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Inverse problems are key issues in several scientific areas, including signal processing and medical imaging. Data-driven approaches for inverse problems aim for learning model and regularization parameters from observed data samples, and investigate their generalization properties when confronted with unseen data. This approach dictates a statistical approach to inverse problems, calling for stochastic optimization methods. In order to learn model and regularisation parameters simultaneously, we develop in this paper a stochastic bilevel optimization approach in which the lower level problem represents a variational reconstruction method formulated as a convex non-smooth optimization problem, depending on the observed sample. The upper level problem represents the learning task of the regularisation parameters. Combining the lower level and the upper level problem leads to a stochastic non-smooth and non-convex optimization problem, for which standard gradient-based methods are not straightforward to implement. Instead, we develop a unified and flexible methodology, building on a derivative-free approach, which allows us to solve the bilevel optimization problem only with samples of the objective function values. We perform a complete complexity analysis of this scheme. Numerical results on signal denoising and experimental design demonstrate the computational efficiency and the generalization properties of our method.

The paper considers a computational governor strategy to facilitate the implementation of Model Predictive Control (MPC) based on inexact optimization when the time available to compute the solution may be insufficient. In the setting of linear-quadratic MPC and a class of optimizers that includes Alternating Direction Method of Multipliers (ADMM), we derive conditions on the reference command adjustment by the computational governor and on a constraint tightening strategy which ensure recursive feasibility, convergence of the modified reference command, and closed-loop stability. An online procedure to select the modified reference command and construct an implicit terminal set is also proposed. A simulation example is reported which illustrates the developed procedures.

Finding interpretable biomechanical models can provide insight into the functionality of organs with regard to physiology and disease. However, identifying broadly applicable dynamical models for in vivo tissue remains challenging. In this proof of concept study we propose a reconstruction framework for data-driven discovery of dynamical models from experimentally obtained undersampled MRI spectral data. The method makes use of the previously developed spectro-dynamic framework which allows for reconstruction of displacement fields at high spatial and temporal resolution required for model identification. The proposed framework combines this method with data-driven discovery of interpretable models using Sparse Identification of Non-linear Dynamics (SINDy). The design of the reconstruction algorithm is such that a symbiotic relation between the reconstruction of the displacement fields and the model identification is created. Our method does not rely on periodicity of the motion. It is successfully validated using spectral data of a dynamic phantom gathered on a clinical MRI scanner. The dynamic phantom is programmed to perform motion adhering to 5 different (non-linear) ordinary differential equations. The proposed framework performed better than a 2-step approach where the displacement fields were first reconstructed from the undersampled data without any information on the model, followed by data-driven discovery of the model using the reconstructed displacement fields. This study serves as a first step in the direction of data-driven discovery of in vivo models., Comment: 11 pages regular paper with 8 figures, 9 pages supplementary material with 6 figures, 1 supplementary video

Recent works demonstrate the effectiveness of diffusion models as unsupervised solvers for inverse imaging problems. Sparse-view computed tomography (CT) has greatly benefited from these advancements, achieving improved generalization without reliance on measurement parameters. However, this comes at the cost of potential hallucinations, especially when handling out-of-distribution (OOD) data. To ensure reliability, it is essential to study OOD detection for CT reconstruction across both clinical and industrial applications. This need further extends to enabling the OOD detector to function effectively as an anomaly inspection tool. In this paper, we explore the use of a diffusion model, trained to capture the target distribution for CT reconstruction, as an in-distribution prior. Building on recent research, we employ the model to reconstruct partially diffused input images and assess OOD-ness through multiple reconstruction errors. Adapting this approach for sparse-view CT requires redefining the notions of "input" and "reconstruction error". Here, we use filtered backprojection (FBP) reconstructions as input and investigate various definitions of reconstruction error. Our proof-of-concept experiments on the MNIST dataset highlight both successes and failures, demonstrating the potential and limitations of integrating such an OOD detector into a CT reconstruction system. Our findings suggest that effective OOD detection can be achieved by comparing measurements with forward-projected reconstructions, provided that reconstructions from noisy FBP inputs are conditioned on the measurements. However, conditioning can sometimes lead the OOD detector to inadvertently reconstruct OOD images well. To counter this, we introduce a weighting approach that improves robustness against highly informative OOD measurements, albeit with a trade-off in performance in certain cases.

