Search

The direct pair-production of the superpartner of the $\tau$-lepton, the $\widetilde{\tau}$, is one of the most interesting channels to search for SUSY in: the $\widetilde{\tau}$ is likely to be the lightest of the scalar leptons, and is one of the most experimentally chalanging ones. The current model-independent $\widetilde{\tau}$ limits come from LEP, while limits obtained at the LHC do extend to higher masses, but are model-dependent. The future Higgs factories will be powerful facilities for SUSY searches, offering advantages with respect to previous electron-positron colliders as well as to hadron machines. In order to quantify the capabilities of these future $e^+e^-$ colliders, the "worst-case" scenario for $\widetilde{\tau}$ exclusion/discovery has been studied, taking into account the effect of the $\widetilde{\tau}$ mixing on $\widetilde{\tau}$ production cross-section and detection efficiency. To evaluate the latter, the ILD concept, originally developed for the International Linear Collider (ILC), and the ILC beam conditions at a centre-of-mass energy of 500 GeV have been used for detailed simulations. The obtained exclusion and discovery reaches extend to only a few GeV below the kinematic limit even in the worst-case scenario. The results of the detailed simulation study are then discussed in view of the experimental environment of other proposed Higgs factory projects., Comment: Contributed to The International Workshop on Future Linear Colliders (LCWS2024), 8-11 July 2024,Tokyo, Japan

Multimodal/vision language models (VLMs) are increasingly being deployed in healthcare settings worldwide, necessitating robust benchmarks to ensure their safety, efficacy, and fairness. Multiple-choice question and answer (QA) datasets derived from national medical examinations have long served as valuable evaluation tools, but existing datasets are largely text-only and available in a limited subset of languages and countries. To address these challenges, we present WorldMedQA-V, an updated multilingual, multimodal benchmarking dataset designed to evaluate VLMs in healthcare. WorldMedQA-V includes 568 labeled multiple-choice QAs paired with 568 medical images from four countries (Brazil, Israel, Japan, and Spain), covering original languages and validated English translations by native clinicians, respectively. Baseline performance for common open- and closed-source models are provided in the local language and English translations, and with and without images provided to the model. The WorldMedQA-V benchmark aims to better match AI systems to the diverse healthcare environments in which they are deployed, fostering more equitable, effective, and representative applications., Comment: submitted for review, total of 14 pages

In this work, we have developed and characterized a position-sensitive CLYC detector that acts as the neutron imaging layer and $\gamma$-ray Compton scatterer of the novel dual \g-ray and neutron imaging system GN-Vision, which aims at simultaneously obtaining information about the spatial origin of \g-ray and neutron sources. We first investigated the performance of large 50$\times$50~mm$^{2}$ monolithic CLYC crystals coupled to a pixelated SiPM in terms of energy resolution and neutron-gamma discrimination. The response of two different 95\% $^{6}$Li-enriched CLYC detectors coupled to an array of 8$\times$8 SiPMs was studied in comparison to the results of a conventional photo-multiplier tube. Energy resolution ranging from 6-8\% for the $^{137}$Cs peak and a figure of merit of 3-4 for the neutron-gamma discrimination have been obtained. The spatial response of the CLYC-SiPM detector to $\gamma$-rays and neutrons has also been characterized using charge modulation-based multiplexing techniques based on a diode-coupled charge division circuit. Average resolutions close to 5~mm FWHM with good linearity are obtained in the transverse crystal plane. Last, this work presents the first proof-of-concept experiments of the neutron imaging capability using a neutron pinhole collimator attached to the developed position sensitive CLYC detector., Comment: 14 pages, 15 Figures, submitted to NIM-A

Quantum simulation is one of the methods that have been proposed and used in practice to bypass computational challenges in the investigation of lattice gauge theories. While most of the proposals rely on truncating the infinite dimensional Hilbert spaces that these models feature, we propose a truncation-free method based on the exact analogy between the local Hilbert space of lattice QED and that of a Josephson junction. We provide several proposals, mostly semi-analog, arranged according to experimental difficulty. Our method can simulate a quasi-2D system of up to $2\times N$ plaquettes, and we present an approximate method that can simulate the fully-2D theory, but is more demanding experimentally and not immediately feasible. This sets the ground for analog quantum simulation of lattice gauge theories with superconducting circuits, in a completely Hilbert space truncation-free procedure, for continuous gauge groups., Comment: 15 pages, 5 figures

Neural codecs have demonstrated strong performance in high-fidelity compression of audio signals at low bitrates. The token-based representations produced by these codecs have proven particularly useful for generative modeling. While much research has focused on improvements in compression ratio and perceptual transparency, recent works have largely overlooked another desirable codec property -- idempotence, the stability of compressed outputs under multiple rounds of encoding. We find that state-of-the-art neural codecs exhibit varied degrees of idempotence, with some degrading audio outputs significantly after as few as three encodings. We investigate possible causes of low idempotence and devise a method for improving idempotence through fine-tuning a codec model. We then examine the effect of idempotence on a simple conditional generative modeling task, and find that increased idempotence can be achieved without negatively impacting downstream modeling performance -- potentially extending the usefulness of neural codecs for practical file compression and iterative generative modeling workflows., Comment: Submitted to ICASSP 2025

We deploy the Hartree-Fock approximation for all-heavy quark hadrons, including quarkonium, baryons, tetraquarks, pentaquarks, dibaryons and up to the 12-body dibaryon-antidibaryon which completely fill the $1s$ orbital, in a unified manner, with the spinless LO Coulomb interaction and beyond. After treating the $c$ and $b$ quarks in various combinations, we delve a bit longer on $t$-quark bound states. We extend the negative result of Kuchiev, Flambaum and Shuryak on the 12-body topball to now include the NLO QCD potential. We find that none of the examined multitop states should have binding energy exceeding their width in the Standard Model. Additional new-physics interactions can then be constrained by experimental searches for these bound states, assuming they are not detected, at a level somewhat less stringent than standing HEFT bounds, but with different systematics., Comment: 39 pages, 7 figures, 16 tables

