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Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set on assumptions that are not supported by data in high volatility markets. Hence, quantitative researchers are looking for alternative models to tackle this problem. Concretely, portfolio management is a problem that has been successfully addressed recently by Deep Reinforcement Learning (DRL) approaches. In particular, DRL algorithms train an agent by estimating the distribution of the expected reward of every action performed by an agent given any financial state in a simulator. However, these methods rely on Deep Neural Networks model to represent such a distribution, that although they are universal approximator models, they cannot explain its behaviour, given by a set of parameters that are not interpretable. Critically, financial investors policies require predictions to be interpretable, so DRL agents are not suited to follow a particular policy or explain their actions. In this work, we developed a novel Explainable Deep Reinforcement Learning (XDRL) approach for portfolio management, integrating the Proximal Policy Optimization (PPO) with the model agnostic explainable techniques of feature importance, SHAP and LIME to enhance transparency in prediction time. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy or to assess the risk of following the agent suggestions. To the best of our knowledge, our proposed approach is the first explainable post hoc portfolio management financial policy of a DRL agent. We empirically illustrate our methodology by successfully identifying key features influencing investment decisions, which demonstrate the ability to explain the agent actions in prediction time.

In the last decade, Artificial Intelligence (AI) models have rapidly integrated into production pipelines propelled by their excellent modeling performance. However, the development of these models has not been matched by advancements in algorithms ensuring their safety, failing to guarantee robust behavior against Out-of-Distribution (OoD) inputs outside their learning domain. Furthermore, there is a growing concern with the sustainability of AI models and their required energy consumption in both training and inference phases. To mitigate these issues, this work explores the use of the Forward-Forward Algorithm (FFA), a biologically plausible alternative to Backpropagation, adapted to the spiking domain to enhance the overall energy efficiency of the model. By capitalizing on the highly expressive topology emerging from the latent space of models trained with FFA, we develop a novel FF-SCP algorithm for OoD Detection. Our approach measures the likelihood of a sample belonging to the in-distribution (ID) data by using the distance from the latent representation of samples to class-representative manifolds. Additionally, to provide deeper insights into our OoD pipeline, we propose a gradient-free attribution technique that highlights the features of a sample pushing it away from the distribution of any class. Multiple experiments using our spiking FFA adaptation demonstrate that the achieved accuracy levels are comparable to those seen in analog networks trained via back-propagation. Furthermore, OoD detection experiments on multiple datasets prove that FF-SCP outperforms avant-garde OoD detectors within the spiking domain in terms of several metrics used in this area. We also present a qualitative analysis of our explainability technique, exposing the precision by which the method detects OoD features, such as embedded artifacts or missing regions.

Cosmological neutrino mass bounds are becoming increasingly stringent. The latest limit within $\Lambda$CDM from Planck 2018+ACT lensing+DESI is $\sum m_\nu < 0.072\,{\rm eV}$ at 95% CL, very close to the minimum possible sum of neutrino masses ($\sum m_\nu > 0.06\,{\rm eV}$), hinting at vanishing or even ''negative'' cosmological neutrino masses. In this context, it is urgent to carefully evaluate the origin of these cosmological constraints. In this paper, we investigate the robustness of these results in three ways: i) we check the role of potential anomalies in Planck CMB and DESI BAO data; ii) we compare the results for frequentist and Bayesian techniques, as very close to physical boundaries subtleties in the derivation and interpretation of constraints can arise; iii) we investigate how deviations from $\Lambda$CDM, potentially alleviating these anomalies, can alter the constraints. From a profile likelihood analysis, we derive constraints in agreement at the $\sim 10\%$ level with Bayesian posteriors. We find that the weak preference for negative neutrino masses is mostly present for Planck 18 data, affected by the well-known `lensing anomaly'. It disappears when the new Planck 2020 HiLLiPoP is used, leading to significantly weaker constraints. Additionally, the pull towards negative masses in DESI data stems from the $z=0.7$ bin, which is in $\sim 3\sigma$ tension with Planck expectations. Without these outliers, and in combination with HiLLiPoP, the bound relaxes to $\sum m_\nu < 0.11\,{\rm eV}$ at 95% CL. The recent preference for dynamical dark energy alleviates this tension and further weakens the bound. As we are at the dawn of a neutrino mass discovery from cosmology, it will be very exciting to see if this trend is confirmed by future data., Comment: 17 pages + Appendices. Comments welcome

We show that the Christensen-Sinclair factorization theorem, when the underlying Hilbert spaces are finite dimensional, is an instance of strong duality of semidefinite programming. This gives an elementary proof of the result and also provides an efficient algorithm to compute the Christensen-Sinclair factorization., Comment: 13 pages

We present multiwavelength polarization measurements of the luminous blazar Mrk~501 over a 14-month period. The 2--8 keV X-ray polarization was measured with the Imaging X-ray Polarimetry Explorer (IXPE) with six 100-ks observations spanning from 2022 March to 2023 April. Each IXPE observation was accompanied by simultaneous X-ray data from NuSTAR, Swift/XRT, and/or XMM-Newton. Complementary optical-infrared polarization measurements were also available in the B, V, R, I, and J bands, as were radio polarization measurements from 4.85 GHz to 225.5 GHz. Among the first five IXPE observations, we did not find significant variability in the X-ray polarization degree and angle with IXPE. However, the most recent sixth observation found an elevated polarization degree at $>3\sigma$ above the average of the other five observations. The optical and radio measurements show no apparent correlations with the X-ray polarization properties. Throughout the six IXPE observations, the X-ray polarization degree remained higher than, or similar to, the R-band optical polarization degree, which remained higher than the radio value. This is consistent with the energy-stratified shock scenario proposed to explain the first two IXPE observations, in which the polarized X-ray, optical, and radio emission arises from different regions., Comment: Accepted for publication in ApJ

