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During their formation, galaxies are subject to tidal forces, which create correlations between their shapes and the large-scale structure of the Universe, known as intrinsic alignment. This alignment is a contamination for cosmic-shear measurements as one needs to disentangle correlations induced by external lensing effects from those intrinsically present in galaxies. We constrain the amplitude of intrinsic alignment and test models by making use of the overlap between the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) covering $3500 \, \mathrm{deg}^2$, and spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS/eBOSS). By comparing our results to measurements from other lensing surveys on the same spectroscopic tracers, we can test the reliability of these estimates and verify they are not survey dependent. We measure projected correlation functions between positions and ellipticities, which we model with perturbation theory to constrain the commonly used non-linear alignment model and its higher-order expansion. Using the non-linear alignment model, we obtain a $13\sigma$ detection with CMASS galaxies, a $3\sigma$ detection with LRGs, and a detection compatible with the null hypothesis for ELGs. We test the tidal alignment and tidal torque model, a higher-order alignment model, which we find to be in good agreement with the non-linear alignment prediction and for which we can constrain the second-order parameters. We show a strong scaling of our intrinsic alignment amplitude with luminosity. We demonstrate that the UNIONS sample is robust against systematic contributions, particularly concerning PSF biases. We reached a reasonable agreement when comparing our measurements to other lensing samples for the same spectroscopic samples. We take this agreement as an indication that direct measurements of intrinsic alignment are mature for stage IV priors., Comment: To be submitted to A&A, 18 pages, 17 figures

In this study, we investigate the flow around a canonical blunt body, specifically a D-shaped body of width $D$, in a closed water channel. Our goal is to explore near-wake flow modifications when a series of rigid and flexible plates ($l=1.8D$) divided into filaments ($h=0.2D$) are added. We focus on assessing the interaction between the flexible filaments and the wake dynamics, with the aim of reducing the recirculation bubble and decreasing the velocity deficit in the wake. To achieve this, we conduct a comparative study varying the stiffness and position of the filaments at different flow velocities. The study combines Particle Image Velocimetry (PIV) measurements in the wake behind the body with recordings of the deformation of the flexible filaments. Our observations show that the flexible filaments can passively reconfigure in a two-dimensional fashion, with a mean tip deflection angle that increases with the incoming flow velocity. Deflection angles up to approximately $\sim 9^\circ$ and vibration tip amplitude of around $\sim 4^\circ$ are achieved for flow velocities $U^{*}\simeq f_{n}D/u_{\infty}\geq 1.77$, where $f_n$ is the natural frequency of the flexible filaments. This reconfiguration results in a reduction of the recirculation bubble and a decrease in the velocity deficit in the wake compared to the reference and rigid cases. In addition, curved filaments with a prescribed rigid deformation exhibit very similar behavior to that of flexible filaments, indicating that the vibration of flexible filaments does not significantly disturb the wake. The obtained results highlight the interest of testing flexible appendages in the wake of blunt bodies for designing effective flow control devices., Comment: 13 pages, 9 figures

$^{76}$Ge can $\beta\beta$ decay into three possible excited states of $^{76}$Se, with the emission of two or, if the neutrino is Majorana, zero neutrinos. None of these six transitions have yet been observed. The MAJORANA DEMONSTRATOR was designed to study $\beta\beta$ decay of $^{76}$Ge using a low background array of high purity germanium detectors. With 98.2 kg-y of isotopic exposure, the DEMONSTRATOR sets the strongest half-life limits to date for all six transition modes. For $2\nu\beta\beta$ to the $0^+_1$ state of $^{76}$Se, this search has begun to probe for the first time half-life values predicted using modern many-body nuclear theory techniques, setting a limit of $T_{1/2}>1.5\times10^{24}$ y (90% CL)., Comment: 8 pages, 3 figures