Surrogate endpoints are often used in place of expensive, delayed, or rare true endpoints in clinical trials. However, regulatory authorities require thorough evaluation to accept these surrogate endpoints as reliable substitutes. One evaluation approach is the information-theoretic causal inference framework, which quantifies surrogacy using the individual causal association (ICA). Like most causal inference methods, this approach relies on models that are only partially identifiable. For continuous outcomes, a normal model is often used. Based on theoretical elements and a Monte Carlo procedure we studied the impact of model misspecification across two scenarios: 1) the true model is based on a multivariate t-distribution, and 2) the true model is based on a multivariate log-normal distribution. In the first scenario, the misspecification has a negligible impact on the results, while in the second, it has a significant impact when the misspecification is detectable using the observed data. Finally, we analyzed two data sets using the normal model and several D-vine copula models that were indistinguishable from the normal model based on the data at hand. We observed that the results may vary when different models are used.

This project focuses on the design and construction of a prototype mouse based on the Arduino platform, intended for individuals without upper limbs to use computers more effectively. The prototype comprises a microcontroller responsible for processing signals from the MPU-6050 sensor, used as a reference for cursor position, and foot-operated buttons for right and left-click functions. Its design enables cursor control through head movements, providing users with an easy and intuitive way to interact with the computer's graphical interface. Feasibility testing was conducted through experimental trials, resulting in ideal accuracy and precision. These trials indicate that the device is viable for use in individuals without upper limbs., Comment: 6 pages, 9 figures

Let $s$ be a fixed positive integer constant, $\varepsilon$ be a fixed small positive number. Then, provided that a prime $p$ is large enough, we prove that for any set $\{{\mathcal M}\subseteq \mathbb F_p^*$ of size $|{\mathcal M}|= \lfloor p^{14/29}\rfloor$ and integer $H=\lfloor p^{14/29+\varepsilon}\rfloor$, any integer $\lambda$ can be represented in the form $$ \frac{m_1}{x_1^s}+\frac{m_2}{x_2^s}+\frac{m_3}{x_3^s}\equiv \lambda \bmod p, $$ with $$ m_i\in {\mathcal M}, \quad 1\le x_i\le H, \qquad i=1,2,3. $$ When $s=1$ we show that for almost all primes $p$ the following holds: if $|{\mathcal M}|= \lfloor p^{1/2}\rfloor$ and $H=\lfloor p^{1/2}(\log p)^{6+\varepsilon}\rfloor$, then any integer $\lambda$ can be represented in the form $$ \frac{m_1}{x_1}+\frac{m_2}{x_2}\equiv \lambda \bmod p, $$ with $$ m_i\in {\mathcal M}, \quad 1\le x_i\le H, \qquad i=1,2. $$

Air-vented ionization chambers exposed to clinical radiation beams may suffer from recombination during the drift of the charge carriers towards the electrodes. Thus, dosimetry protocols recommend the use of a correction factor, usually denominated saturation factor ($k_{\rm sat}$), to correct the ionization chamber readout for the incomplete collection of charge. The two-voltage method is the recommended methodology for the calculation of the saturation factor, however, it is based on the early Boag model, which only takes into account the presence of positive and negative ions in the ionization chamber and does not account for the electric field screening or the free electron contribution to the signal. The numerical simulation shows a better agreement with the experimental data than the current analytical theories in terms of charge collection efficiency. The classical two-voltage method, systematically overestimates the saturation factor, with differences increasing with dose per pulse also present at low dose per pulse. These results may have implications for the dosimetry with ionization chambers in therapy modalities that use a dose per pulse higher than conventional radiotherapy such as intraoperative radiotherapy but also in conventional dose per pulse for ionization chambers that suffer from significant charge recombination.

In this paper we obtain the existence of global attractors for the dynamical systems generated by weak solution of the three-dimensional Navier-Stokes equations with damping. We consider two cases, depending on the values of the parameters \b{eta},{\alpha} controlling the damping term and the viscosity {\mu}. First, for \b{eta} we define a multivalued dynamical systems and prove the existence of the global attractor as well. Second, for either \b{eta}>3 or \b{eta}=3, 4{\alpha}{\mu}>1 the weak solutions are unique and we prove that the global attractor for the corresponding semigroup is more regular. Also, we prove in this case that it is the global attractor for the semigroup generated by the strong solutions. Finally, some numerical simulations are performed.

This work introduces Text2FX, a method that leverages CLAP embeddings and differentiable digital signal processing to control audio effects, such as equalization and reverberation, using open-vocabulary natural language prompts (e.g., "make this sound in-your-face and bold"). Text2FX operates without retraining any models, relying instead on single-instance optimization within the existing embedding space. We show that CLAP encodes valuable information for controlling audio effects and propose two optimization approaches using CLAP to map text to audio effect parameters. While we demonstrate with CLAP, this approach is applicable to any shared text-audio embedding space. Similarly, while we demonstrate with equalization and reverberation, any differentiable audio effect may be controlled. We conduct a listener study with diverse text prompts and source audio to evaluate the quality and alignment of these methods with human perception., Comment: Submitted to ICASSP 2025

We present the first measurement of the $^{47}$K($d,p\gamma$)$^{48}$K transfer reaction, performed in inverse kinematics using a reaccelerated beam of $^{47}$K. The level scheme of $^{48}$K has been greatly extended with nine new bound excited states identified and spectroscopic factors deduced. Detailed comparisons with SDPF-U and SDPF-MU shell-model calculations reveal a number of discrepancies with these results, and a preference for SDPF-MU is found. Intriguingly, an apparent systematic overestimation of spectroscopic factors and a poor reproduction of the energies for 1$^-$ states suggests that the mixing between the $\pi s^{\,\,\,1}_{1/2} d^{\,\,\,4}_{3/2}$ and $\pi s^{\,\,\,2}_{1/2} d^{\,\,\,3}_{3/2}$ proton configurations in $^{48}$K is not correctly described using current interactions, challenging our descriptions of light $N=28$ nuclei., Comment: 7 pages, 3 figures, 1 table