Semi-supervised anomaly detection is based on the principle that potential anomalies are those records that look different from normal training data. However, in some cases we are specifically interested in anomalies that correspond to high attribute values (or low, but not both). We present two asymmetrical distance measures that take this directionality into account: ramp distance and signed distance. Through experiments on synthetic and real-life datasets we show that ramp distance performs as well or better than the absolute distance traditionally used in anomaly detection. While signed distance also performs well on synthetic data, it performs substantially poorer on real-life datasets. We argue that this reflects the fact that in practice, good scores on some attributes should not be allowed to compensate for bad scores on others.

Patients with traumatic brain injury (TBI) often experience pathological increases in intracranial pressure (ICP), leading to intracranial hypertension (tIH), a common and serious complication. Early warning of an impending rise in ICP could potentially improve patient outcomes by enabling preemptive clinical intervention. However, the limited availability of patient data poses a challenge in developing reliable prediction models. In this study, we aim to determine whether foundation models, which leverage transfer learning, may offer a promising solution.

Conditional density estimation (CDE) goes beyond regression by modeling the full conditional distribution, providing a richer understanding of the data than just the conditional mean in regression. This makes CDE particularly useful in critical application domains. However, interpretable CDE methods are understudied. Current methods typically employ kernel-based approaches, using kernel functions directly for kernel density estimation or as basis functions in linear models. In contrast, despite their conceptual simplicity and visualization suitability, tree-based methods -- which are arguably more comprehensible -- have been largely overlooked for CDE tasks. Thus, we propose the Conditional Density Tree (CDTree), a fully non-parametric model consisting of a decision tree in which each leaf is formed by a histogram model. Specifically, we formalize the problem of learning a CDTree using the minimum description length (MDL) principle, which eliminates the need for tuning the hyperparameter for regularization. Next, we propose an iterative algorithm that, although greedily, searches the optimal histogram for every possible node split. Our experiments demonstrate that, in comparison to existing interpretable CDE methods, CDTrees are both more accurate (as measured by the log-loss) and more robust against irrelevant features. Further, our approach leads to smaller tree sizes than existing tree-based models, which benefits interpretability., Comment: Accepted to Neurips 2024

Inhomogeneity of the transmitted radiofrequency field B1+ is a major factor hindering the image quality in Magnetic Resonance Imaging (MRI) at high field strengths. Here, a novel approach is presented, to locally modulate the B1+ utilizing an array of high permittivity materials with switchable connections. A 3$\times$3 array of barium titanate suspension elements was constructed, with two PIN diode-based switchable connectors per element. Electromagnetic simulations were performed to determine configurations that produce strong B1+ modulation. Remote B1+ field switching was tested in a disk- and and a torso-shaped phantom at 3T by applying different bias voltages to the PIN diodes. The attained B1+ modulation was assessed at various switching pattern positions and various depths within the phantoms. The configuration with the strongest effect size has produced up to 11% modulation in simulations at 15 mm depth, with excellent translation properties. The effects were successfully replicated in phantoms, with a 5 V bias voltage producing up to 11.6$\pm$0.2% modulation. At the relative depth of the human heart, up to 6% of modulation was observed in the torso phantom. The presented method may provide a promising direction for cost-effective, and adaptive B1+ shimming without changes to the scanner hardware., Comment: Contains 7 figures in the main text, and 1 figure in a supplementary document

We report the first successful ALMA follow-up observations of a secure $z > 10$ JWST-selected galaxy, by robustly detecting ($6.6\sigma$) the [OIII]$_{88\mu m}\,$ line in JADES-GS-z14-0 (hereafter GS-z14). The ALMA detection yields a spectroscopic redshift of $z=14.1793\pm0.0007$, and increases the precision on the prior redshift measurement of $z=14.32_{-0.20}^{+0.08}$ from NIRSpec by $\gtrsim$180$\times$. Moreover, the redshift is consistent with that previously determined from a tentative detection ($3.6\sigma$) of CIII]$_{1907,1909}$ ($z=14.178\pm0.013$), solidifying the redshift determination via multiple line detections. We measure a line luminosity of $L_\mathrm{[OIII]88} = (2.1 \pm 0.5)\times10^8\,L_\odot$, placing GS-z14 at the lower end, but within the scatter of, the local $L_\mathrm{[OIII]88}$-star formation rate relation. No dust continuum from GS-z14 is detected, suggesting an upper limit on the dust-to-stellar mass ratio of $< 2 \times 10^{-3}$, consistent with dust production from supernovae with a yield $y_d < 0.3\,M_\odot$. Combining a previous JWST/MIRI photometric measurement of the [OIII]$\lambda\lambda$4959,5007$\mathrm{\mathring{A}}$ and H$\beta$ lines with Cloudy models, we find GS-z14 to be surprisingly metal-enriched ($Z\sim0.05 - 0.2\,Z_\odot$) a mere $300\,\mathrm{Myr}$ after the Big Bang. The detection of a bright oxygen line in GS-z14 thus reinforces the notion that galaxies in the early Universe undergo rapid evolution., Comment: 11 Pages, 6 Figures, 1 Table. Submitted to The Astrophysical Journal

