Search

A novel liquid argon purity monitor based on a 207 Bi radioactive source, emitting monochromatic internal-conversion electrons, is presented. This new monitor allows for a very precise and fast measurement of the electronegative impurities concentration in liquid argon. It can be operated continuously in liquid argon TPC experiments without interfering with the main detector operation. Different drift lengths can be assembled for the proposed device, to assess a large range of liquid argon purities while minimizing systematic uncertainties. Two prototypes have been built and successfully operated in dedicated test stands. The results and performance are reported., Comment: 11 pages, 8 figures, 12 references

In this work, we focus on the early design phase of cruise ship hulls, where the designers are tasked with ensuring the structural resilience of the ship against extreme waves while reducing steel usage and respecting safety and manufacturing constraints. The ship's geometry is already finalized and the designer can choose the thickness of the primary structural elements, such as decks, bulkheads, and the shell. Reduced order modeling and black-box optimization techniques reduce the use of expensive finite element analysis to only validate the most promising configurations, thanks to the efficient exploration of the domain of decision variables. However, the quality of the results heavily relies on the problem formulation, and on how the structural elements are assigned to the decision variables. A parameterization that does not capture well the stress configuration of the model prevents the optimization procedure from achieving the most efficient allocation of the steel. To address this issue, we extended an existing pipeline for the structural optimization of cruise ships developed in collaboration with Fincantieri S.p.A. with a novel data-driven reparameterization procedure, based on the optimization of a series of sub-problems. Moreover, we implemented a multi-objective optimization module to provide the designers with insights into the efficient trade-offs between competing quantities of interest and enhanced the single-objective Bayesian optimization module. The new pipeline is tested on a simplified midship section and a full ship hull, comparing the automated reparameterization to a baseline model provided by the designers. The tests show that the iterative refinement outperforms the baseline on the more complex hull, proving that the pipeline streamlines the initial design phase, and helps the designers tackle more innovative projects.

The current observational tensions in the standard cosmological model have reinforced the research on dynamical dark energy, in particular on models with non-gravitational interaction between the dark components. Late-time observables like type Ia supernovas (SNe Ia) and large-scale structures (LSS) point to an energy flux from dark energy to dark matter, while the anisotropy spectrum of the cosmic microwave background (CMB) points to a small flux from dark matter to dark energy, fully consistent with no interaction at all. As background and visible matter tests are insensitive to the suppression/enhancement in the dark matter power spectrum, which is a characteristic of interacting models, while the CMB spectrum is strongly affected by it, this could be the origin of those results. In order to confirm it and at the same time to rule out the role of possible systematics between early and late-time observations, the use of a low redshift observable sensitive to the gravitational potential generated by dark matter is crucial. In the present paper, we investigate the observational viability of a class of interacting dark energy models, namely with energy exchange between vacuum-type and dust components, in the light of the Dark Energy Survey (DES) observations of galaxy weak lensing, in the context of a spatially-flat Friedmann-Lema\^itre-Robertson-Walker spacetime. The best fit of our analysis is compatible with null interaction, with a weak preference for an energy flux from dark matter to dark energy, confirming the CMB based constraints., Comment: 12 pages, 4 figures

GRAMS (Gamma-Ray and AntiMatter Survey) is a next-generation balloon/satellite experiment utilizing a LArTPC (Liquid Argon Time Projection Chamber), to simultaneously target astrophysical observations of cosmic MeV gamma-rays and conduct an indirect dark matter search using antimatter. While LArTPCs are widely used in particle physics experiments, they have never been operated at balloon altitudes. An engineering balloon flight with a small-scale LArTPC (eGRAMS) was conducted on July 27th, 2023, to establish a system for safely operating a LArTPC at balloon altitudes and to obtain cosmic-ray data from the LArTPC. The flight was launched from the Japan Aerospace Exploration Agency's (JAXA) Taiki Aerospace Research Field in Hokkaido, Japan. The total flight duration was 3 hours and 12 minutes, including a level flight of 44 minutes at a maximum altitude of 28.9~km. The flight system was landed on the sea and successfully recovered. The LArTPC was successfully operated throughout the flight, and about 0.5 million events of the cosmic-ray data including muons, protons, and Compton scattering gamma-ray candidates, were collected. This pioneering flight demonstrates the feasibility of operating a LArTPC in high-altitude environments, paving the way for future GRAMS missions and advancing our capabilities in MeV gamma-ray astronomy and dark matter research.

In the present work, we obtain and analyze a new class of analytical solutions of magnetically charged black bounces in k-essence theory, spherically symmetric in (3+1)-dimensions, coupled to nonlinear electrodynamics (NED). We consider two metric models, Simpson-Visser and Bardeen, for the k-essence configurations n = 1/3 and n = 1/5. We obtain in an analytical way which scalar field, field potential, and Lagrangian NED are necessary to support the metrics. We analyze the behavior of these quantities and the energy conditions due to the scalar field and the NED.