Privacy policies define the terms under which personal data may be collected and processed by data controllers. The General Data Protection Regulation (GDPR) imposes requirements on these policies that are often difficult to implement. Difficulties arise in particular due to the heterogeneity of existing systems (e.g., the Internet of Things (IoT), web technology, etc.). In this paper, we propose a method to refine high level GDPR privacy requirements for informed consent into low-level computational models. The method is aimed at software developers implementing systems that require consent management. We mechanize our models in TLA+ and use model-checking to prove that the low-level computational models implement the high-level privacy requirements; TLA+ has been used by software engineers in companies such as Microsoft or Amazon. We demonstrate our method in two real world scenarios: an implementation of cookie banners and a IoT system communicating via Bluetooth low energy., Comment: Pre-print of paper to appear in the proceedings of SEFM'24

This paper explores the adaptation and application of i-TED Compton imagers for real-time dosimetry in Boron Neutron Capture Therapy (BNCT). The i-TED array, previously utilized in nuclear astrophysics experiments at CERN, is being optimized for detecting and imaging 478 keV gamma-rays, critical for accurate BNCT dosimetry. Detailed Monte Carlo simulations were used to optimize the i-TED detector configuration and enhance its performance in the challenging radiation environment typical of BNCT. Additionally, advanced 3D image reconstruction algorithms, including a combination of back-projection and List-Mode Maximum Likelihood Expectation Maximization (LM-MLEM), are implemented and validated through simulations. Preliminary experimental tests at the Institut Laue-Langevin (ILL) demonstrate the potential of i-TED in a clinical setting, with ongoing experiments focusing on improving imaging capabilities in realistic BNCT conditions., Comment: 9 pages, 7 figures, submitted to the Special Issue of Applied Radiation and Isotopes associated to the 20th International Congress on Neutron Capture Therapy (20th ICNCT)

Levels of cancellativity in commutative monoids $M$, determined by stable rank values in $\mathbb{Z}_{> 0} \cup \{\infty\}$ for elements of $M$, are investigated. The behavior of the stable ranks of multiples $ka$, for $k \in \mathbb{Z}_{> 0}$ and $a \in M$, is determined. In the case of a refinement monoid $M$, the possible stable rank values in archimedean components of $M$ are pinned down. Finally, stable rank in monoids built from isomorphism or other equivalence classes of modules over a ring is discussed.

Dosimetry in BNCT poses significant challenges due to the indirect effect of neutrons interacting with elements within the body and uncertainties associated with the uptake of boron compounds used in clinical practice. Current treatment planning relies on unconventional estimates of boron tumor uptake derived from prior PET scans and thus, an online boron-uptake monitor would be highly convenient. This work presents the first pilot experiments carried out at ILL-Grenoble with the high-efficiency Compton camera i-TED, hereby aiming at demonstrating its applicability for BNCT dosimetry by introducing real-time measurement of the boron concentration and imaging capabilities of spatial dose distribution. In this experiment, we measured the $^{10}$B uptake of different cancer cells of tongue squamous cell carcinoma, malignant melanoma and glioblastoma treated with BPA (80~ppm of $^{10}$B). The samples were irradiated with the thermal neutron spectrum of ILL-Grenoble and the 478keV $\gamma$-rays from the $^{7}$Li de-excitation after the neutron-boron reaction were registered both with the Compton imager and the high-sensitivity FIPPS HPGe array. These series of measurements allowed us to demonstrate the imaging capabilities of the Compton imaging device for this type of application, as well as to assess its sensitivity, which was found to be below 1 $\mu$g of $^{10}$B., Comment: 10 pages, 12 figures, submitted to the Special Issue of Applied Radiation and Isotopes associated to the 20th International Congress on Neutron Capture Therapy (20th ICNCT)

Several domains of society will be disrupted once millions of high-quality qubits can be brought together to perform fault-tolerant quantum computing (FTQC). All quantum computing hardware available today is many orders of magnitude removed from the requirements for FTQC. The intimidating challenges associated with integrating such complex systems have already been addressed by the semiconductor industry -hence many qubit makers have retrofitted their technology to be CMOS-compatible. This compatibility, however, can have varying degrees ranging from the mere ability to fabricate qubits using a silicon wafer as a substrate, all the way to the co-integration of qubits with high-yield, low-power advanced electronics to control these qubits. Extrapolating the evolution of quantum processors to future systems, semiconductor spin qubits have unique advantages in this respect, making them one of the most serious contenders for large-scale FTQC. In this review, we focus on the overlap between state-of-the-art semiconductor spin qubit systems and CMOS industry Very Large-Scale Integration (VLSI) principles. We identify the main differences in spin qubit operation, material, and system requirements compared to well-established CMOS industry practices. As key players in the field are looking to collaborate with CMOS industry partners, this review serves to accelerate R&D towards the industrial scale production of FTQC processors., Comment: 36 pages, 5 figures

Open science is a fundamental pillar to promote scientific progress and collaboration, based on the principles of open data, open source and open access. However, the requirements for publishing and sharing open data are in many cases difficult to meet in compliance with strict data protection regulations. Consequently, researchers need to rely on proven methods that allow them to anonymize their data without sharing it with third parties. To this end, this paper presents the implementation of a Python library for the anonymization of sensitive tabular data. This framework provides users with a wide range of anonymization methods that can be applied on the given dataset, including the set of identifiers, quasi-identifiers, generalization hierarchies and allowed level of suppression, along with the sensitive attribute and the level of anonymity required. The library has been implemented following best practices for integration and continuous development, as well as the use of workflows to test code coverage based on unit and functional tests., Comment: Preprint under review

