Instituto de Física de Cantabria

In this paper we address the use of Neural Networks (NNs) for the assessment of the quality and hence safety of several Random Number Generators (RNGs), focusing both on the vulnerability of classical Pseudo Random Number Generators (PRNGs), such as Linear Congruential Generators (LCGs) and the RC4 algorithm, and extending our analysis to non-conventional data sources, such as Quantum Random Number Generators (QRNGs) based on Vertical-Cavity Surface-Emitting Laser (VCSEL). Among the results found, we have classified the generators based on the capability of the NN to distinguish between the RNG and a Golden Standard RNG (GSRNG). We show that sequences from simple PRNGs like LCGs and RC4 can be distinguished from the GSRNG. We also show that sequences from LCG on elliptic curves and VCSEL-based QRNG can not be distinguished from the GSRNG even with the biggest long-short term memory or convolutional neural networks (CNNs) that we have considered. We underline the fundamental role of design decisions in enhancing the safety of RNGs. The influence of network architecture design and associated hyper-parameters variations was also explored. We show that longer sequence lengths and CNNs are more effective for discriminating RNGs against the GSRNG. Moreover, in the prediction domain, the proposed model is able to deftly distinguish between the raw data of our QRNG and data from the GSRNG exhibiting a cross-entropy error of 0.52 on the test data-set used. All these findings reveal the potential of NNs to enhance the security of RNGs, while highlighting the robustness of certain QRNGs, in particular the VCSEL-based variants, for high-quality random number generation applications.

The contributions in this special theme collection, in honor to Prof. P. Villarreal, cover a broad variety of computational methodologies and experimental techniques, containing studies on gas phase, clusters and condensed phase systems. image

In cybersecurity, live production data for predictive analysis pose a significant challenge due to the inherently secure nature of the domain. Although there are publicly available, synthesized, and artificially generated datasets, authentic scenarios are rarely encountered. For anomaly-based detection, the dynamic definition of thresholds has gained importance and attention in detecting abnormalities and preventing malicious activities. Unlike conventional threshold-based methods, deep learning data modeling provides a more nuanced perspective on network monitoring. This enables security systems to continually refine and adapt to the evolving situation in streaming data online, which is also our goal. Furthermore, our work in this paper contributes significantly to AIOps research, particularly through the deployment of our intelligent module that cooperates within a monitoring system in production. Our work addresses a crucial gap in the security research landscape toward more practical and effective secure strategies.

The advancement of computational resources has allowed researchers to run convection-permitting regional climate model (CPRCM) simulations. A pioneering effort promoting a multimodel ensemble of such simulations is the CORDEX Flagship Pilot Studies (FPS) on “Convective Phenomena over Europe and the Mediterranean” over an extended Alps region. In this study, the Distribution Added Value metric is used to determine the improvement of the representation of all available FPS hindcast simulations for the daily mean near-surface wind speed. The analysis is performed on normalized empirical probability distributions and considers station observation data as the reference. The use of a normalized metric allows for spatial comparison among the different regions (coast and inland), altitudes and seasons. This approach permits a direct assessment of the added value between the CPRCM simulations against their global driving reanalysis (ERA-Interim) and respective coarser resolution regional model counterparts. In general, the results show that CPRCMs add value to their global driving reanalysis or forcing regional model, due to better-resolved topography or through better representation of ocean-land contrasts. However, the nature and magnitude of the improvement in the wind speed representation vary depending on the model, the season, the altitude, or the region. Among seasons, the improvement is usually larger in summer than winter. CPRCMs generally display gains at low and medium-range altitudes. In addition, despite some shortcomings in comparison to ERA-Interim, which can be attributed to the assimilation of wind observations on the coast, the CPRCMs outperform the coarser regional climate models, both along the coast and inland.

Sea surface temperature (SST) and sea surface air temperature (SSAT) are commonly used as proxies for investigating the impact of climate change on oceans. These variables have been warming since pre-industrial times and are expected to continue to warm in the future under all Shared Socioeconomic Pathways (SSPs). However, they are warming in a spatially heterogeneous way, even with some cooling spots. In this work, we provide a general overview on the regional scaling of SST and SSAT with global warming, based on a 26-member CMIP6 ensemble. We utilize the global warming level (GWL) as a climate change dimension to analyze scaling patterns between sea temperature anomalies and the corresponding GWLs during the 21st century. This analysis is conducted globally, regionally, and on grid-point basis. The results show that SST and SSAT scale linearly with GWL at global scale, with scaling factors $$\beta $$ β = 0.71 ± 0.001 K/K and $$\beta $$ β = 0.86 ± 0.001 K/K, respectively. These results are robust, showing only minor differences between seasons, SSPs, and horizontal model resolutions. However, large differences emerge at regional scale, and the scaling of the two temperatures are strongly influenced by sea-ice. The lowest values are obtained for the Southern Ocean region, $$\beta $$ β = 0.54 ± 0.005 K/K, projecting that the mean SST will increase only half as fast as the global mean temperature. These results provide valuable insight for refining the ocean IPCC reference regions, considering spatial homogeneity in terms of the regional response to global warming. A refinement of six ocean reference regions has been proposed.