A Newtonian-like theory inspired by the Brans-Dicke gravitational Lagrangian has been recently proposed in Ref. arXiv:2009.04434(v4). We propose here a new variant of this theory such that the usual Newtonian second law is preserved. The cosmological solutions are analysed and accelerated cosmic expansion can be obtained in pure matter dominated universes. This happens due to the dynamical gravitational coupling of the introduced time varying field. We also analyse the matter density perturbations and find they exhibit an enhanced growth in comparison with the usual Newtonian like behavior in Einstein-de Sitter model., Comment: 11 pages, 5 figures

We briefly review the analytical continuation method for determining quasinormal modes (QNMs) and the associated frequencies in open systems. We explore two exactly solvable cases based on the P\"oschl-Teller potential to show that the analytical continuation method cannot determine the full set of QNMs and frequencies of a given problem starting from the associated bound state problem in Quantum Mechanics. The root of the problem is that many QNMs are the analytically continued counterparts of solutions that do not belong to the domain where the associated Schr\"odinger operator is self-adjoint, challenging the application of the method for determining full sets of QNMs. We illustrate these problems through the physically relevant case of BTZ black holes, where the natural domain of the problem is the negative real line., Comment: Review contribution to 'Frontiers Research Topic': Quasi-Normal Modes, Non-Selfadjoint Operators and Pseudospectrum: an Interdisciplinary Approach. Eds. J.L.Jaramillo and P. Bizon. 6 pages

Background and Objective: Diabetes presents a significant challenge to healthcare due to the negative impact of poor blood sugar control on health and associated complications. Computer simulation platforms, notably exemplified by the UVA/Padova Type 1 Diabetes simulator, has emerged as a promising tool for advancing diabetes treatments by simulating patient responses in a virtual environment. The UVA Virtual Lab (UVLab) is a new simulation platform to mimic the metabolic behavior of people with Type 2 diabetes (T2D) with a large population of 6062 virtual subjects. Methods: The work introduces the Distribution-Based Population Selection (DSPS) method, a systematic approach to identifying virtual subsets that mimic the clinical behavior observed in real trials. The method transforms the sub-population selection task into a Linear Programing problem, enabling the identification of the largest representative virtual cohort. This selection process centers on key clinical outcomes in diabetes research, such as HbA1c and Fasting plasma Glucose (FPG), ensuring that the statistical properties (moments) of the selected virtual sub-population closely resemble those observed in real-word clinical trial. Results: DSPS method was applied to the insulin degludec (IDeg) arm of a phase 3 clinical trial. This method was used to select a sub-population of virtual subjects that closely mirrored the clinical trial data across multiple key metrics, including glycemic efficacy, insulin dosages, and cumulative hypoglycemia events over a 26-week period. Conclusion: The DSPS algorithm is able to select virtual sub-population within UVLab to reproduce and predict the outcomes of a clinical trial. This statistical method can bridge the gap between large population simulation platforms and previously conducted clinical trials.

The use of double-faced, metallized, perforated PCB planes, segmented into strips for the anodic read-out of ionization signals in liquid argon TPCs, is emerging as a promising technology for charge readout in liquid argon TPCs used in large volume detectors.As a proof of concept, a prototype liquid Argon TPC hosting this new anode configuration based on single side perforated PCB planes has been constructed and exposed to cosmic rays at LNL in Italy. Tests were performed with both the metallized and insulating sides of the anode facing the drift volume, providing the first evidence of the focusing effect on drift electron trajectories through the PCB holes due to charge accumulation on the insulator surface., Comment: 12 pages, 13 figures

Electronic resonances are metastable states that can decay by electron loss. They are ubiquitous across various fields of science, such as chemistry, physics, and biology. However, current theoretical and computational models for resonances cannot yet rival the level of accuracy achieved by bound-state methodologies. Here, we generalize selected configuration interaction (SCI) to treat resonances using the complex absorbing potential (CAP) technique. By modifying the selection procedure and the extrapolation protocol of standard SCI, the resulting CAP-SCI method yields resonance positions and widths of full configuration interaction quality. Initial results for the shape resonances of \ce{N2-} and \ce{CO-} reveal the important effect of high-order correlation, which shifts the values obtained with CAP-augmented equation-of-motion coupled-cluster with singles and doubles by more than \SI{0.1}{\eV}. The present CAP-SCI approach represents a cornerstone in the development of highly-accurate methodologies for resonances., Comment: 10 pages, 2 figures (supporting information available)