The increasing generation of data in different areas of life, such as the environment, highlights the need to explore new techniques for processing and exploiting data for useful purposes. In this context, artificial intelligence techniques, especially through deep learning models, are key tools to be used on the large amount of data that can be obtained, for example, from weather radars. In many cases, the information collected by these radars is not open, or belongs to different institutions, thus needing to deal with the distributed nature of this data. In this work, the applicability of a personalized federated learning architecture, which has been called adapFL, on distributed weather radar images is addressed. To this end, given a single available radar covering 400 km in diameter, the captured images are divided in such a way that they are disjointly distributed into four different federated clients. The results obtained with adapFL are analyzed in each zone, as well as in a central area covering part of the surface of each of the previously distributed areas. The ultimate goal of this work is to study the generalization capability of this type of learning technique for its extrapolation to use cases in which a representative number of radars is available, whose data can not be centralized due to technical, legal or administrative concerns. The results of this preliminary study indicate that the performance obtained in each zone with the adapFL approach allows improving the results of the federated learning approach, the individual deep learning models and the classical Continuity Tracking Radar Echoes by Correlation approach., Comment: Accepted for publication in Earth Science Informatics

A Weierstrass Prym eigenform is an Abelian differential with a single zero on a Riemann surface possessing some special kinds of symmetries. Such surfaces come equipped with an involution, known as a Prym involution. They were originally discovered by McMullen and only arise in genus 2, 3 and 4. Moreover, they are classified by two invariants: discriminant and spin. We study how the fixed points for the Prym involution of Weierstrass Prym eigenforms are permuted. In previous work, the authors computed the permutation group induced by affine transformations in the case of genus 2, showing that they are dihedral groups depending only on the residue class modulo 8 of the discriminant $D$. In this work, we complete this classification by settling the case of genus 3, showing that the permutation group induced by the affine group on the set of its three (regular) fixed points is isomorphic to $\mathrm{Sym}_2$ when $D$ is even and a quadratic residue modulo 16, and to $\mathrm{Sym}_3$ otherwise. The case of genus 4 is trivial as the Pyrm involution fixes a single (regular) point. In both cases, these same groups arise when considering only parabolic elements of the affine group. By recent work of Freedman, when the Teichm\"uller curve induced by Weierstrass Prym eigenform is not arithmetic, the fixed points of the Prym involution coincide with the periodic points of the surface. Hence, in this case, our result also classifies how periodic points are permuted., Comment: 20 pages, 16 figures. arXiv admin note: substantial text overlap with arXiv:2111.13638

Functional markers become a more frequent tool in medical diagnosis. In this paper, we aim to define an index allowing to discriminate between populations when the observations are functional data belonging to a Hilbert space. We discuss some of the problems arising when estimating optimal directions defined to maximize the area under the curve of a projection index and we construct the corresponding ROC curve. We also go one step forward and consider the case of possibly different covariance operators, for which we recommend a quadratic discrimination rule. Consistency results are derived for both linear and quadratic indexes, under mild conditions. The results of our numerical experiments allow to see the advantages of the quadratic rule when the populations have different covariance operators. We also illustrate the considered methods on a real data set.

The Southern Photometric Local Universe Survey (S-PLUS) is a project to map $\sim9300$ sq deg of the sky using twelve bands (seven narrow and five broadbands). Observations are performed with the T80-South telescope, a robotic telescope located at the Cerro Tololo Observatory in Chile. The survey footprint consists of several large contiguous areas, including fields at high and low galactic latitudes, and towards the Magellanic Clouds. S-PLUS uses fixed exposure times to reach point source depths of about $21$ mag in the $griz$ and $20$ mag in the $u$ and the narrow filters. This paper describes the S-PLUS Data Release 4 (DR4), which includes calibrated images and derived catalogues for over 3000 sq deg, covering the aforementioned area. The catalogues provide multi-band photometry performed with the tools \texttt{DoPHOT} and \texttt{SExtractor} -- point spread function (\PSF) and aperture photometry, respectively. In addition to the characterization, we also present the scientific potential of the data. We use statistical tools to present and compare the photometry obtained through different methods. Overall we find good agreement between the different methods, with a slight systematic offset of 0.05\,mag between our \PSF and aperture photometry. We show that the astrometry accuracy is equivalent to that obtained in previous S-PLUS data releases, even in very crowded fields where photometric extraction is challenging. The depths of main survey (MS) photometry for a minimum signal-to-noise ratio $S/N = 3$ reach from $\sim19.5$ for the bluer bands to $\sim21.5$ mag on the red. The range of magnitudes over which accurate \PSF photometry is obtained is shallower, reaching $\sim19$ to $\sim20.5$ mag depending on the filter. Based on these photometric data, we provide star-galaxy-quasar classification and photometric redshift for millions of objects., Comment: 26 pages, 17 figures, 14 tables, accepted for A&A

Hybrid organic-inorganic perovskites (HOIP) have emerged in recent years as highly promising semiconducting materials for a wide range of optoelectronic and energy applications. Nevertheless, the rotational dynamics of the organic components and many molecule interdependencies, which may strongly impact the functional properties of HOIP, are not yet fully understood. In this study, we quantitatively analyze the orientational disorder and molecular correlations in the archetypal perovskite CH$_{3}$NH$_{3}$PbI$_{3}$ (MAPI) by performing comprehensive molecular dynamics simulations and entropy calculations. We found that, in addition to the usual vibrational and orientational contributions, rigid molecular rotations around the C-N axis and correlations between neighboring molecules noticeably contribute to the entropy increment associated with the temperature-induced order-disorder phase transition in MAPI, $\Delta S_{t}$. Molecular conformational changes are equally infrequent in the low-$T$ ordered and high-$T$ disordered phases and have a null effect on $\Delta S_{t}$. Conversely, the couplings between the angular and vibrational degrees of freedom are substantially reinforced in the high-$T$ disordered phase and significantly counteract the phase-transition entropy increase resulting from other factors. Furthermore, the tendency for neighboring molecules to be orientationally ordered is markedly local, consequently inhibiting the formation of extensive polar nanodomains both at low and high temperatures. This theoretical investigation not only advances the fundamental knowledge of HOIP but also establishes physically insightful connections with contemporary technological applications like photovoltaics and solid-state cooling., Comment: 13 pages, 9 figures

