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Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical nodes for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability., Comment: 28 pages, 10 figures, 4 tables

Spatially-resolved emission line kinematics are invaluable to investigating fundamental galaxy properties and have become increasingly accessible for galaxies at $z\gtrsim0.5$ through sensitive near-infrared imaging spectroscopy and millimeter interferometry. Kinematic modeling is at the core of the analysis and interpretation of such data sets, which at high-z present challenges due to lower signal-to-noise ratio (S/N) and resolution compared to data of local galaxies. We present and test the 3D fitting functionality of DysmalPy, examining how well it recovers intrinsic disk rotation velocity and velocity dispersion, using a large suite of axisymmetric models, covering a range of galaxy properties and observational parameters typical of $z\sim1$-$3$ star-forming galaxies. We also compare DysmalPy's recovery performance to that of two other commonly used codes, GalPak3D and 3DBarolo, which we use in turn to create additional sets of models to benchmark DysmalPy. Over the ranges of S/N, resolution, mass, and velocity dispersion explored, the rotation velocity is accurately recovered by all tools. The velocity dispersion is recovered well at high S/N, but the impact of methodology differences is more apparent. In particular, template differences for parametric tools and S/N sensitivity for the non-parametric tool can lead to differences up to a factor of 2. Our tests highlight and the importance of deep, high-resolution data and the need for careful consideration of: (1) the choice of priors (parametric approaches), (2) the masking (all approaches) and, more generally, evaluating the suitability of each approach to the specific data at hand. This paper accompanies the public release of DysmalPy., Comment: 38 pages, 21 figures, accepted for publication in ApJ

We present BALDUR, a novel Bayesian algorithm designed to deal with multi-modal datasets and small sample sizes in high-dimensional settings while providing explainable solutions. To do so, the proposed model combines within a common latent space the different data views to extract the relevant information to solve the classification task and prune out the irrelevant/redundant features/data views. Furthermore, to provide generalizable solutions in small sample size scenarios, BALDUR efficiently integrates dual kernels over the views with a small sample-to-feature ratio. Finally, its linear nature ensures the explainability of the model outcomes, allowing its use for biomarker identification. This model was tested over two different neurodegeneration datasets, outperforming the state-of-the-art models and detecting features aligned with markers already described in the scientific literature., Comment: 36 pages, 3 figures and 3 tables

Low employment rates in Latin America have contributed to a substantial rise in crime, prompting the emergence of new criminal tactics. For instance, "express robbery" has become a common crime committed by armed thieves, in which they drive motorcycles and assault people in public in a matter of seconds. Recent research has approached the problem by embedding weapon detectors in surveillance cameras; however, these systems are prone to false positives if no counterpart confirms the event. In light of this, we present a distributed IoT system that integrates a computer vision pipeline and object detection capabilities into multiple end-devices, constantly monitoring for the presence of firearms and sharp weapons. Once a weapon is detected, the end-device sends a series of frames to a cloud server that implements a 3DCNN to classify the scene as either a robbery or a normal situation, thus minimizing false positives. The deep learning process to train and deploy weapon detection models uses a custom dataset with 16,799 images of firearms and sharp weapons. The best-performing model, YOLOv5s, optimized using TensorRT, achieved a final mAP of 0.87 running at 4.43 FPS. Additionally, the 3DCNN demonstrated 0.88 accuracy in detecting abnormal situations. Extensive experiments validate that the proposed system significantly reduces false positives while autonomously monitoring multiple locations in real-time., Comment: Accepted for publication in the proceedings of the 37th Conference on Graphics, Patterns and Images (SIBGRAPI 2024)