We report the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on germanium, measured at the Spallation Neutron Source at Oak Ridge National Laboratory. The Ge-Mini detector of the COHERENT collaboration employs large-mass, low-noise, high-purity germanium spectrometers, enabling excellent energy resolution, and an analysis threshold of 1.5 keV electron-equivalent ionization energy. We observe a on-beam excess of 20.6$_{+7.1}^{-6.3}$ counts with a total exposure of 10.22 GWhkg and we reject the no-CEvNS hypothesis with 3.9 sigma significance. The result agrees with the predicted standard model of particle physics signal rate within 2 sigma., Comment: 7 pages, 5 figures

As Machine Learning grows in popularity across various fields, equity has become a key focus for the AI community. However fairness-oriented approaches are still underexplored in smart mobility. Addressing this gap, our study investigates the balance between performance optimization and algorithmic fairness in shared micromobility services providing a novel framework based on Reinforcement Learning. Exploiting Q-Learning, the proposed methodology achieves equitable outcomes in terms of the Gini index across different areas characterized by their distance from central hubs. Through vehicle rebalancing, the provided scheme maximizes operator performance while ensuring fairness principles for users, reducing iniquity by up to 80% while only increasing costs by 30% (w.r.t. applying no equity adjustment). A case study with synthetic data validates our insights and highlights the importance of fairness in urban micromobility., Comment: 6 pages, 2 figures, jointly submitted to IEEE L-CSS and ACC 2025

Electron-neutrino charged-current interactions with xenon nuclei were modeled in the nEXO neutrinoless double-beta decay detector (~5-tonne, 90% ${}^{136}$Xe, 10% ${}^{134}$Xe) to evaluate its sensitivity to supernova neutrinos. Predictions for event rates and detectable signatures were modeled using the MARLEY event generator. We find good agreement between MARLEY's predictions and existing theoretical calculations of the inclusive cross sections at supernova neutrino energies. The interactions modeled by MARLEY were simulated within the nEXO simulation framework and were run through an example reconstruction algorithm to determine the detector's efficiency for reconstructing these events. The simulated data, incorporating the detector response, were used to study the ability of nEXO to reconstruct the incident electron-neutrino spectrum and these results were extended to a larger xenon detector of the same isotope enrichment. We estimate that nEXO will be able to observe electron-neutrino interactions with xenon from supernovae as far as 5 to 8 kpc from earth, while the ability to reconstruct incident electron-neutrino spectrum parameters from observed interactions in nEXO is limited to closer supernovae., Comment: 17 pages, 16 figures

Autonomous intersection management (AIM) poses significant challenges due to the intricate nature of real-world traffic scenarios and the need for a highly expensive centralised server in charge of simultaneously controlling all the vehicles. This study addresses such issues by proposing a novel distributed approach to AIM utilizing multi-agent reinforcement learning (MARL). We show that by leveraging the 3D surround view technology for advanced assistance systems, autonomous vehicles can accurately navigate intersection scenarios without needing any centralised controller. The contributions of this paper thus include a MARL-based algorithm for the autonomous management of a 4-way intersection and also the introduction of a new strategy called prioritised scenario replay for improved training efficacy. We validate our approach as an innovative alternative to conventional centralised AIM techniques, ensuring the full reproducibility of our results. Specifically, experiments conducted in virtual environments using the SMARTS platform highlight its superiority over benchmarks across various metrics., Comment: 15 pages, 2 figures, submitted to Agents in Traffic and Transportation (ATT2024). Status: accepted

In the present study, we generalize the possible ghost field configurations within the framework of $k$-essence theory to the Simpson-Visser metric area function $\Sigma^2=x^2+a^2$. Our analysis encompasses field configurations for the region-defined metric function $dA_\pm$ as well as the general solution that asymptotically behaves as Schwarzschild-de Sitter for $x\to-\infty$. Specifically, we investigate two scalar field configurations and define the associated potential for each one. Through rigorous calculations, we verify that all equations of motion are satisfied. Notably, our findings indicate that even when proposing new configurations of ghost scalar fields, the energy conditions remain unchanged. This result serves to validate the wormhole solutions obtained in previous studies., Comment: 25 pages, 7 figures

Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancement of the field by highlighting shortcomings and proposing recommendations for improvement. In this work, we present a comprehensive analysis of fair ML datasets, demonstrating how unreflective yet common practices hinder the reach and reliability of algorithmic fairness findings. We systematically study protected information encoded in tabular datasets and their usage in 280 experiments across 142 publications. Our analyses identify three main areas of concern: (1) a \textbf{lack of representation for certain protected attributes} in both data and evaluations; (2) the widespread \textbf{exclusion of minorities} during data preprocessing; and (3) \textbf{opaque data processing} threatening the generalization of fairness research. By conducting exemplary analyses on the utilization of prominent datasets, we demonstrate how unreflective data decisions disproportionately affect minority groups, fairness metrics, and resultant model comparisons. Additionally, we identify supplementary factors such as limitations in publicly available data, privacy considerations, and a general lack of awareness, which exacerbate these challenges. To address these issues, we propose a set of recommendations for data usage in fairness research centered on transparency and responsible inclusion. This study underscores the need for a critical reevaluation of data practices in fair ML and offers directions to improve both the sourcing and usage of datasets.