Variational Physics-Informed Neural Networks often suffer from poor convergence when using stochastic gradient-descent-based optimizers. By introducing a Least Squares solver for the weights of the last layer of the neural network, we improve the convergence of the loss during training in most practical scenarios. This work analyzes the computational cost of the resulting hybrid Least-Squares/Gradient-Descent optimizer and explains how to implement it efficiently. In particular, we show that a traditional implementation based on backward-mode automatic differentiation leads to a prohibitively expensive algorithm. To remedy this, we propose using either forward-mode automatic differentiation or an ultraweak-type scheme that avoids the differentiation of trial functions in the discrete weak formulation. The proposed alternatives are up to one hundred times faster than the traditional one, recovering a computational cost-per-iteration similar to that of a conventional gradient-descent-based optimizer alone. To support our analysis, we derive computational estimates and conduct numerical experiments in one- and two-dimensional problems., Comment: 23 pages, 10 figures, preprint

Recent fluctuation identities for $\alpha$-stable L\'evy processes have decomposed paths using generalised spherical polar coordinates revealing an underlying Markov Additive Process (MAP) for which a more advanced form of excursion theory can be exploited. Inspired by this approach, we give a different decomposition of the $d$-dimensional isotropic $\alpha$-stable L\'evy processes in terms of orthogonal coordinates. Accordingly we are able to develop a number of $n$-tuple laws for first entrance into a half-space. We also numerically construct the law of first entry of the process into a slab of the form $(-1, 1)\times \mathbb{R}^{d-1}$ using a walk-on-half-spaces Monte Carlo approach., Comment: 5 figures

We study the influence that research environments have in shaping careers of early-career faculty in terms of their research portfolio. We find that departments exert an attractive force over early-career newcomer faculty, who after their incorporation increase their within-department collaborations, and work on topics closer to those of incumbent faculty. However, these collaborations are not gender blind: Newcomers collaborate less than expected with female senior incumbents. The analysis of departments grouped by fraction of female incumbents reveals that female newcomers in departments with above the median fractions of female incumbents tend to select research topics farther from their department than female newcomers in the remaining departments -- a difference we do not observe for male newcomers. Our results suggest a relationship between the collaboration deficit with female incumbents and the selection of research topics of female early-faculty, thus highlighting the importance of studying research environments to fully understand gender differences in academia.

This article puts forward the use of mutual information values to replicate the expertise of security professionals in selecting features for detecting web attacks. The goal is to enhance the effectiveness of web application firewalls (WAFs). Web applications are frequently vulnerable to various security threats, making WAFs essential for their protection. WAFs analyze HTTP traffic using rule-based approaches to identify known attack patterns and to detect and block potential malicious requests. However, a major challenge is the occurrence of false positives, which can lead to blocking legitimate traffic and impact the normal functioning of the application. The problem is addressed as an approach that combines supervised learning for feature selection with a semi-supervised learning scenario for training a One-Class SVM model. The experimental findings show that the model trained with features selected by the proposed algorithm outperformed the expert-based selection approach in terms of performance. Additionally, the results obtained by the traditional rule-based WAF ModSecurity, configured with a vanilla set of OWASP CRS rules, were also improved.

The goal of future quantum networks is to enable new internet applications that are impossible to achieve using solely classical communication. Up to now, demonstrations of quantum network applications and functionalities on quantum processors have been performed in ad-hoc software that was specific to the experimental setup, programmed to perform one single task (the application experiment) directly into low-level control devices using expertise in experimental physics. Here, we report on the design and implementation of the first architecture capable of executing quantum network applications on quantum processors in platform-independent high-level software. We demonstrate the architecture's capability to execute applications in high-level software, by implementing it as a quantum network operating system -- QNodeOS -- and executing test programs including a delegated computation from a client to a server on two quantum network nodes based on nitrogen-vacancy (NV) centers in diamond. We show how our architecture allows us to maximize the use of quantum network hardware, by multitasking different applications on a quantum network for the first time. Our architecture can be used to execute programs on any quantum processor platform corresponding to our system model, which we illustrate by demonstrating an additional driver for QNodeOS for a trapped-ion quantum network node based on a single $^{40}\text{Ca}^+$ atom. Our architecture lays the groundwork for computer science research in the domain of quantum network programming, and paves the way for the development of software that can bring quantum network technology to society., Comment: 12 pages, 5 figures, supplementary materials (48 pages, 24 figures, 11 tables)