Galaxy clusters identified in optical imaging surveys suffer from projection effects: physically unassociated galaxies along a cluster's line of sight can be counted as its members and boost the observed richness (the number of cluster members). To model the impact projection on cluster cosmology analyses, we apply a halo occupation distribution model to N-body simulations to simulate the red galaxies contributing to cluster members, and we use the number of galaxies in a cylinder along the line-of-sight (counts-in-cylinders) to model the impact of projection on cluster richness. We compare three projection models: uniform, quadratic, and Gaussian, and we convert between them by matching their effective cylinder volumes. We validate our mock catalogs using SDSS redMaPPer data vectors, including cluster abundance vs. richness, stacked lensing signal, spectroscopic redshift distribution of member galaxies, and richness re-measured on a redshift grid. We find the former two are insensitive to the projection model, while the latter two favor a quadratic projection model with a width of 180 Mpc/h (equivalent to the volume of a uniform model with a width of 100 Mpc/h and a Gaussian model with a width of 110 Mpc/h, or a Gaussian redshift error of 0.04). Our framework provides an efficient and flexible way to model optical cluster data vectors, paving the way for a simulation-based joint analysis for clusters, galaxies, and shear., Comment: 12 pages, 9 figures

We report on XMM-Newton, NuSTAR, and NICER X-ray observations of CTCV J2056-3014, a cataclysmic variable (CV) with one of the fastest-spinning white dwarfs (WDs) at P = 29.6 s. While previously classified as an intermediate polar (IP), CJ2056 also exhibits the properties of WZ-Sge-type CVs, such as dwarf novae and superoutbursts. With XMM-Newton and NICER, we detected the spin period up to approximately 2 keV with 7-$\sigma$ significance. We constrained its derivative to |$\dot{P}$| < 1.8e-12 s/s after correcting for binary orbital motion. The pulsed profile is characterized by a single broad peak with approximately 25% modulation. NuSTAR detected a four-fold increase in unabsorbed X-ray flux coincident with an optical flare in November 2022. The XMM-Newton and NICER X-ray spectra in 0.3-10 keV are best characterized by an absorbed optically-thin three-temperature thermal plasma model (kT = 0.3, 1.0, and 4.9 keV), while the NuSTAR spectra in 3-30 keV are best fit by a single-temperature thermal plasma model (kT = 8.4 keV), both with Fe abundance $Z_{Fe}/Z_\odot$ = 0.3. CJ2056 exhibits similarities to other fast-spinning CVs, such as low plasma temperatures, and no significant X-ray absorption at low energies. As the WD's magnetic field strength is unknown, we applied both non-magnetic and magnetic CV spectral models (MKCFLOW and MCVSPEC) to determine the WD mass. The derived WD mass range (M = 0.7-1.0 $M_\odot$) is above the centrifugal break-up mass limit of 0.56 $M_\odot$ and consistent with the mean WD mass of local CVs (M $\approx$ 0.8-0.9 $M_\odot$)., Comment: 22 pages, 12 figures, accepted to ApJ

We develop a theoretical framework and use two-dimensional hydrodynamical simulations to study the repulsive effect between two close orbiters embedded in an accretion disk. We consider orbiters on fixed Keplerian orbits with masses low enough to open shallow gaps. The simulations indicate that the repulsion is larger for more massive orbiters and decreases with the orbital separation and the disk's viscosity. We use two different assumptions to derive theoretical scaling relations for the repulsion. A first scenario assumes that each orbiter absorbs the angular momentum deposited in its horseshoe region by the companion's wake. A second scenario assumes that the corotation torques of the orbiters are modified because the companion changes the underlying radial gradient of the disk surface density. We find a substantial difference between the predictions of these two scenarios. The first one fails to reproduce the scaling of the repulsion with the disk viscosity and generally overestimates the strength of the repulsion. The second scenario, however, gives results that are broadly consistent with those obtained in the simulations., Comment: 18 pages, 15 figures, accepted for publication in ApJ

Building on work of J. Robinson and A. Shlapentokh, we develop a general framework to obtain definability and decidability results of large classes of infinite algebraic extensions of $\mathbb{F}_p(t)$. As an application, we show that for every odd rational prime $p$ there exist infinitely many primes $r$ such that the fields $\mathbb{F}_{p^a}\left(t^{r^{-\infty}}\right)$ have undecidable first-order theory in the language of rings without parameters. Our method uses character theory to construct families of non-isotrivial elliptic curves whose Mordell-Weil group is finitely generated and of positive rank in $\mathbb{Z}_r$-towers., Comment: 28 pages

