino luminosity in an astrophysical scenario where dark matter is captured by a neutron star which eventually implodes to form a low mass black hole. The Trojan horse scenario involves the collapse of a neutron star (NS) due to the accumulation of a critical amount of dark matter (DM) during its lifetime. As a result, a central disk forms out of the ejected material with a finite radial extension, density, temperature, and lepton fraction, producing fainter neutrino luminosities and colder associated spectra than found in a regular core-collapse supernova. The emitted gravitational wave (GW) signal from the imploding NS should be detectable at ultra-high $\gtrsim 0.1$ GHz frequencies., Comment: comments are welcome

Label scarcity is a bottleneck for improving task performance in specialised domains. We propose a novel compositional transfer learning framework (DoT5 - domain compositional zero-shot T5) for zero-shot domain transfer. Without access to in-domain labels, DoT5 jointly learns domain knowledge (from MLM of unlabelled in-domain free text) and task knowledge (from task training on more readily available general-domain data) in a multi-task manner. To improve the transferability of task training, we design a strategy named NLGU: we simultaneously train NLG for in-domain label-to-data generation which enables data augmentation for self-finetuning and NLU for label prediction. We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on NLI, text summarisation and embedding learning. DoT5 demonstrates the effectiveness of compositional transfer learning through multi-task learning. In particular, DoT5 outperforms the current SOTA in zero-shot transfer by over 7 absolute points in accuracy on RadNLI. We validate DoT5 with ablations and a case study demonstrating its ability to solve challenging NLI examples requiring in-domain expertise., Comment: Accepted at TACL, pre-MIT Press publication version. 16 pages, 4 figures

The Burst Observer and Optical Transient Exploring System (BOOTES) was first designed as an asset of autonomous telescopes that started to be deployed in 1998, taking 24 years to be fully developed around the Earth. Nowadays BOOTES has became a global network of robotic telescopes, being the first one present in all continents, as of 2022. Here we present the details of the network and review its achievements over the last two decades regarding follow-up observations of high-energy transient events. Moreover, considering the recent operations of neutrino and gravitational wave detectors, some hot-topic expectations related to robotic astronomy are discussed within the framework of multi-wavelength astrophysics., Comment: Accepted for publication in Frontiers in Astronomy and Space Sciences

Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data., Comment: To appear in CVPR 2023

Using the quadratic expansion in the photon fields of Euler-Heisenberg (EH) non-linear electrodynamics (NLED) Lagrangian model we study relevant vacuum properties in a scenario involving the propagation of a photon probe in the presence of a background constant and static magnetic field, ${\bf B_e}$. We compute the gauge invariant, symmetric and conserved energy-momentum tensor (EMT) and angular momentum tensor (AMT) for arbitrary magnetic field strength using the Hilbert method under the soft-photon approximation. We discuss how the presence of magneto-electric terms in the EH Lagrangian is a source of anisotropy, induce the non-zero trace in the EMT and leads to differences between EMT calculated by the Hilbert or Noether method. From the Hilbert EMT we analyze some quantities of interest such as the energy density, pressures, Poynting vector, and angular momentum vector, comparing and discussing the differences with respect to the improved Noether method. The magnetized vacuum properties are also studied showing that a photon effective magnetic moment can be defined for different polarization modes. The calculations are done in terms of derivatives of the two scalar invariants of electrodynamics, hence, extension to other NLED Lagrangian is straightforward. We discuss further physical implications and experimental strategies to test magnetization, photon pressure, and effective magnetic moment., Comment: 16 pages, 3 figures, matches published version

We present a detailed prompt emission and early optical afterglow analysis of the two very high energy (VHE) detected bursts GRB 201015A and GRB 201216C, and their comparison with a subset of similar bursts. Time-resolved spectral analysis of multi-structured GRB 201216C using the Bayesian binning algorithm revealed that during the entire duration of the burst, the low energy spectral index ($\alpha_{\rm pt}$) remained below the limit of the synchrotron line of death. However, statistically some of the bins supported the additional thermal component. Additionally, the evolution of spectral parameters showed that both peak energy (Ep) and $\alpha_{\rm pt}$ tracked the flux. These results were further strengthened using the values of the physical parameters obtained by synchrotron modeling of the data. Our earliest optical observations of both bursts using FRAM-ORM and BOOTES robotic telescopes displayed a smooth bump in their early optical light curves, consistent with the onset of the afterglow due to synchrotron emission from an external forward shock. Using the observed optical peak, we constrained the initial bulk Lorentz factors of GRB 201015A and GRB 201216C to $\Gamma_0$ = 204 and $\Gamma_0$ = 310, respectively. The present early optical observations are the earliest known observations constraining outflow parameters and our analysis indicate that VHE-detected bursts could have a diverse range of observed luminosity within the detectable redshift range of present VHE facilities., Comment: 40 pages, 17 figures, 13 tables, accepted for publication in ApJ

This article is devoted to the mathematical modeling of migration of neuroblasts, precursor cells of neurons, along the pathway they usually follow before maturing. This pathway is determined mainly by chemotaxis and the heterogeneous mobility of neuroblasts in different regions of the brain. In numerical simulations, the application of novel discontinuous Galerkin methods allows to maintain the properties of the continuous model such as the maximum principle. We present some successful computer tests including parameter adjust to fit real data from rodent brains.

Tensor networks have a gauge degree of freedom on the virtual degrees of freedom that are contracted. A canonical form is a choice of fixing this degree of freedom. For matrix product states, choosing a canonical form is a powerful tool, both for theoretical and numerical purposes. On the other hand, for tensor networks in dimension two or greater there is only limited understanding of the gauge symmetry. Here we introduce a new canonical form, the minimal canonical form, which applies to projected entangled pair states (PEPS) in any dimension, and prove a corresponding fundamental theorem. Already for matrix product states this gives a new canonical form, while in higher dimensions it is the first rigorous definition of a canonical form valid for any choice of tensor. We show that two tensors have the same minimal canonical forms if and only if they are gauge equivalent up to taking limits; moreover, this is the case if and only if they give the same quantum state for any geometry. In particular, this implies that the latter problem is decidable - in contrast to the well-known undecidability for PEPS on grids. We also provide rigorous algorithms for computing minimal canonical forms. To achieve this we draw on geometric invariant theory and recent progress in theoretical computer science in non-commutative group optimization., Comment: 51 pages, more than 8 figures