We present a new method to determine the star formation rate (SFR) density of the Universe at $z \gtrsim 5$ that includes the contribution of dust-obscured star formation. For this purpose, we use a [CII] (158 $\mu$m) selected sample of galaxies serendipitously identified in the fields of known $z\gtrsim 4.5$ objects to characterize the fraction of obscured SFR. The advantage of a [CII] selection is that our sample is SFR-selected, in contrast to a UV-selection that would be biased towards unobscured star formation. We obtain a sample of 23 [CII] emitters near star-forming (SF) galaxies and QSOs -- three of which we identify for the first time -- using previous literature and archival ALMA data. 18 of these serendipitously identified galaxies have sufficiently deep rest-UV data and are used to characterize the obscured fraction of the star formation in galaxies with SFRs $\gtrsim 30\ \text{M}_{\odot} \ \text{yr}^{-1}$. We find that [CII] emitters identified around SF galaxies have $\approx$63\% of their SFR obscured, while [CII] emitters around QSOs have $\approx$93\% of their SFR obscured. By forward modeling existing wide-area UV luminosity function (LF) determinations, we derive the intrinsic UV LF using our characterization of the obscured SFR. Integrating the intrinsic LF to $M_{UV}$ = $-$20 we find that the obscured SFRD contributes to $>3\%$ and $>10\%$ of the total SFRD at $z \sim 5$ and $z \sim 6$ based on our sample of companions galaxies near SFGs and QSOs, respectively. Our results suggest that dust obscuration is not negligible at $z\gtrsim 5$, further underlining the importance of far-IR observations of the $z\gtrsim 5$ Universe., Comment: Accepted for publication in MNRAS. 24 pages, 14 figures, 8 tables (including appendices)

Computed tomography (CT) reconstruction plays a crucial role in industrial nondestructive testing and medical diagnosis. Sparse view CT reconstruction aims to reconstruct high-quality CT images while only using a small number of projections, which helps to improve the detection speed of industrial assembly lines and is also meaningful for reducing radiation in medical scenarios. Sparse CT reconstruction methods based on implicit neural representations (INRs) have recently shown promising performance, but still produce artifacts because of the difficulty of obtaining useful prior information. In this work, we incorporate a powerful prior: the total number of material categories of objects. To utilize the prior, we design AC-IND, a self-supervised method based on Attenuation Coefficient Estimation and Implicit Neural Distribution. Specifically, our method first transforms the traditional INR from scalar mapping to probability distribution mapping. Then we design a compact attenuation coefficient estimator initialized with values from a rough reconstruction and fast segmentation. Finally, our algorithm finishes the CT reconstruction by jointly optimizing the estimator and the generated distribution. Through experiments, we find that our method not only outperforms the comparative methods in sparse CT reconstruction but also can automatically generate semantic segmentation maps., Comment: 12 pages

The Extended Church-Turing Thesis (ECTT) posits that all effective information processing, including unbounded and non-uniform interactive computations, can be described in terms of interactive Turing machines with advice. Does this assertion also apply to the abilities of contemporary large language models (LLMs)? From a broader perspective, this question calls for an investigation of the computational power of LLMs by the classical means of computability and computational complexity theory, especially the theory of automata. Along these lines, we establish a number of fundamental results. Firstly, we argue that any fixed (non-adaptive) LLM is computationally equivalent to a, possibly very large, deterministic finite-state transducer. This characterizes the base level of LLMs. We extend this to a key result concerning the simulation of space-bounded Turing machines by LLMs. Secondly, we show that lineages of evolving LLMs are computationally equivalent to interactive Turing machines with advice. The latter finding confirms the validity of the ECTT for lineages of LLMs. From a computability viewpoint, it also suggests that lineages of LLMs possess super-Turing computational power. Consequently, in our computational model knowledge generation is in general a non-algorithmic process realized by lineages of LLMs. Finally, we discuss the merits of our findings in the broader context of several related disciplines and philosophies., Comment: In Proceedings NCMA 2024, arXiv:2409.06120