Integral field spectroscopy (IFS) is a powerful tool for understanding the formation of galaxies across cosmic history. We present the observing strategy and first results of MSA-3D, a novel JWST program using multi-object spectroscopy in a slit-stepping strategy to produce IFS data cubes. The program observed 43 normal star-forming galaxies at redshifts $0.5 \lesssim z \lesssim 1.5$, corresponding to the epoch when spiral thin-disk galaxies of the modern Hubble sequence are thought to emerge, obtaining kpc-scale maps of rest-frame optical nebular emission lines with spectral resolution $R\simeq2700$. Here we describe the multiplexed slit-stepping method which is $>15$ times more efficient than the NIRSpec IFS mode for our program. As an example of the data quality, we present a case study of an individual galaxy at $z=1.104$ (stellar mass $M_{*} = 10^{10.3}~M_{\odot}$, star formation rate~$=3~M_{\odot}$ yr$^{-1}$) with prominent face-on spiral structure. We show that the galaxy exhibits a rotationally supported disk with moderate velocity dispersion ($\sigma = 36^{+5}_{-4}$~\kms), a negative radial metallicity gradient ($-0.020\pm0.002$~dex\,kpc$^{-1}$), a dust attenuation gradient, and an exponential star formation rate density profile which closely matches the stellar continuum. These properties are characteristic of local spirals, indicating that mature galaxies are in place at $z\sim1$. We also describe the customized data reduction and original cube-building software pipelines which we have developed to exploit the powerful slit-stepping technique. Our results demonstrate the ability of JWST slit-stepping to study galaxy populations at intermediate to high redshifts, with data quality similar to current surveys of the $z\sim0.1$ universe., Comment: 24 pages, 12 figures, 1 table; submitted to ApJ, comments welcome. Our custom designed data reduction pipeline (MSA3D) and example processed data-cube will be publicly released before the JWST Cycle 4 deadline. MSA3D package on GitHub: https://github.com/barisiciv/msa3d.git

Quasinormal modes of rapidly rotating black holes are crucial in understanding the ringdown phase after a merger. While for Kerr black holes these modes have been known for a long time, their calculation has remained a challenge in alternative theories of gravity. We obtain the spectrum of quasinormal modes of rapidly rotating black holes in Einstein-Gauss-Bonnet-dilaton theory without resorting to perturbation theory in the coupling constant. Our approach is based on a spectral decomposition of the linear perturbations of the metric and the scalar field. The quasinormal modes approach smoothly the slow rotation and weak coupling limits known from perturbative studies., Comment: 5 pages, 3 figures

Given a unitary operator in a finite dimensional complex Hilbert space, its unitary contraction to a subspace is defined. The application to quantum graphs is discussed. It is shown how the contraction allows to generate the scattering matrices of new quantum graphs from assembling of simpler graphs. The contraction of quantum channels is also defined. The implementation of the quantum gates corresponding to the contracted unitary operator is investigated, although no explicit construction is presented. The situation is different for the contraction of quantum channels for which explicit implementations are given., Comment: 16 pages, 8 figures