Nonlinear dynamics play an important role in the analysis of signals. A popular, readily interpretable nonlinear measure is Permutation Entropy. It has recently been extended for the analysis of graph signals, thus providing a framework for non-linear analysis of data sampled on irregular domains. Here, we introduce a continuous version of Permutation Entropy, extend it to the graph domain, and develop a ordinal activation function akin to the one of neural networks. This is a step towards Ordinal Deep Learning, a potentially effective and very recently posited concept. We also formally extend ordinal contrasts to the graph domain. Continuous versions of ordinal contrasts of length 3 are also introduced and their advantage is shown in experiments. We also integrate specific contrasts for the analysis of images and show that it generalizes well to the graph domain allowing a representation of images, represented as graph signals, in a plane similar to the entropy-complexity one. Applications to synthetic data, including fractal patterns and popular non-linear maps, and real-life MRI data show the validity of these novel extensions and potential benefits over the state of the art. By extending very recent concepts related to permutation entropy to the graph domain, we expect to accelerate the development of more graph-based entropy methods to enable nonlinear analysis of a broader kind of data and establishing relationships with emerging ideas in data science.

Context: There are currently three pulsars firmly detected by imaging atmospheric Cherenkov telescopes (IACTs), two of them reaching TeV energies, challenging models of very-high-energy (VHE) emission in pulsars. More precise observations are needed to better characterize pulsar emission at these energies. The LST-1 is the prototype of the Large-Sized Telescope, that will be part of the Cherenkov Telescope Array Observatory (CTAO). Its improved performance over previous IACTs makes it well suited for studying pulsars. Aims: To study the Crab pulsar emission with the LST-1, improving and complementing the results from other telescopes. These observations can also be used to characterize the potential of the LST-1 to study other pulsars and detect new ones. Methods: We analyzed a total of $\sim$103 hours of gamma-ray observations of the Crab pulsar conducted with the LST-1 in the period from September 2020 to January 2023. The observations were carried out at zenith angles less than 50 degrees. A new analysis of the Fermi-LAT data was also performed, including $\sim$14 years of observations. Results: The Crab pulsar phaseogram, long-term light-curve, and phase-resolved spectra are reconstructed with the LST-1 from 20 GeV to 450 GeV for P1 and up to 700 GeV for P2. The pulsed emission is detected with a significance of 15.2$\sigma$. The two characteristic emission peaks of the Crab pulsar are clearly detected (>10$\sigma$), as well as the so-called bridge emission (5.7$\sigma$). We find that both peaks are well described by power laws, with spectral indices of $\sim$3.44 and $\sim$3.03 respectively. The joint analysis of Fermi-LAT and LST-1 data shows a good agreement between both instruments in the overlapping energy range. The detailed results obtained in the first observations of the Crab pulsar with LST-1 show the potential that CTAO will have to study this type of sources., Comment: Accepted by A&A

In unsupervised domain adaptation (UDA), where models are trained on source data (e.g., synthetic) and adapted to target data (e.g., real-world) without target annotations, addressing the challenge of significant class imbalance remains an open issue. Despite considerable progress in bridging the domain gap, existing methods often experience performance degradation when confronted with highly imbalanced dense prediction visual tasks like semantic and panoptic segmentation. This discrepancy becomes especially pronounced due to the lack of equivalent priors between the source and target domains, turning class imbalanced techniques used for other areas (e.g., image classification) ineffective in UDA scenarios. This paper proposes a class-imbalance mitigation strategy that incorporates class-weights into the UDA learning losses, but with the novelty of estimating these weights dynamically through the loss gradient, defining a Gradient-based class weighting (GBW) learning. GBW naturally increases the contribution of classes whose learning is hindered by large-represented classes, and has the advantage of being able to automatically and quickly adapt to the iteration training outcomes, avoiding explicitly curricular learning patterns common in loss-weighing strategies. Extensive experimentation validates the effectiveness of GBW across architectures (convolutional and transformer), UDA strategies (adversarial, self-training and entropy minimization), tasks (semantic and panoptic segmentation), and datasets (GTA and Synthia). Analysing the source of advantage, GBW consistently increases the recall of low represented classes.

Advances in neural computation have predominantly relied on the gradient backpropagation algorithm (BP). However, the recent shift towards non-stationary data modeling has highlighted the limitations of this heuristic, exposing that its adaptation capabilities are far from those seen in biological brains. Unlike BP, where weight updates are computed through a reverse error propagation path, Hebbian learning dynamics provide synaptic updates using only information within the layer itself. This has spurred interest in biologically plausible learning algorithms, hypothesized to overcome BP's shortcomings. In this context, Hinton recently introduced the Forward-Forward Algorithm (FFA), which employs local learning rules for each layer and has empirically proven its efficacy in multiple data modeling tasks. In this work we argue that when employing a squared Euclidean norm as a goodness function driving the local learning, the resulting FFA is equivalent to a neo-Hebbian Learning Rule. To verify this result, we compare the training behavior of FFA in analog networks with its Hebbian adaptation in spiking neural networks. Our experiments demonstrate that both versions of FFA produce similar accuracy and latent distributions. The findings herein reported provide empirical evidence linking biological learning rules with currently used training algorithms, thus paving the way towards extrapolating the positive outcomes from FFA to Hebbian learning rules. Simultaneously, our results imply that analog networks trained under FFA could be directly applied to neuromorphic computing, leading to reduced energy usage and increased computational speed.