Pierce’s disease (PD) is a vector-borne disease caused by the bacteria Xylella fastidiosa, which affects grapevines in the Americas. Currently, vineyards in continental Europe, the world’s largest producer of quality wine, have not yet been affected by PD. However, climate change may alter this situation. Here we incorporate the latest regional climate change projections into a climate-driven epidemiological model to assess the risk of PD epidemics in Europe for different levels of global warming. We found a significant increase in risk above +2∘C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+\,2\,^{\circ }\hbox {C}$$\end{document} in the main wine-producing regions of France, Italy and Portugal, in addition to a critical tipping point above +3∘C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+\,3\,^{\circ }\hbox {C}$$\end{document} for the possible spread of PD beyond the Mediterranean. The model identifies decreasing risk trends in Spain, as well as contrasting patterns across the continent with different velocities of risk change and epidemic growth rates. Although there is some uncertainty in model projections over time, spatial patterns of risk are consistent across different climate models. Our study provides a comprehensive analysis of the future of PD at multiple spatial scales (country, Protected Designation of Origin and vineyard), revealing where, why and when PD could become a new threat to the European wine industry.

The X-ray Integral Field Unit (X-IFU) instrument on the future ESA mission Athena X-ray Observatory is a cryogenic micro-calorimeter array of Transition Edge Sensor (TES) detectors designed to provide spatially-resolved high-resolution spectroscopy. The onboard reconstruction software provides energy, spatial location and arrival time of incoming X-ray photons hitting the detector. A new processing algorithm based on a truncation of the classical optimal filter and called 0-padding, has been recently proposed aiming to reduce the computational cost without compromising energy resolution. Initial tests with simple synthetic data displayed promising results. This study explores the slightly better performance of the 0-padding filter and assess its final application to real data. The goal is to examine the larger sensitivity to instrumental conditions that was previously observed during the analysis of the simulations. This 0-padding technique is thoroughly tested using more realistic simulations and real data acquired from NASA and NIST laboratories employing X-IFU-like TES detectors. Different fitting methods are applied to the data, and a comparative analysis is performed to assess the energy resolution values obtained from these fittings. The 0-padding filter achieves energy resolutions as good as those obtained with standard filters, even with those of larger lengths, across different line complexes and instrumental conditions. This method proves to be useful for energy reconstruction of X-ray photons detected by the TES detectors provided proper corrections for baseline drift and jitter effects are applied. The finding is highly promising especially for onboard processing, offering efficiency in computational resources and facilitating the analysis of sources with higher count rates at high resolution.

A bstract We combine perturbation theory with analytic and numerical bootstrap techniques to study the critical point of the long-range Ising (LRI) model in two and three dimensions. This model interpolates between short-range Ising (SRI) and mean-field behaviour. We use the Lorentzian inversion formula to compute infinitely many three-loop corrections in the two-dimensional LRI near the mean-field end. We further exploit the exact OPE relations that follow from bulk locality of the LRI to compute infinitely many two-loop corrections near the mean-field end, as well as some one-loop corrections near SRI. By including such exact OPE relations in the crossing equations for LRI we set up a very constrained bootstrap problem, which we solve numerically using SDPB. We find a family of sharp kinks for two- and three-dimensional theories which compare favourably to perturbative predictions, as well as some Monte Carlo simulations for the two-dimensional LRI.