We aim at characterizing the population of low-luminosity X-ray sources in the Galactic plane by studying their X-ray spectra and periodic signals in the light curves. We are performing an X-ray survey of the Galactic disk using XMM-Newton, and the source XMMU J173029.8-330920 was serendipitously discovered in our campaign. We performed a follow-up observation of the source using our pre-approved NuSTAR target of opportunity time. We used various phenomenological models in xspec for the X-ray spectral modeling. We also computed the Lomb-Scargle periodogram to search for X-ray periodicity. A Monte Carlo method was used to simulate 1000 artificial light curves to estimate the significance of the detected period. We also searched for X-ray, optical, and infrared counterparts of the source in various catalogs. The spectral modeling indicates the presence of an intervening cloud with $N_{\rm H}\sim(1.5-2.3)\times10^{23}\ \rm cm^{-2}$ that partially absorbs the incoming X-ray photons. The X-ray spectra are best fit by a model representing emission from a collisionally ionized diffuse gas with plasma temperature $kT=26^{+11}_{-5}$ keV. Furthermore, an Fe $K_{\alpha}$ line at $6.47^{+0.13}_{-0.06}$ keV was detected with an equivalent width of the line of $312\pm104$ eV. We discovered a coherent pulsation with a period of $521.7\pm0.8$ s. The 3-10 keV pulsed fraction of the source is around $\sim$50-60\%. The hard X-ray emission with plasma temperature $kT=26^{+11}_{-5}$ keV, iron $K_{\alpha}$ emission at 6.4 keV and a periodic behavior of $521.7\pm0.8$ s suggest XMMU J173029.8-33092 to be an intermediate polar. We estimated the mass of the central white dwarf to be $0.94-1.4\ M_{\odot}$ by assuming a distance to the source of $\sim1.4-5$ kpc., Comment: 7 pages, 4 figures, accepted for publication in A&A

This paper investigates the dynamics of cosmological models incorporating nonlinear electrodynamics (NLED), focusing on their stability and causality. We explore two specific NLED models: the Power-Law and Rational Lagrangians. We assess these models' viability in describing the universe's evolution using dynamical systems theory and Bayesian inference. We present the theoretical framework of NLED coupled with general relativity, followed by an analysis of the stability and causality through the squared sound speed of the NLED Lagrangians. We then conduct a detailed dynamical analysis to identify the universe's evolution with this matter content. Our results show that the Power-Law Lagrangian model transitions through various cosmological phases from a Maxwell radiation-dominated state to a matter-dominated state. For the Rational Lagrangian model, including the Maxwell term, stable and causal behavior is observed within specific parameter ranges, with critical points indicating the evolutionary pathways of the universe. To validate our theoretical findings, we perform Bayesian parameter estimation using a comprehensive set of observational data, including cosmic chronometers, Baryon Acoustic Oscillation (BAO) measurements, and Type Ia Supernovae (SNeIa). The estimated parameters for both models align with expected values for the current universe, particularly the matter density $\Omega_m$ and the Hubble parameter $h$. However, the parameters $\alpha$ and $b$ are not tightly constrained within the prior ranges. Our model comparison strongly favors the $\Lambda$CDM model over the NLED models for late-universe observations, as the NLED model does not exhibit a cosmological constant behavior. Our results highlight the need for further refinement and exploration of NLED-based cosmological models to fully integrate them into the standard cosmological framework., Comment: 45 pages, 17 figures and one table

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However, the latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous and limited annotated data. This paper reviews how AI is being used to analyze extreme events (like floods, droughts, wildfires and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating information in real-time, deploying models, and making them understandable, all crucial for gaining the trust of stakeholders and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy for analyzing and predicting extreme events. Such collaborative efforts aim to enhance disaster readiness and disaster risk reduction.

For the joint analysis of second-order weak lensing and galaxy clustering statistics, so-called $3{\times}2$ analyses, the selection and characterization of optimal galaxy samples is a major area of research. One promising choice is to use the same galaxy sample as lenses and sources, which reduces the systematics parameter space that describes uncertainties related to galaxy samples. Such a "lens-equal-source" analysis significantly improves self-calibration of photo-z systematics leading to improved cosmological constraints. With the aim to enable a lens-equal-source analysis on small scales we investigate the halo-galaxy connection of DES-Y3 source galaxies. We develop a technique to construct mock source galaxy populations by matching COSMOS/UltraVISTA photometry onto UniverseMachine galaxies. These mocks predict a source halo occupation distribution (HOD) that exhibits significant redshift evolution, non-trivial central incompleteness and galaxy assembly bias. We produce multiple realizations of mock source galaxies drawn from the UniverseMachine posterior with added uncertainties in measured DES photometry and galaxy shapes. We fit a modified HOD formalism to these realizations to produce priors on the galaxy-halo connection for cosmological analyses. We additionally train an emulator that predicts this HOD to $\sim2\%$ accuracy from redshift $z = 0.1 - 1.3$ that models the dependence of this HOD on 1) observational uncertainties in galaxy size and photometry, and 2) uncertainties in the UniverseMachine predictions., Comment: 15 pages, 7 figures, 1 table. Submitted to The Astrophysical Journal