The emergence of 6G-enabled Internet of Vehicles (IoV) promises to revolutionize mobility and connectivity, integrating vehicles into a mobile Internet-of-Things (IoT)-oriented wireless sensor network (WSN). 5G technologies and mobile edge computing further support this vision by facilitating real-time connectivity and empowering massive access to the Internet. In this context, IoT-oriented WSNs play a crucial role in intelligent transportation systems, offering affordable alternatives for traffic monitoring and management. This paper's contribution is twofold: (i) surveying state-of-the-art model-based techniques for efficient sensor selection in traffic flow monitoring, emphasizing challenges of sensor placement; and (ii) advocating for data-driven methodologies to enhance sensor deployment efficacy and traffic modeling accuracy. Further considerations underscore the importance of data-driven approaches for adaptive transportation systems aligned with the IoV paradigm., Comment: 5 pages, 0 figures, submitted to the EuCNC & 6G Summit, Antwerp, Belgium, 3-6 June, 2024

This brief paper provides an introduction to non-discrimination law in Europe. It answers the questions: What are the key characteristics of non-discrimination law in Europe, and how do the different statutes relate to one another? Our main target group is computer scientists and users of artificial intelligence (AI) interested in an introduction to non-discrimination law in Europe. Notably, non-discrimination law in Europe differs significantly from non-discrimination law in other countries, such as the US. We aim to describe the law in such a way that non-lawyers and non-European lawyers can easily grasp its contents and challenges. The paper shows that the human right to non-discrimination, to some extent, protects individuals against private actors, such as companies. We introduce the EU-wide non-discrimination rules which are included in a number of EU directives, and also explain the difference between direct and indirect discrimination. Significantly, an organization can be fined for indirect discrimination even if the company, or its AI system, discriminated by accident. The last section broadens the horizon to include bias-relevant law and cases from the GDPR, the EU AI Act, and related statutes. Finally, we give reading tips for those inclined to learn more about non-discrimination law in Europe., Comment: 10 pages

Deep Learning (DL) has made significant strides in various medical applications in recent years, achieving remarkable results. In the field of medical imaging, DL models can assist doctors in disease diagnosis by classifying pathologies in Chest X-ray images. However, training on new data to expand model capabilities and adapt to distribution shifts is a notable challenge these models face. Continual Learning (CL) has emerged as a solution to this challenge, enabling models to adapt to new data while retaining knowledge gained from previous experiences. Previous studies have analyzed the behavior of CL strategies in medical imaging regarding classification performance. However, when considering models that interact with sensitive information, such as in the medical domain, it is imperative to disaggregate the performance of socially salient groups. Indeed, DL algorithms can exhibit biases against certain sub-populations, leading to discrepancies in predictive performance across different groups identified by sensitive attributes such as age, race/ethnicity, sex/gender, and socioeconomic status. In this study, we go beyond the typical assessment of classification performance in CL and study bias evolution over successive tasks with domain-specific fairness metrics. Specifically, we evaluate the CL strategies using the well-known CheXpert (CXP) and ChestX-ray14 (NIH) datasets. We consider a class incremental scenario of five tasks with 12 pathologies. We evaluate the Replay, Learning without Forgetting (LwF), LwF Replay, and Pseudo-Label strategies. LwF and Pseudo-Label exhibit optimal classification performance, but when including fairness metrics in the evaluation, it is clear that Pseudo-Label is less biased. For this reason, this strategy should be preferred when considering real-world scenarios in which it is crucial to consider the fairness of the model.

Despite the critical importance of the medical domain in Deep Learning, most of the research in this area solely focuses on training models in static environments. It is only in recent years that research has begun to address dynamic environments and tackle the Catastrophic Forgetting problem through Continual Learning (CL) techniques. Previous studies have primarily focused on scenarios such as Domain Incremental Learning and Class Incremental Learning, which do not fully capture the complexity of real-world applications. Therefore, in this work, we propose a novel benchmark combining the challenges of new class arrivals and domain shifts in a single framework, by considering the New Instances and New Classes (NIC) scenario. This benchmark aims to model a realistic CL setting for the multi-label classification problem in medical imaging. Additionally, it encompasses a greater number of tasks compared to previously tested scenarios. Specifically, our benchmark consists of two datasets (NIH and CXP), nineteen classes, and seven tasks, a stream longer than the previously tested ones. To solve common challenges (e.g., the task inference problem) found in the CIL and NIC scenarios, we propose a novel approach called Replay Consolidation with Label Propagation (RCLP). Our method surpasses existing approaches, exhibiting superior performance with minimal forgetting.