The background index is an important quantity which is used in projecting and calculating the half-life sensitivity of neutrinoless double-beta decay ($0\nu\beta\beta$) experiments. A novel analysis framework is presented to calculate the background index using the specific activities, masses and simulated efficiencies of an experiment's components as distributions. This Bayesian framework includes a unified approach to combine specific activities from assay. Monte Carlo uncertainty propagation is used to build a background index distribution from the specific activity, mass and efficiency distributions. This analysis method is applied to the MAJORANA DEMONSTRATOR, which deployed arrays of high-purity Ge detectors enriched in $^{76}$Ge to search for $0\nu\beta\beta$. The framework projects a mean background index of $\left[8.95 \pm 0.36\right] \times 10^{-4}$cts/(keV kg yr) from $^{232}$Th and $^{238}$U in the DEMONSTRATOR's components., Comment: 9 pages, 3 figures

dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, it implements the top performing state-of-the-art deep learning techniques for ordinal classification problems. Ordinal approaches are designed to leverage the ordering information present in the target variable. Specifically, it includes loss functions, various output layers, dropout techniques, soft labelling methodologies, and other classification strategies, all of which are appropriately designed to incorporate the ordinal information. Furthermore, as the performance metrics to assess novel proposals in ordinal classification depend on the distance between target and predicted classes in the ordinal scale, suitable ordinal evaluation metrics are also included. dlordinal is distributed under the BSD-3-Clause license and is available at https://github.com/ayrna/dlordinal.

An ordinal classification problem is one in which the target variable takes values on an ordinal scale. Nowadays, there are many of these problems associated with real-world tasks where it is crucial to accurately classify the extreme classes of the ordinal structure. In this work, we propose a unimodal regularisation approach that can be applied to any loss function to improve the classification performance of the first and last classes while maintaining good performance for the remainder. The proposed methodology is tested on six datasets with different numbers of classes, and compared with other unimodal regularisation methods in the literature. In addition, performance in the extreme classes is compared using a new metric that takes into account their sensitivities. Experimental results and statistical analysis show that the proposed methodology obtains a superior average performance considering different metrics. The results for the proposed metric show that the generalised beta distribution generally improves classification performance in the extreme classes. At the same time, the other five nominal and ordinal metrics considered show that the overall performance is aligned with the performance of previous alternatives.

The Euclid mission of the European Space Agency will provide weak gravitational lensing and galaxy clustering surveys that can be used to constrain the standard cosmological model and its extensions, with an opportunity to test the properties of dark matter beyond the minimal cold dark matter paradigm. We present forecasts from the combination of these surveys on the parameters describing four interesting and representative non-minimal dark matter models: a mixture of cold and warm dark matter relics; unstable dark matter decaying either into massless or massive relics; and dark matter experiencing feeble interactions with relativistic relics. We model these scenarios at the level of the non-linear matter power spectrum using emulators trained on dedicated N-body simulations. We use a mock Euclid likelihood to fit mock data and infer error bars on dark matter parameters marginalised over other parameters. We find that the Euclid photometric probe (alone or in combination with CMB data from the Planck satellite) will be sensitive to the effect of each of the four dark matter models considered here. The improvement will be particularly spectacular for decaying and interacting dark matter models. With Euclid, the bounds on some dark matter parameters can improve by up to two orders of magnitude compared to current limits. We discuss the dependence of predicted uncertainties on different assumptions: inclusion of photometric galaxy clustering data, minimum angular scale taken into account, modelling of baryonic feedback effects. We conclude that the Euclid mission will be able to measure quantities related to the dark sector of particle physics with unprecedented sensitivity. This will provide important information for model building in high-energy physics. Any hint of a deviation from the minimal cold dark matter paradigm would have profound implications for cosmology and particle physics., Comment: 31 pages, 21 figures