We investigate the planetary migration of low-mass planets ($M_p\in[1,15]M_\oplus$, here $M_\oplus$ is the Earth mass) in a gaseous disc containing a previously formed gap. We perform high-resolution 3D simulations with the FARGO3D code. To create the gap in the surface density of the disc, we use a radial viscosity profile with a bump, which is maintained during the entire simulation time. We find that when the gap is sufficiently deep, the spiral waves excited by the planet trigger the Rossby wave instability, forming cyclonic (underdense) vortices at the edges of the gap. When the planet approaches the gap, it interacts with the vortices, which produce a complex flow structure around the planet. Remarkably, we find a widening of the horseshoe region of the planet produced by the vortex at the outer edge of the gap, which depending on the mass of the planet differs by at least a factor of two with respect to the standard horseshoe width. This inevitably leads to an increase in the corotation torque on the planet and produces an efficient trap to halt its inward migration. In some cases, the planet becomes locked in corotation with the outer vortex. Under this scenario, our results could explain why low-mass planets do not fall towards the central star within the lifetime of the protoplanetary disc. Lastly, the development of these vortices produces an asymmetric temporal evolution of the gap, which could explain the structures observed in some protoplanetary discs., Comment: 17 pages, 20 figures, accepted for publication in MNRAS

{We herein report on the calculation of thermoluminescence (TL) kinetic parameters determined from the TL emission of synthetic GdAlO3 (GAO) phosphors prepared by the co-precipitation method. The sample, characterized by means of X-ray diffraction with an orthorhombic phase structure (space group Pnma (62), shows complex glow curves consisting of at least four groups of components peaked at 100, 140, 240, and 290 {\deg}C where the two lower overlapped temperature peaks are difficult to identify using the $T_M-T_{stop}$. The coexistence of a continuum in the trap distribution (linked to the lower temperature peaks) and a discrete trap system (associated with the components at temperatures higher than 200 {\deg}C) can be distinguished. The estimation of the TL kinetic parameters is performed using GlowFit, TLAnal, the spreadsheet Origin, Computing Glow Curve Deconvolution (CGCD), and various heating rate (VHR) methods. However, only CGCD appears as the suitable technique for such purpose since it provides information on the TL physical process supported by mathematical models based on a linear combination of functions related to the First Order Kinetic approach., Comment: 27 pages, 6 figures and 1 table

We have developed an in-situ index of refraction profile n(z) for cold polar ice, using the transit times of radio signals broadcast from an englacial transmitter to 2-5 km distant radio-frequency receivers, deployed at depths up to 200 m. For propagation through a non-uniform medium, Maxwell's equations generally admit two ray propagation solutions from a given transmitter, corresponding to a direct path (D) and a refracted or reflected path (R); the measured D vs. R timing differences (dt(D,R)) are determined by the refractive index profile. We constrain n(z) near South Pole, where the Askaryan Radio Array (ARA) neutrino observatory is located, by simulating D and R ray paths via ray tracing and comparing simulations to measured dt(D,R) values. Using previous ice density data as a proxy for n(z), we demonstrate that our data strongly favors a glaciologically-motivated three-phase densification model rather than a single exponential scale height model. Effective volume simulations for a detector of ARA station antenna depths yield a 14\% increase in neutrino sensitivity over a range of $10^{17} - 10^{21}$ eV using the three-phase model compared to a single exponential.

