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We confirm at the $5.7\sigma$ level previous studies reporting Cosmic Microwave Background (CMB) temperatures being significantly lower around nearby spiral galaxies than expected in the $\Lambda$CDM model. The significance reported in our earlier work was disputed by Addison 2024, who reported lower signficances when including pixels at distances far beyond the galactic halos while disregarding pixels close to the galaxies where the main signal is seen. Here we limit the study to pixels well within the galactic halos, focus on galaxies in dense cosmic filaments and improve on signal-to-noise compared to previous studies. The average CMB temperature in discs around these galaxies is always much lower in Planck data than in any of the 10.000 Planck-like CMB simulations. Even when correcting for the look-elsewhere-effect, the detection is still at the $3-4\sigma$ level. We further show that the largest scales ($\ell<16$) of the Planck CMB fluctuations are more correlated with the distribution of nearby galaxies than $99.99\%$ of simulated CMB maps. We argue that the existence of a new CMB foreground cannot be ignored and a physical interaction mechanism, possibly involving dark matter, as well as linked to intergalactic magnetic fields, should be sought., Comment: Submitted to A&A

In Kaluza-Klein compactifications, some symmetries of the higher dimensional theory are preserved in lower dimensions, others are broken, and occasionally, there are symmetry enhancements. The symmetries that are enhanced by toroidal compactifications were recently shown to define a symmetry principle with constrained parameters that fixes the action before dimensional reduction. Here we show the opposite: symmetries of the higher dimensional theory that are broken in the reduction process, can actually be realized after dimensional reduction as a global symmetry principle with constrained parameters that fixes couplings in the lower dimensional theory. We implement this principle in pure gravity, half-maximal supergravity and the circle reduction of 11 dimensional supergravity to Type IIA superstring theory. As a further application, we show that it can be used to constrain the quartic Ramond-Ramond couplings in Type IIA superstring theory from the four-point $\zeta(3)\, \alpha'{}^3\, t_8 t_8 R^{(-) 4}$ interactions., Comment: 36p

Generative diffusion models have recently emerged as a powerful strategy to perform stochastic sampling in Bayesian inverse problems, delivering remarkably accurate solutions for a wide range of challenging applications. However, diffusion models often require a large number of neural function evaluations per sample in order to deliver accurate posterior samples. As a result, using diffusion models as stochastic samplers for Monte Carlo integration in Bayesian computation can be highly computationally expensive. This cost is especially high in large-scale inverse problems such as computational imaging, which rely on large neural networks that are expensive to evaluate. With Bayesian imaging problems in mind, this paper presents a Multilevel Monte Carlo strategy that significantly reduces the cost of Bayesian computation with diffusion models. This is achieved by exploiting cost-accuracy trade-offs inherent to diffusion models to carefully couple models of different levels of accuracy in a manner that significantly reduces the overall cost of the calculation, without reducing the final accuracy. The effectiveness of the proposed Multilevel Monte Carlo approach is demonstrated with three canonical computational imaging problems, where we observe a $4\times$-to-$8\times$ reduction in computational cost compared to conventional Monte Carlo averaging., Comment: 13 images

Score-based diffusion methods provide a powerful strategy to solve image restoration tasks by flexibly combining a pre-trained foundational prior model with a likelihood function specified during test time. Such methods are predominantly derived from two stochastic processes: reversing Ornstein-Uhlenbeck, which underpins the celebrated denoising diffusion probabilistic models (DDPM) and denoising diffusion implicit models (DDIM), and the Langevin diffusion process. The solutions delivered by DDPM and DDIM are often remarkably realistic, but they are not always consistent with measurements because of likelihood intractability issues and the associated required approximations. Alternatively, using a Langevin process circumvents the intractable likelihood issue, but usually leads to restoration results of inferior quality and longer computing times. This paper presents a novel and highly computationally efficient image restoration method that carefully embeds a foundational DDPM denoiser within an empirical Bayesian Langevin algorithm, which jointly calibrates key model hyper-parameters as it estimates the model's posterior mean. Extensive experimental results on three canonical tasks (image deblurring, super-resolution, and inpainting) demonstrate that the proposed approach improves on state-of-the-art strategies both in image estimation accuracy and computing time., Comment: 24 pages