Variability can be the pathway to understanding the physical processes in astrophysical jets, however, the high-cadence observations required to test particle acceleration models are still missing. Here we report on the first attempt to produce continuous, >24 hour polarization light curves of blazars using telescopes distributed across the globe and the rotation of the Earth to avoid the rising Sun. Our campaign involved 16 telescopes in Asia, Europe, and North America. We observed BL Lacertae and CGRaBS J0211+1051 for a combined 685 telescope hours. We find large variations in the polarization degree and angle for both sources in sub-hour timescales as well as a ~180 degree rotation of the polarization angle in CGRaBS J0211+1051 in less than two days. We compared our high-cadence observations to Particle-In-Cell magnetic reconnection and turbulent plasma simulations. We find that although the state of the art simulation frameworks can produce a large fraction of the polarization properties, they do not account for the entirety of the observed polarization behavior in blazar jets., Comment: 20 pages, 15 figures, 2 tables, accepted for publication in A&A. The data used in the paper are available here: https://doi.org/10.7910/DVN/IETSXS

We recently demonstrated a continuous Bose-Einstein condensate of strontium atoms. We could turn this into a continuous-wave atom laser if an efficient outcoupling mechanism is found. Here we show a coherent three-photon excitation of the clock transition in a strontium BEC with contrast of 44.6(3.5)%. We follow it up with a demonstration of three-photon STIRAP-like transfer. Our work constitutes an essential step towards the outcoupling of a continuous atom laser beam and provides a robust excitation mechanism for quantum simulation., Comment: 6 pages, 5 figures

The Lorentz Invariance Violation (LIV), a proposed consequence of certain quantum gravity (QG) scenarios, could instigate an energy-dependent group velocity for ultra-relativistic particles. This energy dependence, although suppressed by the massive QG energy scale $E_\mathrm{QG}$, expected to be on the level of the Planck energy $1.22 \times 10^{19}$ GeV, is potentially detectable in astrophysical observations. In this scenario, the cosmological distances traversed by photons act as an amplifier for this effect. By leveraging the observation of a remarkable flare from the blazar Mrk\,421, recorded at energies above 100 GeV by the MAGIC telescopes on the night of April 25 to 26, 2014, we look for time delays scaling linearly and quadratically with the photon energies. Using for the first time in LIV studies a binned-likelihood approach we set constraints on the QG energy scale. For the linear scenario, we set $95\%$ lower limits $E_\mathrm{QG}>2.7\times10^{17}$ GeV for the subluminal case and $E_\mathrm{QG}> 3.6 \times10^{17}$ GeV for the superluminal case. For the quadratic scenario, the $95\%$ lower limits for the subluminal and superluminal cases are $E_\mathrm{QG}>2.6 \times10^{10}$ GeV and $E_\mathrm{QG}>2.5\times10^{10}$ GeV, respectively.

Hydrogen is a promising alternative to fossil fuel, however storing it efficiently poses challenges. One promising solution is to adsorb hydrogen on solid materials demonstrating quasi-molecular bonding with hydrogen. The hydrogen adsorption energy can be tuned by changing the morphology or stoichiometry of bimetallic nanoparticles. Here we used complementary techniques to unveil the chemical compositional and morphological transformation undergone by PdxNi100-x nanoparticles during H2 adsorption. Our findings reveal NiO-rich shell and Pd-rich core as confirmed by X-ray photoelectron spectroscopy, X-ray scattering, and electron energy loss spectroscopy. During hydrogen adsorption, which mainly occurs on Pd atoms, the Pd-rich core fragments into small pockets, increasing its surface area. This process is more pronounced for nanoparticles with lower Pd loading, emphasizing the role of NiO. These results shed light on the atomic changes occurring in the PdxNi100-x nanoparticles during hydrogen adsorption and can be applicable on multi-metallic systems to improve hydrogen storage properties.

We study the entanglement harvesting between two identical buckled honeycomb lattices placed inside a planar microcavity. By applying time dependent perturbation theory, we obtain quantum correlations between both layers induced by the cavity field. Considering the vacuum state as the initial state of the cavity field and tracing out the time-evolved degrees of freedom, we analyze the entanglement formation using the concurrence measure. We show that the concurrence depends on the virtual photon exchanged and the positions of the layer through the interlayer photon propagator. Furthermore, we find that the formation of entanglement between equal energy electrons tends to be enhanced when they move in perpendicular directions. Our results indicate that a buckled honeycomb structure and a large spin-orbit interaction favor the entanglement harvesting., Comment: 10 pages, 6 figures

Radiomics analysis has emerged as a promising approach for extracting quantitative features from medical images to aid in cancer diagnosis and treatment. However, radiomics research currently lacks standardization, and radiomics features can be highly dependent on the acquisition and pre-processing techniques used. In this study, we aim to investigate the effect of various intensity normalization techniques on the robustness of radiomics features extracted from MRI scans of breast cancer patients. The images used are from the publicly available I-SPY TRIAL dataset, which contains MRI scans of stage 2 or 3 breast cancer patients and from the Platinum and PARP inhibitor for Neoadjuvant treatment of Triple Negative and / or BRCA positive breast cancer (PARTNER) trial. We compared the effect of commonly used intensity normalization techniques on the robustness of radiomics features using Intraclass Correlation Coefficient (ICC) between multiple combinations of normalization approaches, identified categories that are robust and therefore could be compared between studies regardless of the pre-processing used. We were able to show that while systematic differences between MRI scanners can significantly affect many radiomics features, a combination of Bias Field correction with piecewise linear histogram normalization can mitigate some of the effects compared to other normalization methods investigated in this paper. We were able to demonstrate the importance of carefully selecting and standardizing normalization methods for accurate and reliable radiomics analysis in breast MRI scans.