Quaternions, discovered by Sir William Rowan Hamilton in the 19th century, are a significant extension of complex numbers and a profound tool for understanding three-dimensional rotations. This work explores the quaternion's history, algebraic structure, and educational implications. We begin with the historical context of quaternions, highlighting Hamilton's contributions and the development of quaternion theory. This sets the stage for a detailed examination of quaternion algebra, including their representations as complex numbers, matrices, and non-commutative nature. Our research presents some advancements compared to previous educational studies by thoroughly examining quaternion applications in rotations. We differentiate between left and right rotations through detailed numerical examples and propose a general approach to rotations via a theorem, clearly defining the associated morphism. This framework enhances the understanding of the algebraic structure of quaternions. A key innovation is presenting a three-dimensional example illustrating the rotation of a frame with strings, connecting quaternions to the quaternion group, half-integer spin phenomena, and Pauli matrices. This approach bridges theoretical concepts with practical applications, enriching the understanding of quaternions in scientific contexts. We emphasize the importance of incorporating the history and applications of quaternions into educational curricula to enhance student comprehension and interest. By integrating historical context and practical examples, we aim to make complex mathematical concepts more accessible and engaging for students at the undergraduate and graduate levels. Our study underscores the enduring relevance of quaternions in various scientific and technological fields and highlights the potential for future research and educational innovations., Comment: This work is part of a PhD thesis in physics education. 31 pages and 13 figures

The last few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime and the canonical two-point (2pt) statistics. We test and benchmark some of these new techniques in a community data challenge "Beyond-2pt", initiated during the Aspen 2022 Summer Program "Large-Scale Structure Cosmology beyond 2-Point Statistics," whose first round of results we present here. The challenge dataset consists of high-precision mock galaxy catalogs for clustering in real space, redshift space, and on a light cone. Participants in the challenge have developed end-to-end pipelines to analyze mock catalogs and extract unknown ("masked") cosmological parameters of the underlying $\Lambda$CDM models with their methods. The methods represented are density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using both EFT and simulation-based inference. In this work, we review the results of the challenge, focusing on problems solved, lessons learned, and future research needed to perfect the emerging beyond-2pt approaches. The unbiased parameter recovery demonstrated in this challenge by multiple statistics and the associated modeling and inference frameworks supports the credibility of cosmology constraints from these methods. The challenge data set is publicly available and we welcome future submissions from methods that are not yet represented., Comment: New submissions welcome! Challenge data available at https://github.com/ANSalcedo/Beyond2ptMock

In our quest to understand the generation of cosmological perturbations, we face two serious obstacles: we do not have direct information about the environment experienced by primordial perturbations during inflation, and our observables are practically limited to correlators of massless fields, heavier fields and derivatives decaying exponentially in the number of e-foldings. The flexible and general framework of open systems has been developed precisely to face similar challenges. Building on previous work, we develop a Schwinger-Keldysh path integral description for an open effective field theory of inflation, describing the possibly dissipative and non-unitary evolution of the Goldstone boson of time translations interacting with an unspecified environment, under the key assumption of locality in space and time. Working in the decoupling limit, we study the linear and interacting theory in de Sitter and derive predictions for the power spectrum and bispectrum that depend on a finite number of effective couplings organised in a derivative expansion. The smoking gun of interactions with the environment is an enhanced but finite bispectrum close to the folded kinematical limit. We demonstrate the generality of our approach by matching our open effective theory to an explicit model. Our construction provides a standard model to simultaneously study phenomenological predictions as well as quantum information aspects of the inflationary dynamics., Comment: 56 pages without appendices (82 pages in total), 12 figures, 2 tables; matches published version in JHEP

We consider slowly rotating Ellis-Bronnikov wormholes and investigate their radial perturbations ($\mathrm{l}=0$), expanding up to second order in rotation. We present the detailed derivations in the general case, including symmetric and non-symmetric wormholes. The calculations show that the unstable mode present in the static case becomes less unstable with increasing rotation, until it reaches zero and then disappears. This indicates that wormhole solutions may become linearly mode stable at sufficiently fast rotation., Comment: 27 pages, 8 figures