In the realm of photochemistry, the significance of double excitations (also known as doubly-excited states), where two electrons are concurrently elevated to higher energy levels, lies in their involvement in key electronic transitions essential in light-induced chemical reactions as well as their challenging nature from the computational theoretical chemistry point of view. Based on state-of-the-art electronic structure methods (such as high-order coupled-cluster, selected configuration interaction, and multiconfigurational methods), we improve and expand our prior set of accurate reference excitation energies for electronic states exhibiting a substantial amount of double excitations [http://dx.doi.org/10.1021/acs.jctc.8b01205; Loos et al. J. Chem. Theory Comput. 2019, 15, 1939]. This extended collection encompasses 47 electronic transitions across 26 molecular systems that we separate into two distinct subsets: (i) 28 "genuine" doubly-excited states where the transitions almost exclusively involve doubly-excited configurations and (ii) 19 "partial" doubly-excited states which exhibit a more balanced character between singly- and doubly-excited configurations. For each subset, we assess the performance of high-order coupled-cluster (CC3, CCSDT, CC4, and CCSDTQ) and multiconfigurational methods (CASPT2, CASPT3, PC-NEVPT2, and SC-NEVPT2). Using as a probe the percentage of single excitations involved in a given transition ($\%T_1$) computed at the CC3 level, we also propose a simple correction that reduces the errors of CC3 by a factor of 3, for both sets of excitations. We hope that this more complete and diverse compilation of double excitations will help future developments of electronic excited-state methodologies., Comment: 25 pages, 3 figures (Supporting Information available)

Niobium carbide (NbC) is a high-field Type II superconductor with a critical temperature ($T_C$) of 11.1 K, just above that of pure Nb ($T_C = 9$ K). Downsizing NbC to the nanoparticle scale introduces significant alterations in its critical field and/or the superconducting temperature. Here we report on superconducting NbC nanoparticles with $T_C \approx$ 10 K synthesized by laser ablation in acetone, using the lens-target distance (laser fluence) and centrifugation as control parameters of the particle size. X-ray diffraction analyses certified the cubic NbC phase and electron microscopy images revealed spherical particles with average size near 8 nm, with no apparent size dependence on fluence. Besides, magnetization curves exhibited magnetic loops featuring a saturation magnetization around $10^{-3} \mu_B$/molecule along with a small and typical superconducting loop for all investigated samples. We also observed a suppression of the diamagnetic behavior below $T_C$ upon decreasing laser fluence. Moreover, all samples exhibited a weak electron spin resonance (ESR) Curie-like signal at $g\approx2.0$ probably associated with localized defects in the particle's surface. The intriguing coexistence of superconductivity and magnetism in nanoparticles has recently garnered significant research attention. This complex scenario and unique properties are due to the substantial increase of surface-to-volume ratio in these superconducting NbC nanoparticles and further investigation would be crucial to unveil novel material properties and shed new light on our understanding of the superconducting phenomenon in this new morphology.

Unimodular gravity became an object of increasing interest in the late $80$-ties and was recently used in primordial Universe modeling with cosmological constant, in the context of the Brans-Dicke gravity including scalar field. In the present article we investigate the possibility of imposing the unimodular condition within the $5$-dimensional Kaluza-Klein theory including the scalar field. The variational principle is formulated in $5$ dimensions first, and dimensional reduction is applied to the resulting set of equations. A cosmological model based on these equations is then presented and discussed., Comment: Latex file, 23 pages, 2 figures. Fit the published version

Gate-tunable transmons (gatemons) employing semiconductor Josephson junctions have recently emerged as building blocks for hybrid quantum circuits. In this study, we present a gatemon fabricated in planar Germanium. We induce superconductivity in a two-dimensional hole gas by evaporating aluminum atop a thin spacer, which separates the superconductor from the Ge quantum well. The Josephson junction is then integrated into an Xmon circuit and capacitively coupled to a transmission line resonator. We showcase the qubit tunability in a broad frequency range with resonator and two-tone spectroscopy. Time-domain characterizations reveal energy relaxation and coherence times up to 75 ns. Our results, combined with the recent advances in the spin qubit field, pave the way towards novel hybrid and protected qubits in a group IV, CMOS-compatible material.