We report the X-ray polarization properties of the high-synchrotron-peaked (HSP) blazar PKS 2155$-$304 based on observations with the Imaging X-ray Polarimetry Explorer (IXPE). We observed the source between Oct 27 and Nov 7, 2023. We also conducted an extensive contemporaneous multiwavelength (MW) campaign. We find that during the first half ($T_1$) of the IXPE pointing, the source exhibited the highest X-ray polarization degree detected for an HSP blazar thus far, (30.7$\pm$2.0)%, which dropped to (15.3$\pm$2.1)% during the second half ($T_2$). The X-ray polarization angle remained stable during the IXPE pointing at 129.4$^\circ$$\pm$1.8$^\circ$ and 125.4$^\circ$$\pm$3.9$^\circ$ during $T_1$ and $T_2$, respectively. Meanwhile, the optical polarization degree remained stable during the IXPE pointing, with average host-galaxy-corrected values of (4.3$\pm$0.7)% and (3.8$\pm$0.9)% during the $T_1$ and $T_2$, respectively. During the IXPE pointing, the optical polarization angle changed achromatically from $\sim$140$^\circ$ to $\sim$90$^\circ$ and back to $\sim$130$^\circ$. Despite several attempts, we only detected (99.7% conf.) the radio polarization once (during $T_2$, at 225.5 GHz): with degree (1.7$\pm$0.4)% and angle 112.5$^\circ$$\pm$5.5$^\circ$. The direction of the broad pc-scale jet is rather ambiguous and has been found to point to the east and south at different epochs; however, on larger scales (> 1.5 pc) the jet points toward the southeast ($\sim$135$^\circ$), similar to all of the MW polarization angles. Moreover, the X-ray to optical polarization degree ratios of $\sim$7 and $\sim$4 during $T_1$ and $T_2$, respectively, are similar to previous IXPE results for several HSP blazars. These findings, combined with the lack of correlation of temporal variability between the MW polarization properties, agree with an energy-stratified shock-acceleration scenario in HSP blazars., Comment: 17 pages, 10 figures, 4 tables, Accepted for publication in A&A

IOP4 is a pipeline to perform photometry and polarimetry analysis of optical data from Calar Alto (CAHA) and Sierra Nevada (OSN) observatories. IOP4 implements Object Relational Mapping (ORM) to seamlessly integrate all information about the reduction and results in a database which can be used to query and plot results, flag data and inspect the reduction process in an integrated fashion with the whole pipeline. It also ships with an already built-in web interface which can be used out of the box to browse the database and supervise all pipeline processes. It is built to ease debugging and inspection of data. Reduction from five different instruments are already implemented: RoperT90, AndorT90 and DIPOL (at OSN 0.9m telescope), AndorT150 (OSN 1.5m telescope) and CAFOS (CAHA 2.2m telescope). IOP4's modular design allows for easy integration of new observatories and instruments, and its results have already featured in several high-impact refereed publications. In this paper we describe the implementation and characteristics of IOP4., Comment: Submitted April 23, 2024

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Paper submitted as part of the A&A special issue`Euclid on Sky'

We consider the problem of learning low-degree quantum objects up to $\varepsilon$-error in $\ell_2$-distance. We show the following results: $(i)$ unknown $n$-qubit degree-$d$ (in the Pauli basis) quantum channels and unitaries can be learned using $O(1/\varepsilon^d)$ queries (independent of $n$), $(ii)$ polynomials $p:\{-1,1\}^n\rightarrow [-1,1]$ arising from $d$-query quantum algorithms can be classically learned from $O((1/\varepsilon)^d\cdot \log n)$ many random examples $(x,p(x))$ (which implies learnability even for $d=O(\log n)$), and $(iii)$ degree-$d$ polynomials $p:\{-1,1\}^n\to [-1,1]$ can be learned through $O(1/\varepsilon^d)$ queries to a quantum unitary $U_p$ that block-encodes $p$. Our main technical contributions are new Bohnenblust-Hille inequalities for quantum channels and completely bounded~polynomials., Comment: 26+4 pages

Context. The blazar AO 0235+164, located at redshift $z=0.94$, has displayed interesting and repeating flaring activity in the past, the latest episodes occurring in 2008 and 2015. In 2020, the source brightened again, starting a new flaring episode that peaked in 2021. Aims. We study the origin and properties of the 2021 flare in relation to previous studies and the historical behavior of the source, in particular to the 2008 and 2015 flaring episodes. Methods. We analyze the multi-wavelength photo-polarimetric evolution of the source. From Very Long Baseline Array images, we derive the kinematic parameters of new components associated with the 2021 flare. We use this information to constrain a model for the spectral energy distribution of the emission during the flaring period. We propose an analytical geometric model to test whether the observed wobbling of the jet is consistent with precession. Results. We report the appearance of two new components that are ejected in a different direction than previously, confirming the wobbling of the jet. We find that the direction of ejection is consistent with that of a precessing jet.The derived period independently agrees with the values commonly found in the literature. Modeling of the spectral energy distribution further confirm that the differences between flares can be attributed to geometrical effects., Comment: Accepted 15 May 2024

Herein we studied the excited state dynamics of two bis-amino substituted anthraquinone (AQ) derivatives. Femtosecond transient absorption spectra show that intersystem crossing (ISC) takes place in 190-320 ps, and nanosecond transient absorption spectra demonstrated unusually short triplet state lifetime (2.1-5.4 us) for the two AQ derivatives at room temperature. Pulsed laser excited time-resolved electron paramagnetic resonance (TREPR) spectra shows an inversion of the electron spin polarization (ESP) phase pattern of the triplet state at longer delay time. Spectral simulations show that the faster decay of the Ty sublevel (x = 15.0 us, y = 1.50 us, z = 15.0 us) rationalizes the short T1 state lifetime and the ESP inversion. Computations taking into account the electron-vibrational coupling, i.e., the Herzberg-Teller effect, successfully rationalize the TREPR experimental observations.

This work focuses on the mathematical study of constant function market makers. We rigorously establish the conditions for optimal trading under the assumption of a quasilinear, but not necessarily convex (or concave), trade function. This generalizes previous results that used convexity, and also guarantees the robustness against arbitrage of so-designed automatic market makers. The theoretical results are illustrated by families of examples given by generalized means, and also by numerical simulations in certain concrete cases. These simulations along with the mathematical analysis suggest that the quasilinear-trade-function based automatic market makers might replicate the functioning of those based on convex functions, in particular regarding their resilience to arbitrage.