This paper presents an overview of a survey of jet substructure observables used to study modifications of jets induced by interaction with a Quark Gluon Plasma. We further outline ideas that were presented and discussed at the \textit{New jet quenching tools to explore equilibrium and non-equilibrium dynamics in heavy-ion collisions} workshop, which was held in February 2024 at the ECT$^{*}$ in Trento, Italy. The goal of this white paper is to provide a brief report on the study of jet quenching observables earlier conducted and to present new ideas that could be relevant for future explorations., Comment: 12 pages, 2 figures, NA3:Jet-QGP group white paper

A central pillar of the UK's response to the SARS-CoV-2 pandemic was the provision of up-to-the moment nowcasts and short term projections to monitor current trends in transmission and associated healthcare burden. Here we present a detailed deconstruction of one of the 'real-time' models that was key contributor to this response, focussing on the model adaptations required over three pandemic years characterised by the imposition of lockdowns, mass vaccination campaigns and the emergence of new pandemic strains. The Bayesian model integrates an array of surveillance and other data sources including a novel approach to incorporating prevalence estimates from an unprecedented large-scale household survey. We present a full range of estimates of the epidemic history and the changing severity of the infection, quantify the impact of the vaccination programme and deconstruct contributing factors to the reproduction number. We further investigate the sensitivity of model-derived insights to the availability and timeliness of prevalence data, identifying its importance to the production of robust estimates., Comment: 35 pages, 7 figures, 3 tables

We present new multi-frequency ALMA continuum observations of the massive [$\log_{10}(M_\star/M_\odot) = 10.3_{-0.2}^{+0.1}$], UV-luminous [$M_\mathrm{UV} = -21.7 \pm 0.2$] $z=7.31$ galaxy REBELS-25 in Bands 3, 4, 5, and 9. Combining the new observations with previously-taken data in Bands 6 and 8, we cover the dust continuum emission of the galaxy in six distinct bands -- spanning rest-frame $50-350\,\mu$m -- enabling simultaneous constraints on its dust mass ($M_\mathrm{dust}$), temperature ($T_\mathrm{dust}$) and emissivity index ($\beta_\mathrm{IR}$) via modified blackbody fitting. Given a fiducial model of optically thin emission, we infer a cold dust temperature of $T_\mathrm{dust} = 32_{-6}^{+9}\,$K and a high dust mass of $\log_{10}(M_\mathrm{dust}/M_\odot) = 8.2_{-0.4}^{+0.6}$, and moderately optically thick dust does not significantly alter these estimates. If we assume dust production is solely through supernovae (SNe), the inferred dust yield would be high, $y = 0.7_{-0.4}^{+2.3}\,M_\odot$ per SN. Consequently, we argue grain growth in the interstellar medium of REBELS-25 also contributes to its dust build-up. This is supported by the steep dust emissivity index $\beta_\mathrm{IR} = 2.5 \pm 0.4$ we measure for REBELS-25, as well as by its high stellar mass, dense interstellar medium, and metal-rich nature. Our results suggest that constraining the dust emissivity indices of high-redshift galaxies is important not only to mitigate systematic uncertainties in their dust masses and obscured star formation rates, but also to assess if dust properties evolve across cosmic time. We present an efficient observing setup to do so with ALMA, combining observations of the peak and Rayleigh-Jeans tail of the dust emission., Comment: 14 pages main text, 6 figures + appendices; re-submitted to MNRAS after a positive referee report

Gravitational waves from black-hole merging events have revealed a population of extra-galactic BHs residing in short-period binaries with masses that are higher than expected based on most stellar evolution models - and also higher than known stellar-origin black holes in our Galaxy. It has been proposed that those high-mass BHs are the remnants of massive metal-poor stars. Gaia astrometry is expected to uncover many Galactic wide-binary systems containing dormant BHs, which may not have been detected before. The study of this population will provide new information on the BH-mass distribution in binaries and shed light on their formation mechanisms and progenitors. As part of the validation efforts in preparation for the fourth Gaia data release (DR4), we analysed the preliminary astrometric binary solutions, obtained by the Gaia Non-Single Star pipeline, to verify their significance and to minimise false-detection rates in high-mass-function orbital solutions. The astrometric binary solution of one source, Gaia BH3, implies the presence of a 32.70 \pm 0.82 M\odot BH in a binary system with a period of 11.6 yr. Gaia radial velocities independently validate the astrometric orbit. Broad-band photometric and spectroscopic data show that the visible component is an old, very metal-poor giant of the Galactic halo, at a distance of 590 pc. The BH in the Gaia BH3 system is more massive than any other Galactic stellar-origin BH known thus far. The low metallicity of the star companion supports the scenario that metal-poor massive stars are progenitors of the high-mass BHs detected by gravitational-wave telescopes. The Galactic orbit of the system and its metallicity indicate that it might belong to the Sequoia halo substructure. Alternatively, and more plausibly, it could belong to the ED-2 stream, which likely originated from a globular cluster that had been disrupted by the Milky Way., Comment: 23 pages, accepted fro publication in A&A Letters. New version with small fixes