The ESO public survey VISTA Variables in the V\'ia L\'actea (VVV) surveyed the inner Galactic bulge and the adjacent southern Galactic disk from $2009-2015$. Upon its conclusion, the complementary VVV eXtended (VVVX) survey has expanded both the temporal as well as spatial coverage of the original VVV area, widening it from $562$ to $1700$ sq. deg., as well as providing additional epochs in $JHK_{\rm s}$ filters from $2016-2023$. With the completion of VVVX observations during the first semester of 2023, we present here the observing strategy, a description of data quality and access, and the legacy of VVVX. VVVX took $\sim 2000$ hours, covering about 4% of the sky in the bulge and southern disk. VVVX covered most of the gaps left between the VVV and the VISTA Hemisphere Survey (VHS) areas and extended the VVV time baseline in the obscured regions affected by high extinction and hence hidden from optical observations. VVVX provides a deep $JHK_{\rm s}$ catalogue of $\gtrsim 1.5\times10^9$ point sources, as well as a $K_{\rm s}$ band catalogue of $\sim 10^7$ variable sources. Within the existing VVV area, we produced a $5D$ map of the surveyed region by combining positions, distances, and proper motions of well-understood distance indicators such as red clump stars, RR Lyrae, and Cepheid variables. In March 2023 we successfully finished the VVVX survey observations that started in 2016, an accomplishment for ESO Paranal Observatory upon 4200 hours of observations for VVV+VVVX. The VVV+VVVX catalogues complement those from the Gaia mission at low Galactic latitudes and provide spectroscopic targets for the forthcoming ESO high-multiplex spectrographs MOONS and 4MOST., Comment: 17 pages, 11 figures (+ appendix). Accepted for publication in Astronomy and Astrophysics in section 14: Catalogs and data

We analyse photometric data of nine supernovae (SNe) in filters V, R and I obtained during observational campaigns at the OAUNI site in 2016, 2017 and 2023. The calibrated magnitudes of the observed SNe were compared with their respective light curves available in the literature to study their evolution after their maximum brightness. In some cases, the supernova color-color diagnostic diagram was used to determine our observation date and correctly locate our magnitudes on the light curves. For this purpose, the use of supernova light curve templates, as well as reference supernovae, was also helpful. This work allowed us to verify the feasibility of performing precision astronomical photometry at the OAUNI., Comment: 28 pages, 14 figures, to be published in RMxAA

This study focuses on analyzing dermoscopy images to determine the depth of melanomas, which is a critical factor in diagnosing and treating skin cancer. The Breslow depth, measured from the top of the granular layer to the deepest point of tumor invasion, serves as a crucial parameter for staging melanoma and guiding treatment decisions. This research aims to improve the prediction of the depth of melanoma through the use of machine learning models, specifically deep learning, while also providing an analysis of the possible existance of graduation in the images characteristics which correlates with the depth of the melanomas. Various datasets, including ISIC and private collections, were used, comprising a total of 1162 images. The datasets were combined and balanced to ensure robust model training. The study utilized pre-trained Convolutional Neural Networks (CNNs). Results indicated that the models achieved significant improvements over previous methods. Additionally, the study conducted a correlation analysis between model's predictions and actual melanoma thickness, revealing a moderate correlation that improves with higher thickness values. Explainability methods such as feature visualization through Principal Component Analysis (PCA) demonstrated the capability of deep features to distinguish between different depths of melanoma, providing insight into the data distribution and model behavior. In summary, this research presents a dual contribution: enhancing the state-of-the-art classification results through advanced training techniques and offering a detailed analysis of the data and model behavior to better understand the relationship between dermoscopy images and melanoma thickness.