In a recent work Li, Qiao, Wigderson, Wigderson and Zhang introduced notions of quantum expansion based on $S_p$ norms and posed as an open question if they were all equivalent. We give an affirmative answer to this question., Comment: 5 pages

Monitored data collected from railway turnouts are vulnerable to cyberattacks: attackers may either conceal failures or trigger unnecessary maintenance actions. To address this issue, a cyberattack investigation method is proposed based on predictions made from the temporal evolution of the turnout behavior. These predictions are then compared to the field acquired data to detect any discrepancy. This method is illustrated on a collection of real-life data., Comment: To be published in the International Conference on Control, Automation and Diagnosis (ICCAD24)

We introduce Multivariate Multiscale Graph-based Dispersion Entropy (mvDEG), a novel, computationally efficient method for analyzing multivariate time series data in graph and complex network frameworks, and demonstrate its application in real-world data. mvDEG effectively combines temporal dynamics with topological relationships, offering enhanced analysis compared to traditional nonlinear entropy methods. Its efficacy is established through testing on synthetic signals, such as uncorrelated and correlated noise, showcasing its adeptness in discerning various levels of dependency and complexity. The robustness of mvDEG is further validated with real-world datasets, effectively differentiating various two-phase flow regimes and capturing distinct dynamics in weather data analysis. An important advancement of mvDEG is its computational efficiency. Our optimized algorithm displays a computational time that grows linearly with the number of vertices or nodes, in contrast to the exponential growth observed in classical methods. This efficiency is achieved through refined matrix power calculations that exploit matrix and Kronecker product properties, making our method faster than the state of the art. The significant acceleration in computational time positions mvDEG as a transformative tool for extensive and real-time applications, setting a new benchmark in the analysis of time series recorded at distributed locations and opening avenues for innovative applications., Comment: 9 pages, 10 figures

Rubrics are a commonly used tool for labeling voice corpora in speech quality assessment, although their application in the context of pathological speech remains relatively limited. In this study, we introduce a comprehensive rubric based on various dimensions of speech quality, including phonetics, fluency, and prosody. The objective is to establish standardized criteria for identifying errors within the speech of individuals with Down syndrome, thereby enabling the development of automated assessment systems. To achieve this objective, we utilized the Prautocal corpus. To assess the quality of annotations using our rubric, two experiments were conducted, focusing on phonetics and fluency. For phonetic evaluation, we employed the Goodness of Pronunciation (GoP) metric, utilizing automatic segmentation systems and correlating the results with evaluations conducted by a specialized speech therapist. While the obtained correlation values were not notably high, a positive trend was observed. In terms of fluency assessment, deep learning models like wav2vec were used to extract audio features, and we employed an SVM classifier trained on a corpus focused on identifying fluency issues to categorize Prautocal corpus samples. The outcomes highlight the complexities of evaluating such phenomena, with variability depending on the specific type of disfluency detected., Comment: Submitted to LREC-Coling 2024

Assume interest is in sampling from a probability distribution $\mu$ defined on $(\mathsf{Z},\mathscr{Z})$. We develop a framework to construct sampling algorithms taking full advantage of numerical integrators of ODEs, say $\psi\colon\mathsf{Z}\rightarrow\mathsf{Z}$ for one integration step, to explore $\mu$ efficiently and robustly. The popular Hybrid/Hamiltonian Monte Carlo (HMC) algorithm [Duane, 1987], [Neal, 2011] and its derivatives are example of such a use of numerical integrators. However we show how the potential of integrators can be exploited beyond current ideas and HMC sampling in order to take into account aspects of the geometry of the target distribution. A key idea is the notion of integrator snippet, a fragment of the orbit of an ODE numerical integrator $\psi$, and its associate probability distribution $\bar{\mu}$, which takes the form of a mixture of distributions derived from $\mu$ and $\psi$. Exploiting properties of mixtures we show how samples from $\bar{\mu}$ can be used to estimate expectations with respect to $\mu$. We focus here primarily on Sequential Monte Carlo (SMC) algorithms, but the approach can be used in the context of Markov chain Monte Carlo algorithms as discussed at the end of the manuscript. We illustrate performance of these new algorithms through numerical experimentation and provide preliminary theoretical results supporting observed performance.

Accurately assessing tumor removal is paramount in the management of glioblastoma. We developed a pipeline using MRI scans and neural networks to segment tumor subregions and the surgical cavity in postoperative images. Our model excels in accurately classifying the extent of resection, offering a valuable tool for clinicians in assessing treatment effectiveness.

We develop a many-body perturbation theory to account for the emergence of moir\'e bands in the continuum model of twisted bilayer graphene. Our framework is build upon treating the moir\'e potential as a perturbation that transfers electrons from one layer to another through the exchange of the three wave vectors that define the moir\'e Brillouin zone. By working in the two-band basis of each monolayer, we analyze the one-particle Green's function and introduce a diagrammatic representation for the scattering processes. We then identify the moir\'e-induced self-energy, relate it to the quasiparticle weight and velocity of the moir\'e bands, and show how it can be obtained by summing irreducible diagrams. We also connect the emergence of flat bands to the behavior of the static self-energy at the magic angle. In particular, we show that a vanishing Dirac velocity is a direct consequence of the relative orientation of the momentum transfer vectors, suggesting that the origin of magic angles in twisted bilayer graphene is intrinsically connected to its geometrical properties. Our approach provides a many-body diagrammatic framework that highlights the physical properties of the moir\'e bands., Comment: 20 pages, 11 figures

We consider the problems of testing and learning an $n$-qubit $k$-local Hamiltonian from queries to its evolution operator with respect the 2-norm of the Pauli spectrum, or equivalently, the normalized Frobenius norm. For testing whether a Hamiltonian is $\epsilon_1$-close to $k$-local or $\epsilon_2$-far from $k$-local, we show that $O(1/(\epsilon_2-\epsilon_1)^{8})$ queries suffice. This solves two questions posed in a recent work by Bluhm, Caro and Oufkir. For learning up to error $\epsilon$, we show that $\exp(O(k^2+k\log(1/\epsilon)))$ queries suffice. Our proofs are simple, concise and based on Pauli-analytic techniques., Comment: 7 pages

This paper discusses the stability assessment of low-inertia power systems through a real-world large-scale low-inertia system, namely, the All-Island power system (AIPS) of Ireland and Northern Ireland. This system currently accommodates world-record levels of system non-synchronous penetration namely 75% (planning to increase to 80% next year). The paper discusses one-month results obtained with the state-of-the-art stability tool called look-ahead security assessment (LSAT). This tool carries out rotor-angle, frequency and voltage stability analyses and is implemented in the control centres of the transmission system operators (TSOs). The paper shows that, at the time of writing, the main binding stability constraint of the AIPS is related to the limits on the rate of change of frequency (RoCoF).