Pulsars often display systematic variations in the position and/or intensity of the subpulses, the components that comprise each single pulse. Although the drift of these subpulses was observed in the early years of pulsar research, and their potential for understanding the elusive emission mechanism was quickly recognised, there is still no consensus on the cause of the drift. We explore the electrodynamics of two recently proposed or refined drift models: one where plasma lags behind corotation, connecting the drift with the rotational pole; and another where plasma drifts around the electric potential extremum of the polar cap. Generally, these are different locations, resulting in different drift behaviours, that can be tested with observations. In this study, however, we specifically examine these models in the axisymmetric case, where the physics is well understood. This approach seems counter-intuitive as both models then predict similar large-scale plasma drift. However, it allows us to show, by studying conditions \emph{within} the sparks for both models, that the lagging behind corotation (LBC) model is inconsistent with Faraday's law. The modified carousel (MC) model, where plasma drifts around the electric potential extremum, not only aligns with Faraday's law, but also provides a future direction for developing a comprehensive model of plasma generation in the polar cap region. Unlike previous models, which considered the drift only inside the discharging regions, the MC model reveals that the electric field \emph{between} the discharges is not completely screened, and plasma drifts there -- a paradigm shift for the drifting subpulse phenomenon., Comment: 7 pages, 4 figures, accepted for publication in MNRAS

A recently developed viewpoint on the fundamentals of density-functional theory for finite interacting spin-lattice systems that centers around the notion of degeneracy regions is presented. It allows for an entirely geometrical description of the Hohenberg-Kohn theorem and v-representability. The phenomena receive exemplification by an Anderson impurity model and other small-lattice examples. The case of adiabatic change and the time-dependent setting are examined as well.

We report detections of fast radio bursts (FRBs) from the repeating source FRB 20201124A with Apertif/WSRT and GMRT, and measurements of basic burst properties, especially the dispersion measure (DM) and fluence. Based on comparisons of these properties with previously published larger samples, we argue that the excess DM reported earlier for pulses with integrated signal to noise ratio $\lesssim 1000$ is due to incompletely accounting for the so-called sad trombone effect, even when using structure-maximizing DM algorithms. Our investigations of fluence distributions next lead us to advise against formal power-law fitting, especially dissuading the use of the least-square method, and we demonstrate the large biases involved. A maximum likelihood estimator (MLE) provides a much more accurate estimate of the power law and we provide accessible code for direct inclusion in future research. Our GMRT observations were fortuitously scheduled around the end of the activity cycle as recorded by FAST. We detected several bursts (one of them very strong) at 400/600 MHz, a few hours after sensitive FAST non-detections already showed the 1.3 GHz FRB emission to have ceased. After FRB 20180916B, this is a second example of a frequency-dependent activity window identified in a repeating FRB source. Since numerous efforts have so-far failed to determine a spin period for FRB 20201124A, we conjecture it to be an ultra-long period magnetar, with a period on the scale of months, and with a very wide, highly irregular duty cycle. Assuming the emission comes from closed field lines, we use radius-to-frequency mapping and polarization information from other studies to constrain the magnetospheric geometry and location of the emission region. Our initial findings are consistent with a possible connection between FRBs and crustal motion events., Comment: submitted to A&A

Label-free quantitative phase imaging is a vital tool for optical microscopy and metrology applications. A hair-thin multimode fiber stands out as a very attractive platform for minimally invasive imaging. Here we propose and experimentally demonstrate a non-interferometric non-iterative approach for high-speed high-resolution label-free quantitative phase imaging via a multimode fiber, unlocking multiple applications in life science and bioimaging.