668 Background: Immune checkpoint inhibitors (ICIs) have proven to be an effective therapy for locally advanced (La) and metastatic (mUC) urothelial carcinoma. However, response rates are typically modest, and patient (pt) selection remains a challenge, especially in the era of ICIs-based combos. The current study analyzes the differential RNA expression as a predictor of ICI response or resistance in La/mUC. Methods: Clinical information and formalin-fixed paraffin embedded (FFPE) samples from pts with La/mUC treated with single agent ICIs [2014 -2021] were obtained and RNA sequencing (RNA-seq) analysis performed. According to response, pts were assigned to one of these three groups: Primary resistant [PR] (progressive disease (PD) as the best response); Secondary resistant [SR] (initial response followed by PD); and Long responders [LR] (stable disease/partial or complete response maintained > 16 months). Results: Clinical data and FFPE samples from 46 patients were available. Nineteen, twelve and fifteen pts respectively were in the PR, SR and LR groups. Clinical characteristics were well-balanced among groups, except for higher percentage of liver metastasis in PR and SR pts. The median overall survival was 11.2 and 18.2 months for the PR and the SR groups and has not yet been reached for the LR cohort, where 80% of the pts remain alive, with 50% achieving complete response. RNA-seq analysis unveiled distinct patterns of differential gene expression in the resistance groups compared to the LR. Notably, an upregulation of the epithelial-mesenchymal transition pathway was observed, with interleukin 2 emerging as a potential surrogate marker of this activity. Additionally, an overactivation of the KRAS pathway was evident, irrespective of its mutational status. Furthermore, in a comparison between the two resistance groups, the SR group displayed increased activation of DNA damage control mechanisms. Conclusions: Our RNA-seq analysis could provide relevant information and holds promise as a valuable tool for predicting responses to ICIs in La-mUC. Nonetheless, further validation on a larger cohort of patients is required.

Oscura is a planned light-dark matter search experiment using Skipper-CCDs with a total active mass of 10 kg. As part of the detector development, the collaboration plans to build the Oscura Integration Test (OIT), an engineering test with 10% of the total mass. Here we discuss the early science opportunities with the OIT to search for millicharged particles (mCPs) using the NuMI beam at Fermilab. mCPs would be produced at low energies through photon-mediated processes from decays of scalar, pseudoscalar, and vector mesons, or direct Drell-Yan productions. Estimates show that the OIT would be a world-leading probe for mCPs in the ∼MeV mass range.

Introduction Regional gray matter (GM) alterations have been reported in early-onset psychosis (EOP, onset before age 18), but previous studies have yielded conflicting results, likely due to small sample sizes and the different brain regions examined. In this study, we conducted a whole brain voxel-based morphometry (VBM) analysis in a large sample of individuals with EOP, using the newly developed ENIGMA-VBM tool. Methods 15 independent cohorts from the ENIGMA-EOP working group participated in the study. The overall sample comprised T1-weighted MRI data from 482 individuals with EOP and 469 healthy controls. Each site performed the VBM analysis locally using the standardized ENIGMA-VBM tool. Statistical parametric T-maps were generated from each cohort and meta-analyzed to reveal voxel-wise differences between EOP and healthy controls as well as the individual-based association between GM volume and age of onset, chlorpromazine (CPZ) equivalent dose, and other clinical variables. Results Compared with healthy controls, individuals with EOP showed widespread lower GM volume encompassing most of the cortex, with the most marked effect in the left median cingulate (Hedges’ g = 0.55, p = 0.001 corrected), as well as small clusters of lower white matter (WM), whereas no regional GM or WM volumes were higher in EOP. Lower GM volume in the cerebellum, thalamus and left inferior parietal gyrus was associated with older age of onset. Deficits in GM in the left inferior frontal gyrus, right insula, right precentral gyrus and right superior frontal gyrus were also associated with higher CPZ equivalent doses. Conclusion EOP is associated with widespread reductions in cortical GM volume, while WM is affected to a smaller extent. GM volume alterations are associated with age of onset and CPZ equivalent dose but these effects are small compared to case-control differences. Mapping anatomical abnormalities in EOP may lead to a better understanding of the role of psychosis in brain development during childhood and adolescence.

We present an improved study of the relation between supermassive black hole growth and their host galaxy properties in the local Universe (z < 0.33). To this end, we build an extensive sample combining spectroscopic measurements of star-formation rate (SFR) and stellar mass from Sloan Digital Sky Survey, with specific Black Hole accretion rate (sBHAR, |$\lambda _{\mathrm{sBHAR}} \propto L_{\rm X}/\mathcal {M}_{\ast }$|⁠) derived from the XMM-Newton Serendipitous Source Catalogue (3XMM-DR8) and the Chandra Source Catalogue (CSC 2.0). We find that the sBHAR probability distribution for both star-forming and quiescent galaxies has a power-law shape peaking at log λsBHAR ∼ −3.5 and declining toward lower sBHAR in all stellar mass ranges. This finding confirms the decrease of AGN activity in the local Universe compared to higher redshifts. We observe a significant correlation between log λsBHAR and log SFR in almost all stellar mass ranges, but the relation is shallower compared to higher redshifts, indicating a reduced availability of accreting material in the local Universe. At the same time, the BHAR-to-SFR ratio for star-forming galaxies strongly correlates with stellar mass, supporting the scenario where both AGN activity and stellar formation primarily depend on the stellar mass via fuelling by a common gas reservoir. Conversely, this ratio remains constant for quiescent galaxies, possibly indicating the existence of the different physical mechanisms responsible for AGN fuelling or different accretion mode in quiescent galaxies.