This paper discusses emerging operational challenges associated with the integration of solar photovoltaic (PV) in the All-Island power system (AIPS) of Ireland and Northern Ireland. These include the impact of solar PV on: (i) dispatch down levels; (ii) long-term frequency deviations; (iii) voltage magnitude variations; and (iv) operational demand variations. A case study based on actual data from the AIPS is used to analyze the above challenges. It is shown that despite its (still) relatively low penetration compared to wind power penetration, solar PV is challenging the real-time operation of the AIPS, e.g., maintaining frequency within operational limits. EirGrid and SONI, the transmission system operators (TSOs) of the AIPS, are working toward addressing all the above challenges.

In the recent years, the interest in electric direct current (DC) technologies (such as converters, batteries, electric vehicles, etc.) is increasing due to its potential on energy efficiency and sustainability. However, the vast majority of electric systems and networks are based on alternating current (AC), as they also have certain advantages regarding cost-effective transport and robustness. In this paper, an AC/DC optimal power flow method for hybrid microgrids and several key performance indicators (KPIs) for its techno-economic assessment are presented. The combination of both calculations allows users to clearly determine the viability of their hybrid microgrids. AC/DC networks have been modelled considering their most common elements. For the power flow method, a polynomial optimisation is formulated considering four different objective functions: the minimisation of energy losses, voltage deviation and operational costs, and also the maximisation of the microgrid generation. The power flow method and the techno-economic analysis have been implemented in Python and validated in the Centro de Desarrollo de Energ\'ias Renovables (CEDER) demonstrator for TIGON. The results show that the calculated power flow variables and the ones measured at CEDER are practically the same. In addition, the KPIs have been obtained and compared for four operating scenarios: baseline, no battery, battery flexibility and virtual battery (VB) flexibility. The last one result in the most profitable option.

A method for identifying and quantifying the flexibility of electricity demand in a production plant is reported. The plant is equipped with electric machines, product storage silos, distributed generation, and electrical storage systems. The method aims to minimize production costs. To achieve this, the plant is mathematically modeled, and an economic optimization problem is formulated by managing these plant equipment. From this optimal schedule (base schedule), the feasibility of modifying it to sell or buy energy in the electricity balancing regulation markets is evaluated, thus obtaining the so called flexibility schedule. Finally, this method was successfully applied to a real case using data from a Spanish cement production plant.

Monochromatic gamma-ray signals constitute a potential smoking gun signature for annihilating or decaying dark matter particles that could relatively easily be distinguished from astrophysical or instrumental backgrounds. We provide an updated assessment of the sensitivity of the Cherenkov Telescope Array (CTA) to such signals, based on observations of the Galactic centre region as well as of selected dwarf spheroidal galaxies. We find that current limits and detection prospects for dark matter masses above 300 GeV will be significantly improved, by up to an order of magnitude in the multi-TeV range. This demonstrates that CTA will set a new standard for gamma-ray astronomy also in this respect, as the world's largest and most sensitive high-energy gamma-ray observatory, in particular due to its exquisite energy resolution at TeV energies and the adopted observational strategy focussing on regions with large dark matter densities. Throughout our analysis, we use up-to-date instrument response functions, and we thoroughly model the effect of instrumental systematic uncertainties in our statistical treatment. We further present results for other potential signatures with sharp spectral features, e.g.~box-shaped spectra, that would likewise very clearly point to a particle dark matter origin., Comment: 43 pages JCAP style (excluding author list and references), 19 figures

The cross dock door design problem consists of deciding on the number and capacity of inbound and outbound doors for receiving product pallets from origin nodes and exiting them to destination nodes. The uncertainty, realized in scenarios, lies in the occurrence of these nodes, the number and cost of the pallets, and the disruption of the capacity of the doors. It is represented using a stochastic two stage binary quadratic model. The first stage decisions are related to the cross dock infrastructure design, and the second stage decisions are related to the node to assignments of the doors. This is the first time, as far as we know, that a stochastic two stage binary quadratic model has been presented for minimizing the construction cost of the infrastructure and its exploitation expected cost in the scenarios. Given the difficulty of solving this combinatorial problem, a mathematically equivalent mixed integer linear formulation is introduced. However, searching an optimal solution is still impractical for commercial solvers. Thus, a scenario cluster decomposition based matheuristic algorithm is introduced to obtain feasible solutions with small optimality gap and reasonable computational effort. A broad study to validate the proposal gives solutions with a much smaller gap than the ones provided by a state of the art general solver. In fact, the proposal provides solutions with a 1 to 5% optimality gap, while the solver does it with up to a 12% gap, if any, and requires a wall time two orders of magnitude higher., Comment: 20 pages, 5 figures

PG 1553+113 is one of the few blazars with a convincing quasi-periodic emission in the gamma-ray band. The source is also a very high-energy (VHE; >100 GeV) gamma-ray emitter. To better understand its properties and identify the underlying physical processes driving its variability, the MAGIC Collaboration initiated a multiyear, multiwavelength monitoring campaign in 2015 involving the OVRO 40-m and Medicina radio telescopes, REM, KVA, and the MAGIC telescopes, Swift and Fermi satellites, and the WEBT network. The analysis presented in this paper uses data until 2017 and focuses on the characterization of the variability. The gamma-ray data show a (hint of a) periodic signal compatible with literature, but the X-ray and VHE gamma-ray data do not show statistical evidence for a periodic signal. In other bands, the data are compatible with the gamma-ray period, but with a relatively high p-value. The complex connection between the low and high-energy emission and the non-monochromatic modulation and changes in flux suggests that a simple one-zone model is unable to explain all the variability. Instead, a model including a periodic component along with multiple emission zones is required., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society. 19 pages, 9 figures. Corresponding authors: Elisa Prandini, Antonio Stamerra, Talvikki Hovatta

In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping. In contrast to previous research focused on electronic health records (EHR), here we propose an ML approach for phenotyping using routinely collected physiological time series data. Our new algorithm integrates Long Short-Term Memory (LSTM) networks with collaborative filtering concepts to identify common physiological states across patients. Tested on real-world ICU clinical data for intracranial hypertension (IH) detection in patients with brain injury, our method achieved an area under the curve (AUC) of 0.889 and average precision (AP) of 0.725. Moreover, our algorithm outperforms autoencoders in learning more structured latent representations of the physiological signals. These findings highlight the promise of our methodology for patient phenotyping, leveraging routinely collected multivariate time series to improve clinical care practices.