The Euclid mission of the European Space Agency will deliver weak gravitational lensing and galaxy clustering surveys that can be used to constrain the standard cosmological model and extensions thereof. We present forecasts from the combination of these surveys on the sensitivity to cosmological parameters including the summed neutrino mass $M_\nu$ and the effective number of relativistic species $N_{\rm eff}$ in the standard $\Lambda$CDM scenario and in a scenario with dynamical dark energy ($w_0 w_a$CDM). We compare the accuracy of different algorithms predicting the nonlinear matter power spectrum for such models. We then validate several pipelines for Fisher matrix and MCMC forecasts, using different theory codes, algorithms for numerical derivatives, and assumptions concerning the non-linear cut-off scale. The Euclid primary probes alone will reach a sensitivity of $\sigma(M_\nu)=$56meV in the $\Lambda$CDM+$M_\nu$ model, whereas the combination with CMB data from Planck is expected to achieve $\sigma(M_\nu)=$23meV and raise the evidence for a non-zero neutrino mass to at least the $2.6\sigma$ level. This can be pushed to a $4\sigma$ detection if future CMB data from LiteBIRD and CMB Stage-IV are included. In combination with Planck, Euclid will also deliver tight constraints on $\Delta N_{\rm eff}< 0.144$ (95%CL) in the $\Lambda$CDM+$M_\nu$+$N_{\rm eff}$ model, or $\Delta N_{\rm eff}< 0.063$ when future CMB data are included. When floating $(w_0, w_a)$, we find that the sensitivity to $N_{\rm eff}$ remains stable, while that to $M_\nu$ degrades at most by a factor 2. This work illustrates the complementarity between the Euclid spectroscopic and imaging/photometric surveys and between Euclid and CMB constraints. Euclid will have a great potential for measuring the neutrino mass and excluding well-motivated scenarios with additional relativistic particles., Comment: 48 pages, 33 figures

Aims:Calibrating the point spread function (PSF) is a fundamental part of weak gravitational lensing analyses. Even with corrected galaxy images, imperfect calibrations can introduce biases. We propose an analytical framework for quantifying PSF-induced systematics as diagnostics for cross-correlation measurements of weak lensing with density tracers, e.g., galaxy-galaxy lensing. We show how those systematics propagate to physical parameters of the density tracers. Those diagnostics only require a shape catalogue of PSF stars and foreground galaxy positions. Methods:We consider the PSF-induced multiplicative bias, and introduce three second-order statistics as additive biases. We compute both biases for the weak-lensing derived halo mass of spectroscopic foreground galaxy samples, in particular, their effect on the tangential shear and fitted halo mass as a function of stellar mass. In addition, we assess their impact on the recently published black-hole - halo-mass relation for type I Active Galactic Nuclei (AGNs). Results:Using weak-lensing catalogues from the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) and Dark Energy Survey (DES), we find the multiplicative biases in the tangential shear to be less than $0.5\%$. No correlations between additive bias and galaxy properties of the foreground sample are detected. The combined PSF systematics affect low-mass galaxies and small angular scales; halo mass estimates can be biased by up to 18$\%$ for a sample of central galaxies in the stellar mass range 9.0 $\leq$ log $M_*/\rm M_{\odot}$ < 9.5. Conclusions:The PSF-induced multiplicative bias is a subdominant contribution to current studies of weak-lensing - density cross-correlations, but might become significant for upcoming Stage-VI surveys. For samples with a low tangential shear, additive PSF systematics can induce a significant bias on derived properties such as halo mass., Comment: 11 pages, 5 figures

This letter presents, for the first time, direct constraints on the black-hole-to-halo-mass relation using weak gravitational lensing measurements. We construct type I and type II Active Galactic Nuclei (AGNs) samples from the Sloan Digital Sky Survey (SDSS), with a mean redshift of 0.4 0.1 for type I (type II) AGNs. This sample is cross-correlated with weak lensing shear from the Ultraviolet Near Infrared Northern Survey (UNIONS). We compute the excess surface mass density of the halos associated with $36,181$ AGNs from $94,308,561$ lensed galaxies and fit the halo mass in bins of black-hole mass. We find that more massive AGNs reside in more massive halos. We see no evidence of dependence on AGN type or redshift in the black-hole-to-halo-mass relationship when systematic errors in the measured black-hole masses are included. Our results are consistent with previous measurements for non-AGN galaxies. At a fixed black-hole mass, our weak-lensing halo masses are consistent with galaxy rotation curves, but significantly lower than galaxy clustering measurements. Finally, our results are broadly consistent with state-of-the-art hydro-dynamical cosmological simulations, providing a new constraint for black-hole masses in simulations., Comment: 14 pages, 5 figures. Submitted to ApJL