This paper presents the Relevance Feature and Vector Machine (RFVM), a novel model that addresses the challenges of the fat-data problem when dealing with clinical prospective studies. The fat-data problem refers to the limitations of Machine Learning (ML) algorithms when working with databases in which the number of features is much larger than the number of samples (a common scenario in certain medical fields). To overcome such limitations, the RFVM incorporates different characteristics: (1) A Bayesian formulation which enables the model to infer its parameters without overfitting thanks to the Bayesian model averaging. (2) A joint optimisation that overcomes the limitations arising from the fat-data characteristic by simultaneously including the variables that define the primal space (features) and those that define the dual space (observations). (3) An integrated prunning that removes the irrelevant features and samples during the training iterative optimization. Also, this last point turns out crucial when performing medical prospective studies, enabling researchers to exclude unnecessary medical tests, reducing costs and inconvenience for patients, and identifying the critical patients/subjects that characterize the disorder and, subsequently, optimize the patient recruitment process that leads to a balanced cohort. The model capabilities are tested against state-of-the-art models in several medical datasets with fat-data problems. These experimental works show that RFVM is capable of achieving competitive classification accuracies while providing the most compact subset of data (in both terms of features and samples). Moreover, the selected features (medical tests) seem to be aligned with the existing medical literature., Comment: 19 pages of main text, 12 pages of appendices, 2 figures and 5 tables

In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.

Lipolysis is a life-essential metabolic process, which supplies fatty acids stored in lipid droplets to the body in order to match the demands of building new cells and providing cellular energy. In this paper, we present a first mathematical modelling approach for lipolysis, which takes into account that the involved enzymes act on the surface of lipid droplets. We postulate an active region near the surface where the substrates are within reach of the surface-bound enzymes and formulate a system of reaction-diffusion PDEs, which connect the active region to the inner core of lipid droplets via interface conditions. We establish two numerical discretisations based on finite element method and isogeometric analysis, and validate them to perform reliably. For numerical testing purposes, we introduce and analyse a testing model featuring a nontrivial explicit stationary state solution, which describes beside lipolysis also a reverse process (in a physiologically oversimplified way). We prove the unique existence of global and equilibrium solutions. We establish exponential convergence to the equilibrium solutions using the entropy method. We then study the stationary state model and compute explicitly for radially symmetric solutions. Concerning the finite element methods, we show numerically the linear and quadratic convergence of the errors with respect to the $H^{1}$- and $L^{2}$-norms, respectively. Finally, we present numerical simulations of a prototypical PDE model of lipolysis and illustrate that ATGL clustering on lipid droplets can significantly slow down lipolysis., Comment: 27 pages, 18 figures

Feature selection in noisy label scenarios remains an understudied topic. We propose a novel genetic algorithm-based approach, the Noise-Aware Multi-Objective Feature Selection Genetic Algorithm (NMFS-GA), for selecting optimal feature subsets in binary classification with noisy labels. NMFS-GA offers a unified framework for selecting feature subsets that are both accurate and interpretable. We evaluate NMFS-GA on synthetic datasets with label noise, a Breast Cancer dataset enriched with noisy features, and a real-world ADNI dataset for dementia conversion prediction. Our results indicate that NMFS-GA can effectively select feature subsets that improve the accuracy and interpretability of binary classifiers in scenarios with noisy labels.