This article discusses the development of an IoT system for monitoring and controlling various devices and systems from different vendors. The authors considered key challenges in IoT projects, such as interoperability and integration, scalability, and data storage, processing, and visualization, during the design and deployment phases. In addition to these general challenges, the authors also delve into the specific integration challenges they encountered. Various devices and systems were integrated into the system and five real-world scenarios in a university campus environment are used to illustrate the challenges encountered. The scenarios involve monitoring various aspects of a university campus environment, including air quality, environmental parameters, energy efficiency, solar photovoltaic energy, and energy consumption. The authors analyzed data and CPU usage to ensure that the system could handle the large amount of data generated by the devices. The platform developed uses open source projects such as Home Assistant, InfluxDB, Grafana, and Node-RED. All developments have been published as open source in public repositories. In conclusion, this work highlights the potential and feasibility of IoT systems in various real-world applications, the importance of considering key challenges in IoT projects during the design and deployment phases, and the specific integration challenges that may be encountered., Comment: 21 pages, 16 figures, Published in Internet of Things

Cosmological structure formation simulations of ultralight axion-like dark matter have shown that an axion star forms at the center of every dark matter halo in the Universe. These axion stars would then form in large numbers during the dark ages, $z \lesssim 70$. Axion stars would represent the densest axion environments in the Universe, and as such they can trigger collective processes that cannot otherwise occur for axions in vacuum. In particular, even though the lifetime of individual sub-eV axions decaying into a pair of photons is much larger than the age of the Universe, axion stars can decay into photons on very short time scales due to parametric resonance. In this talk, based on arXiv:2302.10206 and arXiv:2301.09769, I will discuss the cosmological implications of such decays. We show that massive enough axion stars will decay into a large number of radio photons which will in turn lead to heating and ionization during the dark ages which is strongly constrained by Planck. As a result, we find that couplings $10^{-14}\,{\rm GeV}^{-1} \lesssim g_{a\gamma\gamma} \lesssim 10^{-10}\,{\rm GeV}^{-1}$ are excluded by Planck for $10^{-14}\,{\rm eV}\lesssim m_a\lesssim 10^{-8}\,{\rm eV}$ within our benchmark model of axion star abundance. We also highlight that future measurements of the 21 cm line can have sensitivity to couplings at least one order of magnitude smaller., Comment: 11 pages, 4 figures. Contribution to the 1st COSMIC WISPers Workshop, Bari, September 2023

In recent years, a new generation of optical intensity interferometers has emerged, leveraging the existing infrastructure of Imaging Atmospheric Cherenkov Telescopes (IACTs). The MAGIC telescopes host the MAGIC-SII system (Stellar Intensity Interferometer), implemented to investigate the feasibility and potential of this technique on IACTs. After the first successful measurements in 2019, the system was upgraded and now features a real-time, dead-time-free, 4-channel, GPU-based correlator. These hardware modifications allow seamless transitions between MAGIC's standard very-high-energy gamma-ray observations and optical interferometry measurements within seconds. We establish the feasibility and potential of employing IACTs as competitive optical Intensity Interferometers with minimal hardware adjustments. The measurement of a total of 22 stellar diameters are reported, 9 corresponding to reference stars with previous comparable measurements, and 13 with no prior measurements. A prospective implementation involving telescopes from the forthcoming Cherenkov Telescope Array Observatory's northern hemisphere array, such as the first prototype of its Large-Sized Telescopes, LST-1, is technically viable. This integration would significantly enhance the sensitivity of the current system and broaden the UV-plane coverage. This advancement would enable the system to achieve competitive sensitivity with the current generation of long-baseline optical interferometers over blue wavelengths., Comment: 18 pages, 13 figures, submitted to MNRAS

In the world of Internet of Things (IoT), obtaining the physical location of devices has always been a task of great interest for developing increasingly complex location-based services (LBS). That is why in recent years wireless communication standards have been incorporating new additions focused on providing localization mechanisms to technologies widely used in the IoT world, such as Wi-Fi or Bluetooth. In particular, the IEEE 802.11-2016 Wi-Fi standard introduced ranging estimation between two devices through the so-called fine time measurement (FTM) protocol, defined by the IEEE 802.11mc. FTM is not yet widespread in the IoT field, but commercial modules capable of offering this functionality at a reasonable price are starting to appear. In early 2021, the most widespread system on a chip (SOC) family among IoT devices, the ESP32-XX series, added support for this Wi-Fi standard, enabling, for the first time, the use of a standard designed for location-based systems. This article analyzes the performance of this FTM implementation by carrying out and studying several measurement campaigns in different indoor and outdoor scenarios. Additionally, this work proposes an alternative real-time implementation for distance estimation inside the ESP32 using an approach based on machine learning. Such an implementation is successfully validated in a scenario totally different than those considered for the training and test sets. Finally, both the measurement sets and the developed software are available to the scientific community., Comment: 14 pages, 21 figures, Published in IEEE Internet of Things Journal