This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of generations, selecting the best individuals from each population in the same proportion and combining them to constitute a new initial population. At this point the main loop of an evolutionary algorithm is applied to the new population. The results show that our proposal considerably improves both the efficiency of previous methodologies and also, significantly, their efficacy in most of the data sets. We have carried out our experimentation on twelve data sets from the UCI repository and two complex real-world problems which differ in their number of instances, features and classes., Comment: 12 pages, 5 figures

In the last decade, the sound quality of electric induction motors is a hot topic in the research field. Specially, due to its high number of applications, the population is exposed to physical and psychological discomfort caused by the noise emission. Therefore, it is necessary to minimise its psychological impact on the population. In this way, the main goal of this work is to evaluate the use of multitask artificial neural networks as a modelling technique for simultaneously predicting psychoacoustic parameters of induction motors. Several inputs are used, such as, the electrical magnitudes of the motor power signal and the number of poles, instead of separating the noise of the electric motor from the environmental noise. Two different kind of artificial neural networks are proposed to evaluate the acoustic quality of induction motors, by using the equivalent sound pressure, the loudness, the roughness and the sharpness as outputs. Concretely, two different topologies have been considered: simple models and more complex models. The former are more interpretable, while the later lead to higher accuracy at the cost of hiding the cause-effect relationship. Focusing on the simple interpretable models, product unit neural networks achieved the best results: for MSE and for SEP. The main benefit of this product unit model is its simplicity, since only 10 inputs variables are used, outlining the effective transfer mechanism of multitask artificial neural networks to extract common features of multiple tasks. Finally, a deep analysis of the acoustic quality of induction motors in done using the best product unit neural networks.

In this work, a Generalized Finite Difference (GFD) scheme is presented for effectively computing the numerical solution of a parabolic-elliptic system modelling a bacterial strain with density-suppressed motility. The GFD method is a meshless method known for its simplicity for solving non-linear boundary value problems over irregular geometries. The paper first introduces the basic elements of the GFD method, and then an explicit-implicit scheme is derived. The convergence of the method is proven under a bound for the time step, and an algorithm is provided for its computational implementation. Finally, some examples are considered comparing the results obtained with a regular mesh and an irregular cloud of points.

A new model is considered to describe the evolution of tissue temperature and water concentration during a thermal ablation process. The aim of this study is to expand the results obtained in Blauth et. al. 2020, where the tissue water concentration is considered as a known function, experimentally obtained. The model consists of two coupled equations: a parabolic partial differential equation for the temperature $\theta$ and an ordinary differential equation for the water content $w$. The equation for $\theta$ accounts for temperature diffusion, blood perfusion, the heat source provided by the laser applicator and the vaporization effects which act when the temperature approaches 100C. The equation for $w$ accounts for the loss of tissue water that occurs during vaporization. The model equations are solved in a cylindrical domain around the laser applicator and the results are compared with the existing experimental data for porcine liver tissue from H\"ubner et. al. 2017.

Although it is well known that the bulk of dark matter (DM) has to be cold, the existence of an additional sub-dominant, hot species remains a valid possibility. In this paper we investigate the potential of the cosmic shear power spectrum to constrain such a mixed (hot plus cold) DM scenario with two additional free parameters, the hot-to-total DM fraction ($f_{\rm hdm}$) and the thermal mass of the hot component ($m_{\rm hdm}$). Running a Bayesian inference analysis for both the Kilo-Degree Survey cosmic shear data (KiDS) as well as the Cosmic Microwave Background (CMB) temperature and polarisation data from Planck, we derive new constraints for the mixed DM scenario. We find a 95 per cent confidence limit of $f_{\rm hdm}<0.08$ for a very hot species of $m_{\rm hdm}\leq20$ eV. This constraint is weakened to $f_{\rm hdm}<0.25$ for $m_{\rm hdm}\leq80$ eV. Scenarios with masses above $m_{\rm hdm}\sim200$ eV remain unconstrained by the data. Next to providing limits, we investigate the potential of mixed DM to address the clustering (or $S_8$) tension between lensing and the CMB. We find a reduction of the 2D ($\Omega_m - S_8$) tension from 2.9$\sigma$ to 1.6$\sigma$ when going from a pure cold DM to a mixed DM scenario. When computing the 1D gaussian tension on $S_8$ the improvement is milder, from 2.4$\sigma$ to 2.0$\sigma$., Comment: 8 pages, 4 figures