Recently, a new distributed implementation of the full configuration interaction (FCI) method has been reported [Gao et al. J. Chem Theory Comput. 2024, 20, 1185]. Thanks to a hybrid parallelization scheme, the authors were able to compute the exact energy of propane (\ce{C3H8}) in the minimal basis STO-3G. This formidable task involves handling an active space of 26 electrons in 23 orbitals or a Hilbert space of \SI{1.3d12} determinants. This is, by far, the largest FCI calculation reported to date. Here, we illustrate how, from a general point of view, selected configuration interaction (SCI) can achieve microhartree accuracy at a fraction of the computational and memory cost, via a sparse exploration of the FCI space. The present SCI calculations are performed with the \textit{Configuration Interaction using a Perturbative Selection made Iteratively} (CIPSI) algorithm, as implemented in a determinant-driven way in the \textsc{quantum package} software. The present study reinforces the common wisdom that among the exponentially large number of determinants in the FCI space, only a tiny fraction of them significantly contribute to the energy. More importantly, it demonstrates the feasibility of achieving comparable accuracy using more reasonable and sustainable computational resources, hence reducing the ever-growing carbon footprint of computational chemistry., Comment: 5 pages, 2 figures (supporting information available)

The structure of unimodular gravity (UG) is invariant to a subclass of diffeomorphism, the transverse diffeomorphism, due to the unimodular condition ($\sqrt{-g}=\epsilon=cte$). Consequently, there is a freedom to define how the conservation laws of the energy-momentum tensor in unimodular gravity in the cosmological context. One of the main characteristics of the complete system of equations that describe cosmological dynamics in UG is that they form an underdetermined system if the usual conservation law of the energy-momentum tensor is not used in your structure, that is, it is necessary to insert extra information into the system to solve the complete set of equations. In this article, we propose the construction of a background cosmological model based on the description of a holographic dark energy component with a cutoff of the order of Ricci scalar in non-conservative UG. Although this choice is indeed a new addition of information to the cosmological system, the complete set of equations remains underdetermined, however, the new feature of this cosmological model is the appearance of an interaction between matter and dark energy. Indeed, this is a well-known characteristic of cosmological models in which we have holographic dark energy density. Consequently, we propose an ansatz to the interaction term $Q=\beta H \rho_{m}$, and obtain the cosmological parameters of our model. We found a viable universe model with similar characteristics to the $\Lambda \mathrm{CDM}$ model. We performed statistical analysis of the background model using the "Cosmic Chronometer" (CC) data for $H(z)$, and obtain as a result using Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC) as model selection criteria that $\Lambda \mathrm{CDM}$ prevails as the best model. However, the proposed model is competitive when compared to the cosmological model $\omega\mathrm{CDM}$.

A detailed knowledge of the decay properties of the so called Hoyle state in the $^{12}$C nucleus ($E_x=7.654$ MeV, $0^+$) is required to calculate the rate at which carbon is forged in typical red-giant stars. This paper reports on a new almost background-free measurement of the radiative decay branching ratio of the Hoyle state using advanced charged particle coincidence techniques. The exploitation, for the first time in a similar experiment, of a bidimensional map of the coincidence efficiency allows to reach an unitary value and, consequently, to strongly reduce sources of systematic uncertainties. The present results suggest a value of the radiative branching ratio of $\Gamma_{rad}/\Gamma_{tot}=4.2(6)\cdot10^{-4}$. This finding helps to resolve the tension between recent data published in the literature.

We reexamine $\Delta$CCSD, a state-specific coupled-cluster (CC) with single and double excitations (CCSD) approach that targets excited states through the utilization of non-Aufbau determinants. This methodology is particularly efficient when dealing with doubly excited states, a domain where the standard equation-of-motion CCSD (EOM-CCSD) formalism falls short. Our goal here is to evaluate the effectiveness of $\Delta$CCSD when applied to other types of excited states, comparing its consistency and accuracy with EOM-CCSD. To this end, we report a benchmark on excitation energies computed with the $\Delta$CCSD and EOM-CCSD methods, for a set of molecular excited-state energies that encompasses not only doubly excited states but also doublet-doublet transitions and (singlet and triplet) singly-excited states of closed-shell systems. In the latter case, we rely on a minimalist version of multireference CC known as the two-determinant CCSD method to compute the excited states. Our dataset, consisting of 276 excited states stemming from the \textsc{quest} database [V\'eril \textit{et al.}, \textit{WIREs Comput. Mol. Sci.} \textbf{2021}, 11, e1517], provides a significant base to draw general conclusions concerning the accuracy of $\Delta$CCSD. Except for the doubly-excited states, we found that $\Delta$CCSD underperforms EOM-CCSD. For doublet-doublet transitions, the difference between the mean absolute errors (MAEs) of the two methodologies (of \SI{0.10}{\eV} and \SI{0.07}{\eV}) is less pronounced than that obtained for singly-excited states of closed-shell systems (MAEs of \SI{0.15}{\eV} and \SI{0.08}{\eV}). This discrepancy is largely attributed to a greater number of excited states in the latter set exhibiting multiconfigurational characters, which are more challenging for $\Delta$CCSD., Comment: 16 pages, 6 figures