In contemporary society, the pressing challenge of preserving user privacy clashes with the imperative for smart buildings to efficiently manage their resources, particularly in the context of occupancy monitoring for optimized energy utilization. This paper delves into the application of millimiter wave (mmWave) frequency modulated continuous wave (FMCW) radar technology for occupancy monitoring. mmWave FMCW radar, unlike conventional methods that often require the use of identifiable tags or involve image analysis, operates without the need for such identifiers, mitigating privacy concerns. However, challenges arise when attempting to cover extensive indoor spaces due to the limited range of individual mmWave FMCW radar devices. The present work proposes the use of a flexible software architecture to integrate the measurements of several mmWave FMCW radar devices, so that the whole behaves as a single sensor. To validate the proposal, an example of use in a real environment in an indoor space monitored with three mmWave FMCW radar devices is also presented. The example details the whole process, from the physical installation of the devices to the use of the different software modules that allow the integration of the data into a common internet of things (IoT) management platform such as Home Assistant. All the elements, from the measurements captured during the test to the different software implementations, are shared publicly with the scientific community., Comment: 13 pages, 9 figures, submitted to IEEE Internet of Things Journal

Today's needs for monitoring and control of different devices in organizations require an Internet of Things (IoT) platform that can integrate heterogeneous elements provided by multiple vendors and using different protocols, data formats and communication technologies. This article provides a comprehensive review of all the architectures, technologies, protocols and data formats most commonly used by existing IoT platforms. On this basis, a comparative analysis of the most widely used open source IoT platforms is presented. This exhaustive comparison is based on multiple characteristics that will be essential to select the platform that best suits the needs of each organization., Comment: 15 pages, 4 figures, Published in Internet of Things

Surface-enhanced infrared absorption (SEIRA) spectroscopy exploits the field enhancement near nanophotonic structures for highly sensitive characterization of (bio)molecules. The vibrational signature observed in SEIRA spectra is typically interpreted as field-enhanced molecular absorption. Here we study molecular vibrations in the near field of single antennas and show that the vibrational signature can be equally well explained by field-enhanced molecular scattering. Although the infrared scattering cross section of molecules is negligible compared to their absorption cross section, the interference between the molecular-scattered field and the incident field enhances the spectral signature caused by molecular vibrational scattering by 10 orders of magnitude, thus becoming as large as that of field-enhanced molecular absorption. We provide experimental evidence that field-enhanced molecular scattering can be measured, scales in intensity with the fourth power of the local field enhancement and fully explains the vibrational signature in SEIRA spectra in both magnitude and line shape. Our work may open new paths for developing highly sensitive SEIRA sensors that exploit the presented scattering concept., Comment: 38 pages, 5 figures, 6 extended data figures

Axion-like particles (ALPs) are pseudo-Nambu-Goldstone bosons that emerge in various theories beyond the standard model. These particles can interact with high-energy photons in external magnetic fields, influencing the observed gamma-ray spectrum. This study analyzes 41.3 hrs of observational data from the Perseus Galaxy Cluster collected with the MAGIC telescopes. We focused on the spectra the radio galaxy in the center of the cluster: NGC 1275. By modeling the magnetic field surrounding this target, we searched for spectral indications of ALP presence. Despite finding no statistical evidence of ALP signatures, we were able to exclude ALP models in the sub-micro electronvolt range. Our analysis improved upon previous work by calculating the full likelihood and statistical coverage for all considered models across the parameter space. Consequently, we achieved the most stringent limits to date for ALP masses around 50 neV, with cross sections down to $g_{a\gamma} = 3 \times 10^{-12}$ GeV$^{-1}$., Comment: 25 pages, 10 figures, accepted for publication in Physics of the Dark Universe

Observations of linear polarization in the 2-8 keV energy range with the Imaging X-ray Polarimetry Explorer (IXPE) explore the magnetic field geometry and dynamics of the regions generating non-thermal radiation in relativistic jets of blazars. These jets, particularly in blazars whose spectral energy distribution peaks at X-ray energies, emit X-rays via synchrotron radiation from high-energy particles within the jet. IXPE observations of the X-ray selected BL Lac-type blazar 1ES 1959+650 in 2022 May 3-4 showed a significant linear polarization degree of $\Pi_\mathrm{x} = 8.0\% \pm 2.3\%$ at an electric-vector position angle $\psi_\mathrm{x} = 123^\circ \pm 8^\circ$. However, in 2022 June 9-12, only an upper limit of $\Pi_\mathrm{x} \leq 5.1\%$ could be derived (at the 99% confidence level). The degree of optical polarization at that time $\Pi_\mathrm{O} \sim 5\%$ is comparable to the X-ray measurement. We investigate possible scenarios for these findings, including temporal and geometrical depolarization effects. Unlike some other X-ray selected BL Lac objects, there is no significant chromatic dependence of the measured polarization in 1ES 1959+650, and its low X-ray polarization may be attributed to turbulence in the jet flow with dynamical timescales shorter than 1 day., Comment: 16 pages, 4 figures, accepted for publication in The Astrophysical Journal

We here present the results of an analysis of the optical spectroscopy of 42 globular cluster (GC) candidates in the nearby spiral galaxy M81 (3.61~Mpc). The spectra were obtained using the long-slit and MOS modes of the OSIRIS instrument at the 10.4~m Gran Telescopio Canarias (GTC) at a spectral resolution of $\sim$1000. We used the classical H$\beta$ vs [MgFe]$'$ index diagram to separate genuine old GCs from clusters younger than 3 Gyr. Of the 30 spectra with continuum signal-to-noise ratio $>10$, we confirm 17 objects to be classical GCs (age $>10$~Gyr, $-1.4<$[Fe/H]$<-$0.4), with the remaining 13 being intermediate-age clusters (1-7.5~Gyr). We combined age and metallicity data of other nearby spiral galaxies ($\lesssim18$~Mpc) obtained using similar methodology like the one we have used here to understand the origin of GCs in spiral galaxies in the cosmological context. We find that the metal-poor ([Fe/H]<$-$1) GCs continued to form up to 6~Gyr after the first GCs were formed, with all younger systems (age $<8$~Gyr) being metal-rich., Comment: 16 pages, 13 figures, 8 tables; Accepted for publication in MNRAS