The enclosed data release consists of a subset of the calibration data from the Majorana Demonstrator experiment. Each Majorana event is accompanied by raw Germanium detector waveforms, pulse shape discrimination cuts, and calibrated final energies, all shared in an HDF5 file format along with relevant metadata. This release is specifically designed to support the training and testing of Artificial Intelligence (AI) and Machine Learning (ML) algorithms upon our data. This document is structured as follows. Section I provides an overview of the dataset's content and format; Section II outlines the location of this dataset and the method for accessing it; Section III presents the NPML Machine Learning Challenge associated with this dataset; Section IV contains a disclaimer from the Majorana collaboration regarding the use of this dataset; Appendix A contains technical details of this data release. Please direct questions about the material provided within this release to liaobo77@ucsd.edu (A. Li)., Comment: DataPlanet Access: https://dataplanet.ucsd.edu/dataset.xhtml?persistentId=perma:83.ucsddata/UQWQAV

Decaying dark matter (DDM) scenarios have recently regained attention due to their potential ability to resolve the well-known clustering (or $S_8$) tension between weak lensing (WL) and cosmic microwave background (CMB) measurements. In this paper, we investigate a well-established model where the original dark matter particle decays into a massless particle and a massive daughter particle. The latter obtains a velocity kick during the decay process that results in the suppression of the matter power spectrum at scales that are observable with WL shear observations. We perform the first fully non-linear WL analysis of this two-body decaying dark matter ($\Lambda$DDM) scenario, including intrinsic alignment and baryonic feedback processes. We used the cosmic shear band power spectra from \textit{KiDS-1000} data and combined it with temperature and polarisation data from \texttt{Planck} in order to constrain the $\Lambda$DDM model. We report new limits on the decay rate and mass splitting parameters that are significantly stronger than previous results, especially in the case of low-mass splittings. Regarding the $S_8$ tension, we found a reduction from about 3 to 2 $\sigma$, depending on which statistical measure is applied. We therefore conclude that the two-body $\Lambda$DDM model is able to reduce the $S_8$ tension without convincingly solving it., Comment: 15 pages, 13 figures

Time Series Classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time Series Ordinal Classification (TSOC) is the field covering this gap, yet unexplored in the literature. There are a wide range of time series problems showing an ordered label structure, and TSC techniques that ignore the order relationship discard useful information. Hence, this paper presents a first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state-of-the-art. Both convolutional- and deep learning-based methodologies (among the best performing alternatives for nominal TSC) are adapted for TSOC. For the experiments, a selection of 29 ordinal problems from two well-known archives has been made. In this way, this paper contributes to the establishment of the state-of-the-art in TSOC. The results obtained by ordinal versions are found to be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems., Comment: 13 pages, 9 figures, 2 tables

The MAJORANA DEMONSTRATOR was a search for neutrinoless double-beta decay ($0\nu\beta\beta$) in the $^{76}$Ge isotope. It was staged at the 4850-foot level of the Sanford Underground Research Facility (SURF) in Lead, SD. The experiment consisted of 58 germanium detectors housed in a low background shield and was calibrated once per week by deploying a $^{228}$Th line source for 1 to 2 hours. The energy scale calibration determination for the detector array was automated using custom analysis tools. We describe the offline procedure for calibration of the Demonstrator germanium detectors, including the simultaneous fitting of multiple spectral peaks, estimation of energy scale uncertainties, and the automation of the calibration procedure.

