Search

Electron-capture decays of $^{125}$Xe and $^{127}$Xe, and double-electron-capture decays of $^{124}$Xe, are backgrounds in searches for weakly interacting massive particles (WIMPs) conducted by dual-phase xenon time projection chambers such as LUX-ZEPLIN (LZ). These decays produce signals with more light and less charge than equivalent-energy $\beta$ decays, and correspondingly overlap more with WIMP signals. We measure three electron-capture charge yields in LZ: the 1.1~keV M-shell, 5.2~keV L-shell, and 33.2~keV K-shell at drift fields of 193~V/cm and 96.5~V/cm. The LL double-electron-capture decay of $^{124}$Xe exhibits even more pronounced shifts in charge and light. We provide a first model of double-electron-capture charge yields using the link between ionization density and electron-ion recombination, and identify a need for more accurate calculations. Finally, we discuss the implications of the reduced charge yield of these decays and other interactions creating inner-shell vacancies for future dark matter searches., Comment: 16 pages, 11 figures

Concept-based models are an emerging paradigm in deep learning that constrains the inference process to operate through human-interpretable concepts, facilitating explainability and human interaction. However, these architectures, on par with popular opaque neural models, fail to account for the true causal mechanisms underlying the target phenomena represented in the data. This hampers their ability to support causal reasoning tasks, limits out-of-distribution generalization, and hinders the implementation of fairness constraints. To overcome these issues, we propose \emph{Causally reliable Concept Bottleneck Models} (C$^2$BMs), a class of concept-based architectures that enforce reasoning through a bottleneck of concepts structured according to a model of the real-world causal mechanisms. We also introduce a pipeline to automatically learn this structure from observational data and \emph{unstructured} background knowledge (e.g., scientific literature). Experimental evidence suggest that C$^2$BM are more interpretable, causally reliable, and improve responsiveness to interventions w.r.t. standard opaque and concept-based models, while maintaining their accuracy.

We consider fitting a bivariate spline regression model to data using a weighted least-squares cost function, with weights that sum to one to form a discrete probability distribution. By applying the principle of maximum entropy, the weight distribution is determined by maximizing the associated entropy function. This approach, previously applied successfully to polynomials and spline curves, enhances the robustness of the regression model by automatically detecting and down-weighting anomalous data during the fitting process. To demonstrate the effectiveness of the method, we present applications to two image processing problems and further illustrate its potential through two synthetic examples., Comment: 18 pages, 23 figures

The standard cosmological model, rooted in General Relativity (GR), has achieved remarkable success, yet it still faces unresolved issues like the nature of dark matter, dark energy, and the Hubble tension. These challenges might imply the need for alternative gravitational theories. Teleparallel gravity offers a compelling framework by reformulating the gravitational interaction using torsion, rather than curvature, as its fundamental geometrical property. This paper delves into $f(T)$ gravity, an extension of the Teleparallel Equivalent of General Relativity (TEGR), which introduces non-linear modifications of the torsion scalar $T$. We focus on the role of spacetime-dependent Lorentz transformations in the vierbein formalism, examining their impact on both background solutions and perturbation dynamics. Special attention is given to the homogeneous and isotropic FLRW spacetime, as well as the anisotropic Bianchi I spacetime. Furthermore, the analysis of the propagating degrees of freedom on these spacetimes is performed. While it is well established that TEGR reproduces the same results as GR, the propagating degrees of freedom in its non-linear extension, $f(T)$ gravity, is still debated in the literature. In this work, we find that only two fields propagate in the gravity sector, independently of the background spacetime considered, either FLRW or Bianchi I. Although not definitive, this paper provides fresh insights into the issue of the propagating degrees of freedom in $f(T)$ gravity, opening the door to intriguing new directions for further investigation., Comment: 19 pages

We consider the equation $- \Delta_{p} u = f(x)$ in $\Omega,$ where $\Delta_{p}$ is the $p$-Laplace operator. We provide $L^{\infty}$-type estimates for the second derivatives of solutions when $p$ approaches to $2$., Comment: 19 pages

Motivated by a recently discovered connection between the greybody factors of black holes and the ringdown signal, we investigate the greybody factors of ultracompact horizonless objects, also elucidating their connection to echoes. The greybody factor of ultracompact objects features both low-frequency resonances and high-frequency, quasi-reflectionless scattering modes, which become purely reflectionless in the presence of symmetric cavity potentials, as it might be the case for a wormhole. We show that it is these high-frequency (quasi-)reflectionless scattering modes, rather than low-frequency resonances, to be directly responsible for the echoes in the time-domain response of ultracompact objects or of black holes surrounded by matter fields localized at large distances.