In this paper, we simulate diffusion bridges by using an approximation of the Wiener-chaos expansion (WCE), or a Fourier-Hermite expansion, for a related diffusion process. Indeed, we consider the solution of stochastic differential equations, and we apply the WCE to a particular representation of the diffusion bridge. Thus, we obtain a method to simulate the proposal diffusion bridges that is fast and that in every attempt constructs a diffusion bridge, which means there are no rejection rates. The method presented in this work could be very useful in statistical inference. We validate the method with a simple Ornstein-Uhlenbeck process. We apply our method to three examples of SDEs and show the numerical results., Comment: 21 pages, 5 figures, 1 algorithm

This paper proposes two realistic and biologically viable methods for Wolbachia-based biocontrol of Aedes aegypti mosquitoes, assuming imperfect maternal transmission of the Wolbachia bacterium, incomplete cytoplasmic incompatibility, and direct loss of Wolbachia infection caused by thermal stress. Both methods are based on optimization approaches and allow for the stable persistence of Wolbachia-infected mosquitoes in the wild Ae. aegypti populations in a minimum time and using the smallest quantity of Wolbachia-carrying insects to release. The first method stems from the continuous-time optimal release strategy, which is further transformed into a sequence of suboptimal impulses mimicking instantaneous releases of Wolbachia-carrying insects. The second method constitutes a novel alternative to all existing techniques aimed at the design of release strategies. It is developed using metaheuristics ($\epsilon$-constraint method combined with the genetic algorithm) and directly produces a discrete sequence of decisions, where each element represents the quantity of Wolbachia-carrying mosquitoes to be released instantaneously and only once per a specified time unit. It turns out that a direct discrete-time optimization (second method) renders better quantifiable results compared to transforming a continuous-time optimal release function into a sequence of suboptimal impulses (first method). As an illustration, we provide examples of daily, weekly, and fortnightly release strategies designed by both methods for two Wolbachia strains, wMel and wMelPop., Comment: 28 pages, 9 figures

We present for the first time a study of the quasinormal modes of rapidly rotating Ellis-Bronnikov wormholes in General Relativity. We compute the spectrum of the wormholes using a spectral decomposition of the metric perturbations on a numerical background. We focus on the $M_z=2,3$ sector of the perturbations, and show that the triple isospectrality of the symmetric and static Ellis-Bronnikov wormhole is broken due to rotation, giving rise to a much richer spectrum than the spectrum of Kerr black holes. We do not find any instabilities for $M_z=2,3$ perturbations., Comment: 19 pages, 4 figures, 3 tables; v2: equations added, results unchanged, matches published version

Galaxy clusters identified with optical imaging tend to suffer from projection effects, which impact richness (the number of member galaxies in a cluster) and lensing coherently. Physically unassociated galaxies can be mistaken as cluster members due to the significant uncertainties in their line-of-sight distances, thereby changing the observed cluster richness; at the same time, projection effects alter the weak gravitational lensing signals of clusters, leading to a correlated scatter between richness and lensing at a given halo mass. As a result, the lensing signals for optically selected clusters tend to be biased high. This optical selection bias problem of cluster lensing is one of the key challenges in cluster cosmology. Fortunately, recently available multiwavelength observations of clusters provide a solution. We analyze a simulated data set mimicking the observed lensing of clusters identified by both optical photometry and gas properties, aiming to constrain this selection bias. Assuming a redMaPPer sample from the Dark Energy Survey with South Pole Telescope Sunyaev-Zeldovich effect observations, we find that an overlapping survey of 1300 square deg, 0.2 < z < 0.65, can constrain the average lensing bias to an accuracy of 5 percent. This provides an exciting opportunity for directly constraining optical selection bias from observations. We further show that our approach can remove the optical selection bias from the lensing signal, paving the way for future optical cluster cosmology analyses., Comment: 16 pages, 5 figures. Submitted to PRD

We report a new method to calculate the quasinormal modes of rotating black holes, using a spectral decomposition to solve the partial differential equations that result from introducing linear metric perturbations to a rotating background. Our approach allows us to calculate a large sector of the quasinormal mode spectrum. In particular, we study the accuracy of the method for the $(l=2)$-led and $(l=3)$-led modes for different values of the $M_z$ azimuthal number, considering the fundamental modes as well as the first two excitations. We show that our method reproduces the Kerr fundamental modes with an accuracy of $10^{-6}$ or better for $a/M<0.8$, while it stays below $0.1\%$ for $a/M<0.98$., Comment: 18 pages, 5 figures. v2: Minor changes to match published version