$^{180m}$Ta is a rare nuclear isomer whose decay has never been observed. Its remarkably long lifetime surpasses the half-lives of all other known $\beta$ and electron capture decays due to the large K-spin differences and small energy differences between the isomeric and lower energy states. Detecting its decay presents a significant experimental challenge but could shed light on neutrino-induced nucleosynthesis mechanisms, the nature of dark matter and K-spin violation. For this study, we repurposed the MAJORANA DEMONSTRATOR, an experimental search for the neutrinoless double-beta decay of $^{76}$Ge using an array of high-purity germanium detectors, to search for the decay of $^{180m}$Ta. More than 17 kilograms, the largest amount of tantalum metal ever used for such a search was installed within the ultra-low background detector array. In this paper we present results from the first year of Ta data taking and provide an updated limit for the $^{180m}$Ta half-life on the different decay channels. With new limits up to 1.5 x $10^{19}$ years, we improved existing limits by one to two orders of magnitude. This result is the most sensitive search for a single $\beta$ and electron capture decay ever achieved.

Predictive Maintenance (PdM) methods aim to facilitate the scheduling of maintenance work before equipment failure. In this context, detecting early faults in automated teller machines (ATMs) has become increasingly important since these machines are susceptible to various types of unpredictable failures. ATMs track execution status by generating massive event-log data that collect system messages unrelated to the failure event. Predicting machine failure based on event logs poses additional challenges, mainly in extracting features that might represent sequences of events indicating impending failures. Accordingly, feature learning approaches are currently being used in PdM, where informative features are learned automatically from minimally processed sensor data. However, a gap remains to be seen on how these approaches can be exploited for deriving relevant features from event-log-based data. To fill this gap, we present a predictive model based on a convolutional kernel (MiniROCKET and HYDRA) to extract features from the original event-log data and a linear classifier to classify the sample based on the learned features. The proposed methodology is applied to a significant real-world collected dataset. Experimental results demonstrated how one of the proposed convolutional kernels (i.e. HYDRA) exhibited the best classification performance (accuracy of 0.759 and AUC of 0.693). In addition, statistical analysis revealed that the HYDRA and MiniROCKET models significantly overcome one of the established state-of-the-art approaches in time series classification (InceptionTime), and three non-temporal ML methods from the literature. The predictive model was integrated into a container-based decision support system to support operators in the timely maintenance of ATMs.

Time Series Classification (TSC) is an extensively researched field from which a broad range of real-world problems can be addressed obtaining excellent results. One sort of the approaches performing well are the so-called dictionary-based techniques. The Temporal Dictionary Ensemble (TDE) is the current state-of-the-art dictionary-based TSC approach. In many TSC problems we find a natural ordering in the labels associated with the time series. This characteristic is referred to as ordinality, and can be exploited to improve the methods performance. The area dealing with ordinal time series is the Time Series Ordinal Classification (TSOC) field, which is yet unexplored. In this work, we present an ordinal adaptation of the TDE algorithm, known as ordinal TDE (O-TDE). For this, a comprehensive comparison using a set of 18 TSOC problems is performed. Experiments conducted show the improvement achieved by the ordinal dictionary-based approach in comparison to four other existing nominal dictionary-based techniques.

This whitepaper presents the research priorities decided on by attendees of the 2022 Town Meeting for Fundamental Symmetries, Neutrons and Neutrinos, which took place December 13-15, 2022 in Chapel Hill, NC, as part of the Nuclear Science Advisory Committee (NSAC) 2023 Long Range Planning process. A total of 275 scientists registered for the meeting. The whitepaper makes a number of explicit recommendations and justifies them in detail.

This article studies a parabolic-elliptic system modelling the pattern formation in E. coli bacteria in response to a chemoattractant known as acylhomoserine lactone concentration (AHL). The system takes into account certain bacterial strains with motility regulation, and the parameters of the equations represent the bacterial logistic growth, AHL diffusion and the rates of production and degradation of AHL. We consider the numerical solution to the system using the Generalized Finite Difference (GFD) Method, a meshless method known to effectively compute numerical solutions to nonlinear problems. The paper is organized to first explain the derivation of the explicit formulae of the method, followed by the study of the convergence of the explicit scheme. Then, several examples over regular and irregular meshes are given.