The accurate prediction of the Fire Weather Index (FWI), a multivariate climate index for wildfire risk characterization, is crucial for both wildfire management and climate-resilient planning. Moreover, consistent multisite fire danger predictions are key for targeted allocation of resources and early intervention in high-risk areas, as well as for “megafire” risk detection. In this regard, Convolutional Neural Networks (CNNs) are known to capture complex spatial patterns in climate data. This study compares different CNN architectures and traditional Statistical Downscaling (SD) methods (regression and analogs) for predicting daily FWI across diverse locations in Spain, considering marginal, distributional and spatial coherence measures for validation. Overall, the CNN-Multi-Site-Multi-Gaussian configuration, which explicitly accounts for the inter-site variability in the output layer structure, showed a superior performance. These insights provide a methodological guidance for the successful application of CNNs in the context wildfire risk assessment, enhancing wildfire response strategies and climate adaptation planning.

Plain Language Summary Due to limitations in the computational resources available, General Circulation Models (GCMs) are often used to simulate the climate system over coarse resolution grids. This hampers the applicability of GCM products in the regional‐to‐local scale, highly demanded by different socio‐economic sectors. Statistical downscaling aims to solve this problem by generating high‐resolution climate fields. Recently, machine learning techniques—particularly deep learning (DL) models—have shown promising results in this task. These models are first trained in a historical period through observational data sets, and then applied to the GCM outputs of plausible future scenarios, thus generating high‐resolution climate change products. To assess the performance of these methods, a number of evaluation metrics have been proposed considering both the skill to reproduce present climate conditions and the ability to generalize changing conditions. Here, we illustrate the possibilities of eXplainable Artificial Intelligence (XAI) techniques to expand the evaluation framework for deep downscaling methods, introducing new XAI‐derived diagnostics to unravel their internal behavior. The results show the usefulness of incorporating XAI techniques into statistical downscaling evaluation frameworks, especially when working with large regions and/or under climate change conditions.

Pancreatic ductal adenocarcinoma (PDAC) has a very poor prognosis because of its high propensity to metastasize and its immunosuppressive microenvironment. Using a panel of pancreatic cancer cell lines, three-dimensional (3D) invasion systems, microarray gene signatures, microfluidic devices, mouse models, and intravital imaging, we demonstrate that ROCK–Myosin II activity in PDAC cells supports a transcriptional program conferring amoeboid invasive and immunosuppressive traits and in vivo metastatic abilities. Moreover, we find that immune checkpoint CD73 is highly expressed in amoeboid PDAC cells and drives their invasive, metastatic, and immunomodulatory traits. Mechanistically, CD73 activates RhoA–ROCK–Myosin II downstream of PI3K. Tissue microarrays of human PDAC biopsies combined with bioinformatic analysis reveal that rounded-amoeboid invasive cells with high CD73–ROCK–Myosin II activity and their immunosuppressive microenvironment confer poor prognosis to patients. We propose targeting amoeboid PDAC cells as a therapeutic strategy.

Sea surface temperature (SST) and sea surface air temperature (SSAT) are commonly used as proxies for investigating the impact of climate change on oceans. These variables have been warming since pre-industrial times and are expected to continue to warm in the future under all Shared Socioeconomic Pathways (SSPs). However, they are warming at different rates in a spatially heterogeneous way, even with some cooling spots. In this work, we provide a general overview on the regional scaling of SST and SSAT with global warming, based on a 26-member CMIP6 ensemble. We use global warming level (GWL) as a climate change dimension, and analyze the slope of the linear fit (β) between decadal sea temperature anomalies and the corresponding GWLs during the 21st century. This analysis is done globally, regionally, and also grid-point by grid-point. The results show that SST and SSAT scale linearly with GWL at global scale, with scaling factors 0.71±0.001 K/K and 0.86±0.001 K/K, respectively. These results are quite robust, with small differences between seasons, SSPs and horizontal model resolutions. However, large differences appear at regional scale, and the scaling of the two temperatures are strongly affected by sea-ice. The lowest values are obtained for the Southern Ocean region, β = 0.54±0.005 K/K, projecting the mean SST to increase half as fast as the global mean temperature. These results provide relevant information for a refinement of ocean reference regions, taking into account the spatial homogeneity in terms of regional response to global warming.

We address the blind detection of extragalactic point sources on maps of the temperature anisotropies of the cosmic microwave background as an image segmentation problem. We project the sky into two-dimensional patches and design a convolutional neural network (CNN) to identify the sources’ position through supervised training with binary labels. To improve the CNN performance, we divide the sky into three regions of progressively increasing Galactic foreground intensity and independently train specialized models for them. With this strategy, we achieve promising completeness levels even in the center of the Galactic plane.