The student-optimal stable mechanism (DA), the most popular mechanism in school choice, is the only one that is both stable and strategy-proof. However, when DA is implemented, a student can change the schools of others without changing her own. We show that this drawback is limited: a student cannot change her classmates without modifying her school. We refer to this new property as {\it{local non-bossiness}} and use it to provide a new characterization of DA that does not rely on stability. Furthermore, we show that local non-bossiness plays a crucial role in providing incentives to be truthful when students have preferences over their colleagues. As long as students first consider the school to which they are assigned and then their classmates, DA induces the only stable and strategy-proof mechanism. There is limited room to expand this preference domain without compromising the existence of a stable and strategy-proof mechanism.

Bayesian statistics is a cornerstone of imaging sciences, underpinning many and varied approaches from Markov random fields to score-based denoising diffusion models. In addition to powerful image estimation methods, the Bayesian paradigm also provides a framework for uncertainty quantification and for using image data as quantitative evidence. These probabilistic capabilities are important for the rigorous interpretation of experimental results and for robust interfacing of quantitative imaging pipelines with scientific and decision-making processes. However, are the probabilities delivered by existing Bayesian imaging methods meaningful under replication of an experiment, or are they only meaningful as subjective measures of belief? This paper presents a Monte Carlo method to explore this question. We then leverage the proposed Monte Carlo method and run a large experiment requiring 1,000 GPU-hours to probe the accuracy of five canonical Bayesian imaging methods that are representative of some of the main Bayesian imaging strategies from the past decades (a score-based denoising diffusion technique, a plug-and-play Langevin algorithm utilising a Lipschitz-regularised DnCNN denoiser, a Bayesian method with a dictionary-based prior trained subject to a log-concavity constraint, an empirical Bayesian method with a total-variation prior, and a hierarchical Bayesian Gibbs sampler based on a Gaussian Markov random field model). We find that, a few cases, the probabilities reported by modern Bayesian imaging techniques are in broad agreement with long-term averages as observed over a large number of replication of an experiment, but existing Bayesian imaging methods are generally not able to deliver reliable uncertainty quantification results.

We study the evolution of the Halo Occupation Distribution (HOD) and galaxy properties of nodes and filamentary structures obtained by \textsc{DisPerSE} from the \textsc{Illustris TNG300-1} hydrodynamical simulation, in the redshift range $0 \leq z \leq 2$. We compute the HOD in filaments and nodes and fit the HOD parameters to study their evolution for both faint and bright galaxies. In nodes, the number of faint galaxies increases with decreasing redshift in the low mass halos, while no significant differences are seen in high mass halos. Limiting the HOD to bright galaxies shows that halos increase in mass more than the number of bright galaxies they accrete. For filaments, no large differences in HOD are found for faint galaxies, although for brighter galaxies the behaviour is the same as in nodes. The HOD parametrization suggests that filaments have no effect on the mass required to host a galaxy (central or satellite), whereas nodes do. The results of the study indicate that with this parametrization, filaments do not seem to affect the stellar mass content of galaxies. In contrast, nodes appear to affect halos with masses below approximately $10^{12.5} h^{-1} M_{\odot}$ at local redshift. The analysis of the galaxy colour evolution shows a reddening towards lower redshift, although these processes seem to be more efficient in massive halos, with a strong effect on bright galaxies. The general evolution suggests that the building of galaxy population within halos is influenced by both the accretion of faint galaxies and the mass growth of the bright ones., Comment: 15 pages, 7 figures