We employ correlated density-functional theory methods (DFT + Hubbard $U$) to investigate the spin-density wave state of the bilayer Ruddlesden-Popper (RP) nickelate La$_{3}$Ni$_{2}$O$_{7}$ which becomes superconducting under pressure. We predict that the ground state of this bilayer RP material is a single spin-charge stripe phase with in-plane up$^\prime$/up/down$^\prime$/down diagonal stripes with up$^\prime$/down$^\prime$ being low spin (formally Ni$^{3+}$: $d^7$) and up/down being high spin (formally Ni$^{2+}$: $d^8$). The main feature of this solution (that is insulating even at $U=0$) is the dominant role of $d_{x^{2}-y^{2}}$ bands around the Fermi level, which would become doped with the introduction of electrons via oxygen vacancies. In spite of the similarity with cuprates in terms of the dominant role of $d_{x^{2}-y^{2}}$ bands, some differences are apparent in the magnetic ground state of La$_{3}$Ni$_{2}$O$_{7}$: the antiferromagnetic out-of-plane coupling within the bilayer (linked to the $d_{z^2}$ orbitals forming a spin-singlet-like configuration) is found to be the dominant one while in-plane interactions are reduced due to the stripe order of the ground state. With pressure, this striped magnetic ground state remains similar in nature but the increase in bandwidth quickly transitions La$_{3}$Ni$_{2}$O$_{7}$ into a metallic state with all the activity close to the Fermi level involving, to a large extent, $d_{x^2-y^2}$ orbitals. This is reminiscent of the cuprates and may provide key insights into how superconductivity arises in this material under pressure., Comment: 8 pages, 4 figures

The probabilities for gamma-ray and particle emission as a function of the excitation energy of a decaying nucleus are valuable observables for constraining the ingredients of the models that describe the de-excitation of nuclei near the particle emission threshold. These models are essential in nuclear astrophysics and applications. In this work, we have for the first time simultaneously measured the gamma-ray and neutron emission probabilities of 208Pb. The measurement was performed in inverse kinematics at the Experimental Storage Ring (ESR) of the GSI/FAIR facility, where a 208Pb beam interacted through the 208Pb(p,p') reaction with a hydrogen gas jet target. Instead of detecting the gamma-rays and neutrons emitted by 208Pb, we detected the heavy beam-like residues produced after gamma and neutron emission. These heavy residues were fully separated by a dipole magnet of the ESR and were detected with outstanding efficiencies. The comparison of the measured probabilities with model calculations has allowed us to test different descriptions of the gamma-ray strength function and the nuclear level density available in the literature., Comment: 23 pages, 13 figures

We present the first detection in space of 1,4-pentadiyne. It has been found towards TMC-1 with the QUIJOTE line survey in the 31-50 GHz range. We observed a total of 17 transitions with J = 2 up to 13 and Ka = 0, 1 and 2. The observed transitions allowed us to derive a rotational temperature of 9.5 +- 0.5 K and a column density of (5.0 +- 0.5) x 10^12 cm-2. This molecule was the last non-cyclic isomer of the C5H4 family that could be detected via radio astronomy. A computational chemistry study was performed to determine the energies of the five most stable isomers. The isomer (c-C3H3CCH) has a considerably higher energy than the others, and it has not yet been detected. To better understand the chemical reactions involving these species, we compared the ethynyl and cyano derivatives. The observed abundances of these species are in good agreement with the branching ratios of the formation reactions studied with our chemical model of TMC-1.

The $^{246}$Cm(n,$\gamma$) and $^{248}$Cm(n,$\gamma$) cross-sections have been measured at the Experimental Area 2 (EAR2) of the n_TOF facility at CERN with three C$_6$D$_6$ detectors. This measurement is part of a collective effort to improve the capture cross-section data for Minor Actinides (MAs), which are required to estimate the production and transmutation rates of these isotopes in light water reactors and innovative reactor systems. In particular, the neutron capture in $^{246}$Cm and $^{248}$Cm open the path for the formation of other Cm isotopes and heavier elements such as Bk and Cf and the knowledge of (n,$\gamma$) cross-sections of these Cm isotopes plays an important role in the transport, transmutation and storage of the spent nuclear fuel. The reactions $^{246}$Cm(n,$\gamma$) and $^{248}$Cm(n,$\gamma$) have been the two first capture measurements analyzed at n_TOF EAR2. Until this experiment and two recent measurements performed at J-PARC, there was only one set of data of the capture cross-sections of $^{246}$Cm and $^{248}$Cm, that was obtained in 1969 in an underground nuclear explosion experiment. In the measurement at n_TOF a total of 13 resonances of $^{246}$Cm between 4 and 400 eV and 5 of $^{248}$Cm between 7 and 100 eV have been identified and fitted. The radiative kernels obtained for $^{246}$Cm are compatible with JENDL-5, but some of them are not with JENDL-4, which has been adopted by JEFF-3.3 and ENDF/B-VIII.0. The radiative kernels obtained for the first three $^{248}$Cm resonances are compatible with JENDL-5, however, the other two are not compatible with any other evaluation and are 20% and 60% larger than JENDL-5.

Fine-grained editing of speech attributes$\unicode{x2014}$such as prosody (i.e., the pitch, loudness, and phoneme durations), pronunciation, speaker identity, and formants$\unicode{x2014}$is useful for fine-tuning and fixing imperfections in human and AI-generated speech recordings for creation of podcasts, film dialogue, and video game dialogue. Existing speech synthesis systems use representations that entangle two or more of these attributes, prohibiting their use in fine-grained, disentangled editing. In this paper, we demonstrate the first disentangled and interpretable representation of speech with comparable subjective and objective vocoding reconstruction accuracy to Mel spectrograms. Our interpretable representation, combined with our proposed data augmentation method, enables training an existing neural vocoder to perform fast, accurate, and high-quality editing of pitch, duration, volume, timbral correlates of volume, pronunciation, speaker identity, and spectral balance., Comment: Interspeech 2024