We develop a method to deduce the symmetry properties of many-body Hamiltonians when they are prepared in Jordan-Wigner form for evaluation on quantum computers. Symmetries, such as point-group symmetries in molecules, are apparent in the standard second quantized form of the Hamiltonian. They are, however, masked when the Hamiltonian is translated into a Pauli matrix representation required for its operation on qubits. To reveal these symmetries we prove a general theorem that provides a straightforward method to calculate the transformation of Pauli tensor strings under symmetry operations. They are a subgroup of the Clifford group transformations and induce a corresponding group representation inside the symplectic matrices. We finally give a simplified derivation of an affine qubit encoding scheme which allows for the removal of qubits due to Boolean symmetries and thus reduces computational effort in quantum computing applications.

Transformer models have recently become very successful in the natural language domain. Their value as sequence-to-sequence translators there, also makes them a highly interesting technique for learning relationships between astrophysical time series. Our aim is investigate how well such a transformer neural network can establish causal temporal relations between different channels of a single-source signal. We thus apply a transformer model to the two phases of Gamma-Ray Bursts (GRBs), reconstructing one phase from the other. GRBs are unique instances where a single process and event produces two distinct time variable phenomena: the prompt emission and the afterglow. We here investigate if a transformer model can predict the afterglow flux from the prompt emission. If successful, such a predictive scheme might then be distilled to the most important underlying physics drivers in the future. We combine the transformer model with a novel dense neural network setup to directly estimate the starting value of the prediction. We find that the transformer model can, in some instances, successfully predict different phases of canonical afterglows, including the plateau phase. Hence it is a useful and promising new astrophysical analysis technique. For the GRB test case, the method marginally exceeds the baseline model overall, but still achieves accurate recovery of the prompt-afterglow fluence-fluence correlation in reconstructed light curves. Despite this progress, we conclude that consistent improvement over the baseline model is not yet achieved for the GRB case. We discuss the future improvements in data and modeling that are required to identify new physical-relation parameters or new insights into the single process driving both GRB phases., Comment: 13 pages, 11 figures. Accepted for RAS Techniques & Instruments, further comments welcome

Background: External validations are essential to assess clinical prediction models (CPMs) before deployment. Apart from model misspecification, differences in patient population and other factors influence a model's AUC (c-statistic). We aimed to quantify variation in AUCs across external validation studies and adjust expectations of a model's performance in a new setting. Methods: The Tufts-PACE CPM Registry contains CPMs for cardiovascular disease prognosis. We analyzed the AUCs of 469 CPMs with a total of 1,603 external validations. For each CPM, we performed a random effects meta-analysis to estimate the between-study standard deviation $\tau$ among the AUCs. Since the majority of these meta-analyses has only a handful of validations, this leads to very poor estimates of $\tau$. So, we estimated a log normal distribution of $\tau$ across all CPMs and used this as an empirical prior. We compared this empirical Bayesian approach with frequentist meta-analyses using cross-validation. Results: The 469 CPMs had a median of 2 external validations (IQR: [1-3]). The estimated distribution of $\tau$ had a mean of 0.055 and a standard deviation of 0.015. If $\tau$ = 0.05, the 95% prediction interval for the AUC in a new setting is at least +/- 0.1, regardless of the number of validations. Frequentist methods underestimate the uncertainty about the AUC in a new setting. Accounting for $\tau$ in a Bayesian approach achieved near nominal coverage. Conclusion: Due to large heterogeneity among the validated AUC values of a CPM, there is great irreducible uncertainty in predicting the AUC in a new setting. This uncertainty is underestimated by existing methods. The proposed empirical Bayes approach addresses this problem which merits wide application in judging the validity of prediction models.

Predictions made by graph neural networks (GNNs) usually lack interpretability due to their complex computational behavior and the abstract nature of graphs. In an attempt to tackle this, many GNN explanation methods have emerged. Their goal is to explain a model's predictions and thereby obtain trust when GNN models are deployed in decision critical applications. Most GNN explanation methods work in a post-hoc manner and provide explanations in the form of a small subset of important edges and/or nodes. In this paper we demonstrate that these explanations can unfortunately not be trusted, as common GNN explanation methods turn out to be highly susceptible to adversarial perturbations. That is, even small perturbations of the original graph structure that preserve the model's predictions may yield drastically different explanations. This calls into question the trustworthiness and practical utility of post-hoc explanation methods for GNNs. To be able to attack GNN explanation models, we devise a novel attack method dubbed \textit{GXAttack}, the first \textit{optimization-based} adversarial white-box attack method for post-hoc GNN explanations under such settings. Due to the devastating effectiveness of our attack, we call for an adversarial evaluation of future GNN explainers to demonstrate their robustness. For reproducibility, our code is available via GitHub.