The Fermi/GBM instrument is a vital source of detections of gamma-ray bursts and has an increasingly important role to play in understanding gravitational-wave transients. In both cases, its impact is increased by accurate positions with reliable uncertainties. We evaluate the RoboBA and BALROG algorithms for determining the position of gamma-ray bursts detected by the Fermi/GBM instrument. We construct a sample of 54 bursts with detections both by Swift/BAT and by Fermi/GBM. We then compare the positions predicted by RoboBA and BALROG with the positions measured by BAT, which we can assume to be the true position. We find that RoboBA and BALROG are similarly precise for bright bursts whose uncertainties are dominated by systematic errors, but RoboBA performs better for faint bursts whose uncertainties are dominated by statistical noise. We further find that the uncertainties in the positions predicted by RoboBA are consistent with the distribution of position errors, whereas BALROG seems to be underestimating the uncertainties by a factor of about two. Additionally, we consider the implications of these results for the follow-up of the optical afterglows of Fermi/GBM bursts. In particular, for the DDOTI wide-field imager we conclude that a single pointing is best. Our sample would allow a similar study to be carried out for other telescopes., Comment: Revision sent to MNRAS after addressing minimal referee comments

Imaging is a standard example of an inverse problem, where the task of reconstructing a ground truth from a noisy measurement is ill-posed. Recent state-of-the-art approaches for imaging use deep learning, spearheaded by unrolled and end-to-end models and trained on various image datasets. However, many such methods require the availability of ground truth data, which may be unavailable or expensive, leading to a fundamental barrier that can not be bypassed by choice of architecture. Unsupervised learning presents an alternative paradigm that bypasses this requirement, as they can be learned directly on noisy data and do not require any ground truths. A principled Bayesian approach to unsupervised learning is to maximize the marginal likelihood with respect to the given noisy measurements, which is intrinsically linked to classical variational regularization. We propose an unsupervised approach using maximum marginal likelihood estimation to train a convex neural network-based image regularization term directly on noisy measurements, improving upon previous work in both model expressiveness and dataset size. Experiments demonstrate that the proposed method produces priors that are near competitive when compared to the analogous supervised training method for various image corruption operators, maintaining significantly better generalization properties when compared to end-to-end methods. Moreover, we provide a detailed theoretical analysis of the convergence properties of our proposed algorithm.

This paper presents a novel stochastic optimisation methodology to perform empirical Bayesian inference in semi-blind image deconvolution problems. Given a blurred image and a parametric class of possible operators, the proposed optimisation approach automatically calibrates the parameters of the blur model by maximum marginal likelihood estimation, followed by (non-blind) image deconvolution by maximum-a-posteriori estimation conditionally to the estimated model parameters. In addition to the blur model, the proposed approach also automatically calibrates the noise variance as well as any regularisation parameters. The marginal likelihood of the blur, noise variance, and regularisation parameters is generally computationally intractable, as it requires calculating several integrals over the entire solution space. Our approach addresses this difficulty by using a stochastic approximation proximal gradient optimisation scheme, which iteratively solves such integrals by using a Moreau-Yosida regularised unadjusted Langevin Markov chain Monte Carlo algorithm. This optimisation strategy can be easily and efficiently applied to any model that is log-concave, and by using the same gradient and proximal operators that are required to compute the maximum-a-posteriori solution by convex optimisation. We provide convergence guarantees for the proposed optimisation scheme under realistic and easily verifiable conditions and subsequently demonstrate the effectiveness of the approach with a series of deconvolution experiments and comparisons with alternative strategies from the state of the art., Comment: 43 pages

The prediction of a nearly scale-invariant spectrum of curvature and tensor fluctuations is among the main features of cosmic inflation. The current measurements of the primordial fluctuations in the cosmic microwave background (CMB) provide tight constraints on the amplitude of the scalar and tensor spectra, and the scalar tilt. However, the precise connection between these observables and a given inflationary model, depends on the expansion history between the end of inflation and the beginning of the radiation dominated era, which corresponds to the reheating epoch. This mapping between horizon exit and reentry of fluctuations, parametrized by the number of $e$-folds $N_*$, can therefore be affected by the presence of a transient epoch of non-perturbative particle production during reheating ({\em preheating}). Using a combination of perturbative and lattice computations, we quantify the impact of preheating in a non-equilibrated dark matter sector on the CMB observables, under the assumption of a simultaneous perturbative decay of the inflaton into Standard Model particles. Combined with structure formation constraints, this allows us to impose stringent bounds on the post-inflationary reheating temperature., Comment: 27 pages, 10 figures