Model Predictive Control (MPC) is a powerful method for complex system regulation, but its reliance on an accurate model poses many limitations in real-world applications. Data-driven predictive control (DDPC) aims at overcoming this limitation, by relying on historical data to provide information on the plant to be controlled. In this work, we present a unified stochastic framework for direct DDPC, where control actions are obtained by optimizing the Final Control Error (FCE), which is directly computed from available data only and automatically weighs the impact of uncertainty on the control objective. Our framework allows us to establish a separation principle for Predictive Control, elucidating the role that predictive models and their uncertainty play in DDPC. Moreover, it generalizes existing DDPC methods, like regularized Data-enabled Predictive Control (DeePC) and $\gamma$-DDPC, providing a path toward noise-tolerant data-based control with rigorous optimality guarantees. The theoretical investigation is complemented by a series of numerical case studies, revealing that the proposed method consistently outperforms or, at worst, matches existing techniques without requiring tuning regularization parameters as other methods do., Comment: 17 pages, 2 figures, 1 table, submitted to Automatica (first submission: December 22nd, 2023)

A seminar given about 30 years ago by Ruben Aldrovandi motivates this text where some reflexions about constructing theories that modify General Relativity are made. Two particular cases, the Brans-Dicke and Unimodular Gravity ones, are discussed, in a quite qualitative way, showing on how they can address some of the most outstanding problems of General Relativity, specially the transplanckian physics and the cosmological constant problem., Comment: Latex file, 14 pages. To appear in the volume "Tribute to Ruben Aldrovandi" (Editora Livraria da F\'isica, S\~ao Paulo, 2024)

We consider the potential for a 10-kg undoped cryogenic CsI detector operating at the Spallation Neutron Source to measure coherent elastic neutrino-nucleus scattering and its sensitivity to discover new physics beyond the standard model. Through a combination of increased event rate, lower threshold, and good timing resolution, such a detector would significantly improve on past measurements. We considered tests of several beyond-the-standard-model scenarios such as neutrino non-standard interactions and accelerator-produced dark matter. This detector's performance was also studied for relevant questions in nuclear physics and neutrino astronomy, namely the weak charge distribution of CsI nuclei and detection of neutrinos from a core-collapse supernova.

This article provides a comprehensive investigation on the chemical bonding, electronic structure, and spectroscopic properties of the lanarkite-type PSO structure from a computational perspective. Thus, different functionals in the field of DFT (e.g., PBE, PBE0, PBESOL, PBESOL0, BLYP, WC1LYP and B3LYP) using a triple-zeta valence plus polarization basis set were assessed to accurate prediction their fundamental properties. Among all DFT functionals tested in this study, in particular, the PBE was the one that showed the best agreement with the experimental data available in the literature. Our results also reveal that the [PbO$_5$] clusters are formed with three different Pb--O bond lengths, with values of about 2.32, 2.59 and 2.84\AA, respectively, and the [SO$_4$] clusters have the same S--O bond length of 1.57\AA. We then performed a complete topological analysis of this system to better understand these structural differences. Additionally, the PSO structure has an indirect band gap energy of 2.9 eV and an effective mass ratio of about 0.415 (using PBE calculations) which may, in principle, indicate a low recombination of electro-hole pairs in the lanarkite structure. Therefore, we believe that a detailed understanding of their electronic structure and spectroscopic properties as well as their chemical bonding is critically important for developing new technologies based on PSO., Comment: 16 pages, 3 figures

In this work, we discuss the determination of the distance to the Large Magellanic Cloud (LMC) using the Leavitt Law, utilizing the public catalog of Classical Cepheid Variable stars from the observational project OGLE-IV (The Optical Gravitational Lensing Experiment Collection of Variable Stars), consisting of 4709 stars in the Large Magellanic Cloud. To determine the pulsation period of Cepheid Variable stars, we employ the computational algorithm \textit{Lomb-Scargle periodogram} modified for our data. Additionally, with the calculation of the period, we can derive a period-luminosity relation for Cepheid Variables in the Large Magellanic Cloud and, using an independent calibration distance, deduce their distance moduli. We also discuss some general theoretical concepts of the physical mechanism behind the oscillation of variable stars., Comment: in Portuguese language