Understanding the origin of fast radio bursts (FRBs) has become the main science driver of recent dedicated FRB surveys. Between July 2019 and February 2022, we carried out ALERT, an FRB survey at 1370 MHz using the Apertif instrument installed at the Westerbork Synthesis Radio Telescope (WSRT). Here we report the detection of 18 new FRBs, and we study the properties of the entire 24 burst sample detected during the survey. For five bursts, we identify host galaxy candidates with >50% probability association. We observe an average linear polarisation fraction of $\sim$43% and an average circular polarisation fraction consistent with 0%. A third of the FRBs display multiple components. The sample next reveals a population of highly scattered bursts, which is most likely to have been produced in the immediate circumburst environment. Furthermore, two FRBs show evidence for high rotation measures, reaching |RM|>$10^3$ rad m$^{-2}$ in the source reference frames. Together, the scattering and rotation measures ALERT finds prove that a large fraction of FRBs are embedded in complex media such as star forming regions or supernova remnants. Through the discovery of the third most dispersed FRB so far, we show that one-off FRBs can emit at frequencies in excess of 6 GHz. Finally, we determine an FRB all-sky rate of $459^{+208}_{-155}$ sky$^{-1}$ day$^{-1}$ above a fluence limit of 4.1 Jy ms, and a fluence cumulative distribution with a power law index $\gamma=-1.23\pm0.06\pm0.2$, which is roughly consistent with the Euclidean Universe predictions. Through the high resolution in time, frequency, polarisation and localisation that ALERT featured, we were able to determine the morphological complexity, polarisation, local scattering and magnetic environment, and high-frequency luminosity of FRBs. We find all these strongly resemble those seen in young, energetic, highly magnetised neutron stars., Comment: 40 pages (including 11 of appendix), 37 figures, 5 tables. Accepted for publication in A&A

Ephemeral Fast Radio Bursts (FRBs) must be powered by some of the most energetic processes in the Universe. That makes them highly interesting in their own right and as precise probes for estimating cosmological parameters. This field thus poses a unique challenge: FRBs must be detected promptly and immediately localised and studied based only on that single millisecond-duration flash. The problem is that the burst occurrence is highly unpredictable and that their distance strongly suppresses their brightness. Since the discovery of FRBs in single-dish archival data in 2007, detection software has evolved tremendously. Pipelines now detect bursts in real-time within a matter of seconds, operate on interferometers, buffer high-time and frequency resolution data, and issue real-time alerts to other observatories for rapid multi-wavelength follow-up. In this paper, we review the components that comprise a FRB search software pipeline, we discuss the proven techniques that were adopted from pulsar searches, we highlight newer, more efficient techniques for detecting FRBs, and we conclude by discussing the proposed novel future methodologies that may power the search for FRBs in the era of big data astronomy., Comment: 17 pages, 7 figures, published in the special issue of the Universe journal (Guest editor: Maura Pilia)

A complete and statistically consistent uncertainty quantification for deep learning is provided, including the sources of uncertainty arising from (1) the new input data, (2) the training and testing data (3) the weight vectors of the neural network, and (4) the neural network because it is not a perfect predictor. Using Bayes Theorem and conditional probability densities, we demonstrate how each uncertainty source can be systematically quantified. We also introduce a fast and practical way to incorporate and combine all sources of errors for the first time. For illustration, the new method is applied to quantify errors in cloud autoconversion rates, predicted from an artificial neural network that was trained by aircraft cloud probe measurements in the Azores and the stochastic collection equation formulated as a two-moment bin model. For this specific example, the output uncertainty arising from uncertainty in the training and testing data is dominant, followed by uncertainty in the input data, in the trained neural network, and uncertainty in the weights. We discuss the usefulness of the methodology for machine learning practice, and how, through inclusion of uncertainty in the training data, the new methodology is less sensitive to input data that falls outside of the training data set., Comment: 25 pages 4 figures, submitted to Environmental data Science