This paper presents a statistical forward model for a Compton imaging system, called Compton imager. This system, under development at the University of Illinois Urbana Champaign, is a variant of Compton cameras with a single type of sensors which can simultaneously act as scatterers and absorbers. This imager is convenient for imaging situations requiring a wide field of view. The proposed statistical forward model is then used to solve the inverse problem of estimating the location and energy of point-like sources from observed data. This inverse problem is formulated and solved in a Bayesian framework by using a Metropolis within Gibbs algorithm for the estimation of the location, and an expectation-maximization algorithm for the estimation of the energy. This approach leads to more accurate estimation when compared with the deterministic standard back-projection approach, with the additional benefit of uncertainty quantification in the low photon imaging setting.

Assessing performance in Natural Language Processing is becoming increasingly complex. One particular challenge is the potential for evaluation datasets to overlap with training data, either directly or indirectly, which can lead to skewed results and overestimation of model performance. As a consequence, human evaluation is gaining increasing interest as a means to assess the performance and reliability of models. One such method is the red teaming approach, which aims to generate edge cases where a model will produce critical errors. While this methodology is becoming standard practice for generative AI, its application to the realm of conditional AI remains largely unexplored. This paper presents the first study on human-based red teaming for Machine Translation (MT), marking a significant step towards understanding and improving the performance of translation models. We delve into both human-based red teaming and a study on automation, reporting lessons learned and providing recommendations for both translation models and red teaming drills. This pioneering work opens up new avenues for research and development in the field of MT., Comment: arXiv admin note: substantial text overlap with arXiv:2312.05187

Next-generation radio interferometers like the Square Kilometer Array have the potential to unlock scientific discoveries thanks to their unprecedented angular resolution and sensitivity. One key to unlocking their potential resides in handling the deluge and complexity of incoming data. This challenge requires building radio interferometric imaging methods that can cope with the massive data sizes and provide high-quality image reconstructions with uncertainty quantification (UQ). This work proposes a method coined QuantifAI to address UQ in radio-interferometric imaging with data-driven (learned) priors for high-dimensional settings. Our model, rooted in the Bayesian framework, uses a physically motivated model for the likelihood. The model exploits a data-driven convex prior, which can encode complex information learned implicitly from simulations and guarantee the log-concavity of the posterior. We leverage probability concentration phenomena of high-dimensional log-concave posteriors that let us obtain information about the posterior, avoiding MCMC sampling techniques. We rely on convex optimisation methods to compute the MAP estimation, which is known to be faster and better scale with dimension than MCMC sampling strategies. Our method allows us to compute local credible intervals, i.e., Bayesian error bars, and perform hypothesis testing of structure on the reconstructed image. In addition, we propose a novel blazing-fast method to compute pixel-wise uncertainties at different scales. We demonstrate our method by reconstructing radio-interferometric images in a simulated setting and carrying out fast and scalable UQ, which we validate with MCMC sampling. Our method shows an improved image quality and more meaningful uncertainties than the benchmark method based on a sparsity-promoting prior. QuantifAI's source code: https://github.com/astro-informatics/QuantifAI., Comment: 30 pages, 14 figures, 10 tables, code available at https://github.com/astro-informatics/QuantifAI

In the last few years there is an unprecedented progress in the increase of the power conversion efficiency of perovskite solar cells. Evidently, further advances of the efficiency of these devices will depend on the constraints imposed by the optical and electronic properties of their constituents. Quite apparently that during the manufacturing process of a solar cell, there is an inevitable variation in the thicknesses of various functional layers, which affects the optoelectronic characteristics of the final sample. In this work a possible strategy of the analysis of the solar cell performance is suggested, based on statistically averaging procedure of experimental data. We present a case study, in which the optoelectronic properties of the meso-structured perovskite solar cell (with a mesoporous TiO$_2$ layer) are analysed within the method providing a deeper understanding of the device operation. This method enables an assessment of the overall quality of the device, pointing pathways towards the maximum efficiency design of a perovskite solar cell by material properties tuning., Comment: 13 pages, 10 figures