In magnetic tunnel junctions based on iron oxide nanoparticles the disorder and the oxidation state of the surface spin as well as the nanoparticles functionalization play a crucial role in the magnetotransport properties. In this work, we report a systematic study of the effects of vacuum annealing on the structural, magnetic and transport properties of self-assembled ~10 nm Fe3O4 nanoparticles. The high temperature treatment (from 573 to 873 K) decomposes the organic coating into amorphous carbon, reducing the electrical resistivity of the assemblies by 4 orders of magnitude. At the same time, the 3Fe2+/(Fe3++Fe2+) ratio is reduced from 1.11 to 0.13 when the annealing temperature of the sample increases from 573 to 873 K, indicating an important surface oxidation. Although the 2 nm physical gap remains unchanged with the thermal treatment, a monotonous decrease of tunnel barrier width was obtained from the electron transport measurements when the annealing temperature increases, indicating an increment in the number of defects and hot-spots in the gap between the nanoparticles. This is reflected in the reduction of the spin dependent tunneling, which reduces the interparticle magnetoresistance. This work shows new insights about influence of the nanoparticle interfacial composition, as well their the spatial arrangement, on the tunnel transport of self-assemblies, and evidence the importance of optimizing the nanostructure fabrication for increasing the tunneling current without degrading the spin polarized current.

Unimodular gravity (UG) is considered, under many aspects, equivalent to General Relativity (GR), even if the theory is invariant under a more restricted diffeomorphic class of transformations. We discuss the conditions for the equivalence between the two formulations by applying the UG to the static and spherically symmetric configurations being the energy-momentum tensor sourced by a scalar field or by the electromagnetic field. We argue that the equivalence between UG and GR may be broken when analyzing the stability of the solutions at perturbative level., Comment: 12 pages, latex file

Compilation of papers presented by the GAPS Collaboration at the 38th International Cosmic Ray Conference (ICRC), held July 26 through August 3, 2023 in Nagoya, Japan.

In this article, the implementation of black-bounce solutions in $f(R)$ theories is investigated. Black-bounce solutions are regular configurations of the static spherically symmetric space-time, containing both black holes and wormholes structures. In General Relativity (GR), black-bounce solution implies violation of the energy conditions. We investigate the same issue in $f(R)$ theories using two strategies: first, supposing a given form for the $f(R)$ function and then determining the matter behavior; second, imposing a condition on the matter density and obtaining the resulting $f(R)$ function. In all cases, a given structure for the metric functions is supposed. Violation of the energy conditions still occur but they are less severe than in the corresponding GR cases. We propose a zero-density model that has horizons, which differs from the GR case. We also propose a model with positive energy density and show that $\rho+p_r>0$, which was not the case in GR., Comment: 9 pages, 4 figures

To enrich and enhance the diversity of the \textsc{quest} database of highly-accurate excitation energies [\href{https://doi.org/10.1002/wcms.1517}{V\'eril \textit{et al.}, \textit{WIREs Comput.~Mol.~Sci.}~\textbf{11}, e1517 (2021)}], we report vertical transition energies in transition metal compounds. Eleven diatomic molecules with singlet or doublet ground state containing a fourth-row transition metal (\ce{CuCl}, \ce{CuF}, \ce{CuH}, \ce{ScF}, \ce{ScH}, \ce{ScO}, \ce{ScS}, \ce{TiN}, \ce{ZnH}, \ce{ZnO}, and \ce{ZnS}) are considered and the corresponding excitation energies are computed using high-level coupled-cluster (CC) methods, namely CC3, CCSDT, CC4, and CCSDTQ, as well as multiconfigurational methods such as CASPT2 and NEVPT2. In some cases, to provide more comprehensive benchmark data, we also provide full configuration interaction estimates computed with the \textit{"Configuration Interaction using a Perturbative Selection made Iteratively"} (CIPSI) method. Based on these calculations, theoretical best estimates of the transition energies are established in both the aug-cc-pVDZ and aug-cc-pVTZ basis sets. This allows us to accurately assess the performance of CC and multiconfigurational methods for this specific set of challenging transitions. Furthermore, comparisons with experimental data and previous theoretical results are also reported., Comment: 17 pages, 3 figures

Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of, algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders., Comment: Alessandro Fabris, Nina Baranowska, Matthew J. Dennis, David Graus, Philipp Hacker, Jorge Saldivar, Frederik Zuiderveen Borgesius, and Asia J. Biega. Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey. ACM Transactions on Intelligent Systems and Technology. 2024. https://doi.org/10.1145/3696457

In the present work, we construct black-bounce configurations in the context of $k$-essence theory. The solutions have a regular metric function at the origin. The area metric function is linked to the black-bounce area initially considered by Simpson-Visser, $\Sigma^2=x^2+a^2$. Subsequently, the expressions for the scalar field and scalar potential corresponding to the found solutions are determined, exhibiting phantom behavior everywhere due to violation of Null Energy Condition $(NEC^\phi)$. The Kretschmann scalar is regular throughout spacetime, and the geodesics are complete. The energy conditions are analyzed, verifying that the null $(NEC^\phi_1)$ and dominant energy conditions $(DEC^\phi_1)$ are violated inside and outside the event horizon. Finally, the extrinsic curvature method was applied to determine the sign of the mass on the junction surface., Comment: 13 pages, 15 figures