Collecting and annotating real-world data for the development of object detection models is a time-consuming and expensive process. In the military domain in particular, data collection can also be dangerous or infeasible. Training models on synthetic data may provide a solution for cases where access to real-world training data is restricted. However, bridging the reality gap between synthetic and real data remains a challenge. Existing methods usually build on top of baseline Convolutional Neural Network (CNN) models that have been shown to perform well when trained on real data, but have limited ability to perform well when trained on synthetic data. For example, some architectures allow for fine-tuning with the expectation of large quantities of training data and are prone to overfitting on synthetic data. Related work usually ignores various best practices from object detection on real data, e.g. by training on synthetic data from a single environment with relatively little variation. In this paper we propose a methodology for improving the performance of a pre-trained object detector when training on synthetic data. Our approach focuses on extracting the salient information from synthetic data without forgetting useful features learned from pre-training on real images. Based on the state of the art, we incorporate data augmentation methods and a Transformer backbone. Besides reaching relatively strong performance without any specialized synthetic data transfer methods, we show that our methods improve the state of the art on synthetic data trained object detection for the RarePlanes and DGTA-VisDrone datasets, and reach near-perfect performance on an in-house vehicle detection dataset., Comment: Submitted to and presented at SPIE Defense + Commercial Sensing 2024, 13 pages, 4 figures, 3 tables

While the appeal of their extraordinary radio luminosity to our curiosity is undiminished, the nature of fast radio bursts (FRBs) has remained unclear. The challenge has been due in part to small sample sizes and limited understanding of telescope selection effects. We here present the first inclusion of the entire set of one-off FRBs from CHIME/FRB Catalog 1 in frbpoppy. Where previous work had to curate this data set, and fit for few model parameters, we have developed full multi-dimensional Markov chain Monte Carlo (MCMC) capabilities for frbpoppy -- the comprehensive, open-science FRB population synthesis code -- that allow us to include all one-off CHIME bursts. Through the combination of these two advances we now find the best description of the real, underlying FRB population, with higher confidence than before. We show that $4\pm3\times10^{3}$ one-off FRBs go off every second between Earth and $z$=1; and we provide a mock catalog based on our best model, for straight-forward inclusion in other studies. We investigate CHIME side-lobe detection fractions, and FRB luminosity characteristics, to show that some bright, local FRBs are still being missed. We find strong evidence that FRB birth rates evolve with the star formation rate of the Universe, even with a hint of a short (0.1$-$1 Gyr) delay time. The preferred contribution of the hosts to the FRB dispersion agrees with a progenitor birth location in the host disk. This population-based evidence solidly aligns with magnetar-like burst sources, and we conclude FRBs are emitted by neutron stars., Comment: 20 pages, 15 figures. Accepted by A&A

We present high resolution ($\sim0.14$" = 710 pc) ALMA [CII] 158$\mu$m and dust continuum follow-up observations of REBELS-25, a [CII]-luminous ($L_{\mathrm{[CII]}}=(1.7\pm0.2)\times 10^9 \mathrm{L_{\odot}}$) galaxy at redshift $z=7.3065\pm0.0001$. These high resolution, high signal-to-noise observations allow us to study the sub-kpc morphology and kinematics of this massive ($M_* = 8^{+4}_{-2} \times 10^9 \mathrm{M_{\odot}}$) star-forming (SFR$_{\mathrm{UV+IR}} = 199^{+101}_{-63} \mathrm{M_{\odot}} \mathrm{yr}^{-1}$) galaxy in the Epoch of Reionisation. By modelling the kinematics with $^{\mathrm{3D}}$BAROLO, we find it has a low velocity dispersion ($\bar{\sigma} = 33 \pm 9$ km s$^{-1}$) and a high ratio of ordered-to-random motion ($V_{\mathrm{rot, ~max}}/\bar{\sigma} = 11 ^{+8}_{-4}$), indicating that REBELS-25 is a dynamically cold disc. Additionally, we find that the [CII] distribution is well fit by a near-exponential disc model, with a S\'ersic index, $n$, of $1.3 \pm 0.2$, and we see tentative evidence of more complex non-axisymmetric structures suggestive of a bar in the [CII] and dust continuum emission. By comparing to other high spatial resolution cold gas kinematic studies, we find that dynamically cold discs seem to be more common in the high redshift Universe than expected based on prevailing galaxy formation theories, which typically predict more turbulent and dispersion-dominated galaxies in the early Universe as an outcome of merger activity, gas accretion and more intense feedback. This higher degree of rotational support seems instead to be consistent with recent cosmological simulations that have highlighted the contrast between cold and warm ionised gas tracers, particularly for massive galaxies. We therefore show that dynamically settled disc galaxies can form as early as 700 Myr after the Big Bang., Comment: Submitted to MNRAS