In this paper, we present an end-to-end multi-source Entity Matching problem, which we call entity group matching, where the goal is to assign to the same group, records originating from multiple data sources but representing the same real-world entity. We focus on the effects of transitively matched records, i.e. the records connected by paths in the graph G = (V,E) whose nodes and edges represent the records and whether they are a match or not. We present a real-world instance of this problem, where the challenge is to match records of companies and financial securities originating from different data providers. We also introduce two new multi-source benchmark datasets that present similar matching challenges as real-world records. A distinctive characteristic of these records is that they are regularly updated following real-world events, but updates are not applied uniformly across data sources. This phenomenon makes the matching of certain groups of records only possible through the use of transitive information. In our experiments, we illustrate how considering transitively matched records is challenging since a limited amount of false positive pairwise match predictions can throw off the group assignment of large quantities of records. Thus, we propose GraLMatch, a method that can partially detect and remove false positive pairwise predictions through graph-based properties. Finally, we showcase how fine-tuning a Transformer-based model (DistilBERT) on a reduced number of labeled samples yields a better final entity group matching than training on more samples and/or incorporating fine-tuning optimizations, illustrating how precision becomes the deciding factor in the entity group matching of large volumes of records., Comment: 12 pages, 4 figures, accepted as research paper at EDBT 2025

The NEXT-White detector, a high-pressure gaseous xenon time projection chamber, demonstrated the excellence of this technology for future neutrinoless double beta decay searches using photomultiplier tubes (PMTs) to measure energy and silicon photomultipliers (SiPMs) to extract topology information. This analysis uses $^{83m}\text{Kr}$ data from the NEXT-White detector to measure and understand the energy resolution that can be obtained with the SiPMs, rather than with PMTs. The energy resolution obtained of (10.9 $\pm$ 0.6) $\%$, full-width half-maximum, is slightly larger than predicted based on the photon statistics resulting from very low light detection coverage of the SiPM plane in the NEXT-White detector. The difference in the predicted and measured resolution is attributed to poor corrections, which are expected to be improved with larger statistics. Furthermore, the noise of the SiPMs is shown to not be a dominant factor in the energy resolution and may be negligible when noise subtraction is applied appropriately, for high-energy events or larger SiPM coverage detectors. These results, which are extrapolated to estimate the response of large coverage SiPM planes, are promising for the development of future, SiPM-only, readout planes that can offer imaging and achieve similar energy resolution to that previously demonstrated with PMTs.

The spatial distribution of molecules in AGB circumstellar envelopes is regulated by different processes. In the outer layers all molecules are destroyed due to the interaction with interstellar ultraviolet photons. Here we aim to characterize in a coherent and uniform way the radial extent of three molecules (SiO, CS, and SiS) in envelopes around AGB stars of O- and C-rich character, and to study their dependence with mass loss rate. To that purpose, we used the Yebes 40m and IRAM 30m telescopes to observe 7 M-type and 7 C-type AGB envelopes covering a wide range of mass loss rates (1e-7 - 1e-5 Msun/yr) in lines of SiO, CS, and SiS spanning a range of upper level energies of 2-130 K. We carried out excitation and radiative transfer calculations over a wide parameter space to characterize the molecular abundance and radial extent. A chi2 analysis indicates that the abundance is well constrained while the radial extent is more difficult to constrain. The radial extent increases with increasing envelope density, in agreement with previous observational findings. At high envelope densities, Mdot/vexp > 1e-6 (Msun/yr)/(km/s), the radial extent of SiO, CS, and SiS are similar, while at low envelope densities, Mdot/vexp < 1e-7 (Msun/yr)/(km/s), the radial extent differ among the three molecules, in agreement with theoretical expectations based on destruction due to photodissociation. At low envelope densities we find a sequence of increasing radial extent, SiS -> CS -> SiO. We also find a tentative dependence of the radial extent with the chemical type (O- or C-rich) of the star for SiO and CS. Interferometric observations and further investigation of the photodissociation of SiO, CS, and SiS should allow to clarify the situation on the relative photodissociation radius of SiO, CS, and SiS in AGB envelopes and the dependence with envelope density and C/O ratio., Comment: Accepted for publication in A&A. Abstract abridged

This study investigates whether Compressed-Language Models (CLMs), i.e. language models operating on raw byte streams from Compressed File Formats~(CFFs), can understand files compressed by CFFs. We focus on the JPEG format as a representative CFF, given its commonality and its representativeness of key concepts in compression, such as entropy coding and run-length encoding. We test if CLMs understand the JPEG format by probing their capabilities to perform along three axes: recognition of inherent file properties, handling of files with anomalies, and generation of new files. Our findings demonstrate that CLMs can effectively perform these tasks. These results suggest that CLMs can understand the semantics of compressed data when directly operating on the byte streams of files produced by CFFs. The possibility to directly operate on raw compressed files offers the promise to leverage some of their remarkable characteristics, such as their ubiquity, compactness, multi-modality and segment-nature.

One of the fundamental distinctions in McMullen and Sullivan's description of the Teichm\"uller space of a complex dynamical system is between discrete and indiscrete grand orbit relations. We investigate these on the Fatou set of transcendental entire maps and provide criteria to distinguish between the two types. Furthermore, we show that discrete and indiscrete grand orbit relations may coexist non-trivially in a wandering domain, a phenomenon which does not occur for any other type of Fatou component. One of the tools used is a novel quasiconformal surgery technique of independent interest., Comment: 29 pages, 3 figures

Both black hole thermodynamics and finite volume effects in quantum field theory violate the null energy condition. Motivated by this, we compare thermodynamic features between two 1+1-dimensional systems: (i) a scalar field confined to a periodic spatial interval of length $a$ and tunneling between two degenerate vacua; (ii) a dilatonic black hole at temperature $T$ in the presence of matter fields. If we identify $a\propto T^{-1}$, we find similar thermodynamic behaviours, which suggests some deeper connection arising from the presence of non-trivial boundary conditions in both systems., Comment: 28 pages, 4 figures

Whilst the Universal Approximation Theorem guarantees the existence of approximations to Sobolev functions -- the natural function spaces for PDEs -- by Neural Networks (NNs) of sufficient size, low-regularity solutions may lead to poor approximations in practice. For example, classical fully-connected feed-forward NNs fail to approximate continuous functions whose gradient is discontinuous when employing strong formulations like in Physics Informed Neural Networks (PINNs). In this article, we propose the use of regularity-conforming neural networks, where a priori information on the regularity of solutions to PDEs can be employed to construct proper architectures. We illustrate the potential of such architectures via a two-dimensional (2D) transmission problem, where the solution may admit discontinuities in the gradient across interfaces, as well as power-like singularities at certain points. In particular, we formulate the weak transmission problem in a PINNs-like strong formulation with interface and continuity conditions. Such architectures are partially explainable; discontinuities are explicitly described, allowing the introduction of novel terms into the loss function. We demonstrate via several model problems in one and two dimensions the advantages of using regularity-conforming architectures in contrast to classical architectures. The ideas presented in this article easily extend to problems in higher dimensions.