The standard paradigm for the formation of the Universe suggests that large structures are formed from hierarchical clustering by the continuous accretion of less massive galaxy systems through filaments. In this context, filamentary structures play an important role in the properties and evolution of galaxies by connecting high-density regions, such as nodes, and being surrounded by low-density regions, such as cosmic voids. The availability of the filament and point critic catalogues extracted by \textsc{DisPerSE} from the \textsc{Illustris} TNG300-1 hydrodynamic simulation allows a detailed analysis of these structures. The halo occupation distribution (HOD) is a powerful tool for linking galaxies and dark matter halos, allowing constrained models of galaxy formation and evolution. In this work we combine the advantage of halo occupancy with information from the filament network to analyse the HOD in filaments and nodes. In our study, we distinguish the inner regions of cosmic filaments and nodes from their surroundings. The results show that the filamentary structures have a similar trend to the total galaxy sample covering a wide range of densities. In the case of the nodes sample, an excess of faint and blue galaxies is found for the low-mass nodes suggesting that these structures are not virialised and that galaxies may be continuously falling through the filaments. Instead, the higher-mass halos could be in a more advanced stage of evolution showing features of virialised structures.

Context. The location of the Solar System constrains the detection of extragalactic sources beyond the Milky Way(MW) plane. The optical observations are hampered in the so--called Zone of Avoidance (ZOA) where stellar crowding and Galactic absorption are severe. Observations at longer wavelengths are needed to discover new background galaxies and complete the census in the ZOA. Aims. The goal of this work is to identify galaxies behind the MW Bulge using near-infrared (NIR) data from the VISTA Variables in V\'ia L\'actea (VVV) survey. Methods. To this end we made use of different VISTA Science Archive (VSA) tools in order to extract relevant information from more than 32 billion catalogued sources in the VVV Bulge region. We find that initial photometric restriction on sources from VSA \texttt{vvvSource} table combined with restrictions on star--galaxy separation parameters obtained from Source Extractor is a successful strategy to achieve acceptable levels of contamination (60\%) and a high completeness (75\%) in the construction of a galaxy target sample. To decontaminate the initial target sample from Galactic sources our methodology also incorporates the visual inspection of false colour RGB images, a crucial quality control which was carried out following a specifically defined procedure. Results. Under this methodology we find 14480 galaxy candidates in the VVV Bulge region making this sample the largest catalogue to date in the ZOA. Moreover these new sources provide a \textbf{fresh} picture of the Universe hidden behind the curtain of stars, dust and gas in the unexplored MW Bulge region. Conclusions. The results from this work further demonstrate the potential of the VVV/VVVX survey to find and study a large number of galaxies and extragalactic structures obscured by the MW, enlightening our knowledge of the Universe in this challenging and impressive region of the sky.

Scientific imaging problems are often severely ill-posed, and hence have significant intrinsic uncertainty. Accurately quantifying the uncertainty in the solutions to such problems is therefore critical for the rigorous interpretation of experimental results as well as for reliably using the reconstructed images as scientific evidence. Unfortunately, existing imaging methods are unable to quantify the uncertainty in the reconstructed images in a manner that is robust to experiment replications. This paper presents a new uncertainty quantification methodology based on an equivariant formulation of the parametric bootstrap algorithm that leverages symmetries and invariance properties commonly encountered in imaging problems. Additionally, the proposed methodology is general and can be easily applied with any image reconstruction technique, including unsupervised training strategies that can be trained from observed data alone, thus enabling uncertainty quantification in situations where there is no ground truth data available. We demonstrate the proposed approach with a series of numerical experiments and through comparisons with alternative uncertainty quantification strategies from the state-of-the-art, such as Bayesian strategies involving score-based diffusion models and Langevin samplers. In all our experiments, the proposed method delivers remarkably accurate high-dimensional confidence regions and outperforms the competing approaches in terms of estimation accuracy, uncertainty quantification accuracy, and computing time.