The design and development of new photonic devices for technological applications requires a deep understanding of the effect of structural properties on the resulting band gap size and its position. Here, we perform a theoretical study of behavior of the photonic band gap sizes, positions and percentages under variations of the parameters characterizing binary (two materials), ternary (three materials) and linear dielectric grating multilayer structures. The resulting band gap atlas show that binary systems may suffice for most applications but ternary systems may add additional flexibility in design if needed. Linear gratings show a regular pattern for all gaps studied, this regularity was able to be reproduced with only few materials involved. The position of the gaps showed a very monotonous behavior for all calculations performed. Finally, additional extensions of formulas commonly used in the design of Bragg mirrors/reflectors using binary materials were proposed with their corresponding limitations discussed. These results can be seen as a technological horizon for photonic device development., Comment: 17 pages, 18 figures, regular article

Recent $\gamma$-ray and neutrino observations seem to favor the consideration of non-uniform diffusion of cosmic rays (CRs) throughout the Galaxy. In this study, we investigate the consequences of spatially-dependent inhomogeneous propagation of CRs on the fluxes of secondary CRs and antiprotons detected at Earth. A comparison is made among different scenarios in search of potential features that may guide us toward favoring one over another in the near future. We also examine both the influence of inhomogeneous propagation in the production of secondary CRs from interactions with the gas, and the effects of this scenario on the local fluxes of antiprotons and light antinuclei produced as final products of dark matter annihilation. Our results indicate that the consideration of an inhomogeneous diffusion model could improve the compatibility of the predicted local antiproton flux with that of B, Be and Li, assuming only secondary origin of these particles. In addition, our model predicts a slightly harder local antiproton spectrum, making it more compatible with the high energy measurements of AMS-02. Finally, no significant changes are expected in the predicted local flux of antiprotons and antinuclei produced from dark matter among the different considered propagation scenarios., Comment: 19 pages, 6 figures in the main text and 3 figures in appendices, 1 table, 2 appendices; based on MSc Thesis of \'Alvaro Tovar-Pardo. Comments are welcome

The imaging of individual Ba$^{2+}$ ions in high pressure xenon gas is one possible way to attain background-free sensitivity to neutrinoless double beta decay and hence establish the Majorana nature of the neutrino. In this paper we demonstrate selective single Ba$^{2+}$ ion imaging inside a high-pressure xenon gas environment. Ba$^{2+}$ ions chelated with molecular chemosensors are resolved at the gas-solid interface using a diffraction-limited imaging system with scan area of 1$\times$1~cm$^2$ located inside 10~bar of xenon gas. This new form of microscopy represents an important enabling step in the development of barium tagging for neutrinoless double beta decay searches in $^{136}$Xe, as well as a new tool for studying the photophysics of fluorescent molecules and chemosensors at the solid-gas interface.

We propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using deep learning. We feed the model with fine-grid data after projecting it to the coarse-grid discretization. We substitute the default differencing scheme for the convection term by a feed-forward neural network that interpolates velocities from cell centers to face values to produce velocities that approximate the down-sampled fine-grid data well. The deep learning framework incorporates the open-source CFD code OpenFOAM, resulting in an end-to-end differentiable model. We automatically differentiate the CFD physics using a discrete adjoint code version. We present a fast communication method between TensorFlow (Python) and OpenFOAM (c++) that accelerates the training process. We applied the model to the flow past a square cylinder problem, reducing the error from 120% to 25% in the velocity for simulations inside the training distribution compared to the traditional solver using an x8 coarser mesh. For simulations outside the training distribution, the error reduction in the velocities was about 50%. The training is affordable in terms of time and data samples since the architecture exploits the local features of the physics.

We give a number of constructions where inverse limits seriously degrade properties of regular rings, such as unit-regularity, diagonalisation of matrices, and finite stable rank. This raises the possibility of using inverse limits to answer the long standing Separativity Problem (in the negative).

Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer health is critical to maintain grid stability and performance. Thermal insulation ageing is a key transformer failure mode, which is generally tracked by monitoring the hotspot temperature (HST). However, HST measurement is complex, costly, and often estimated from indirect measurements. Existing HST models focus on space-agnostic thermal models, providing worst-case HST estimates. This article introduces a spatio-temporal model for transformer winding temperature and ageing estimation, which leverages physics-based partial differential equations (PDEs) with data-driven Neural Networks (NN) in a Physics Informed Neural Networks (PINNs) configuration to improve prediction accuracy and acquire spatio-temporal resolution. The computational accuracy of the PINN model is improved through the implementation of the Residual-Based Attention (PINN-RBA) scheme that accelerates the PINN model convergence. The PINN-RBA model is benchmarked against self-adaptive attention schemes and classical vanilla PINN configurations. For the first time, PINN based oil temperature predictions are used to estimate spatio-temporal transformer winding temperature values, validated through PDE numerical solution and fiber optic sensor measurements. Furthermore, the spatio-temporal transformer ageing model is inferred, which supports transformer health management decision-making. Results are validated with a distribution transformer operating on a floating photovoltaic power plant., Comment: 23 pages, 18 figures