X-ray observations of the optically selected $z=6.025$ QSO CFHQS J164121+375520 (hereafter J1641) revealed that its flux dropped by a factor $\gtrsim7$ from 2018, when it was a bright and soft X-ray source, to 2021. Such a strong variability amplitude has not been observed before among $z>6$ QSOs, and the underlying physical mechanism was unclear. We carried out a new X-ray and rest-frame UV monitoring campaign of J1641 over 2022-2024. We detected J1641 with Chandra in the 2-7 keV band, while no significant emission is detected at softer X-ray energies, making J1641 an X-ray changing look QSO at $z>6$. Comparing with the 2018 epoch, the 0.5-2 keV flux dropped dramatically by a factor $>20$. We ascribe this behaviour to intervening, and still ongoing, obscuration by Compton-thick gas intercepting our line of sight between 2018 and 2021. The screening material could be an inner disk or a failed nuclear wind that increased their thickness. Another possibility is that we have witnessed an occultation event due to dust-free clouds located at sub-pc/pc scales, similar to those recently invoked to explain the remarkable X-ray weakness of AGN discovered by JWST. These interpretations are also consistent with the lack of strong variations of the QSO rest-frame UV lightcurve over the same period. Future monitoring of J1641 and the possible discovery of other X-ray changing look QSOs at $z>6$ will provide us with precious information about the physics of rapid supermassive black-hole growth at high redshift., Comment: Accepted for publication on A&A Letters

We performed a detailed spectroscopic analysis of three extremely metal-poor RR Lyrae stars, exploring uncharted territories at these low metallicities for this class of stars. Using high-resolution spectra acquired with HARPS-N at TNG, UVES at VLT, and PEPSI at LBT, and employing Non-Local Thermodynamic Equilibrium (NLTE) spectral synthesis calculations, we provide abundance measurements for Fe, Al, Mg, Ca, Ti, Mn, and Sr. Our findings indicate that the stars have metallicities of [Fe/H] = -3.40 \pm 0.05, -3.28 \pm 0.02, and -2.77 \pm 0.05 for HD 331986, DO Hya, and BPS CS 30317-056, respectively. Additionally, we derived their kinematic and dynamical properties to gain insights into their origins. Interestingly, the kinematics of one star (HD 331986) is consistent with the Galactic disc, while the others exhibit Galactic halo kinematics, albeit with distinct chemical signatures. We compared the [Al/Fe] and [Mg/Mn] ratios of the current targets with recent literature estimates to determine whether these stars were either accreted or formed in situ, finding that the adopted chemical diagnostics are ineffective at low metallicities ([Fe/H] $\lesssim -$1.5). Finally, the established horizontal branch evolutionary models, indicating that these stars arrive at hotter temperatures on the Zero-Age Horizontal Branch (ZAHB) and then transition into RR Lyrae stars as they evolve, fully support the existence of such low-metallicity RR Lyrae stars. As a consequence, we can anticipate detecting more of them when larger samples of spectra become available from upcoming extensive observational campaigns., Comment: Accepted for publication in Astronomy & Astrophysics; 13 pages. Appendices D-E available on Zenodo

Optimal prompt selection is crucial for maximizing large language model (LLM) performance on downstream tasks. As the most powerful models are proprietary and can only be invoked via an API, users often manually refine prompts in a black-box setting by adjusting instructions and few-shot examples until they achieve good performance as measured on a validation set. Recent methods addressing static black-box prompt selection face significant limitations: They often fail to leverage the inherent structure of prompts, treating instructions and few-shot exemplars as a single block of text. Moreover, they often lack query-efficiency by evaluating prompts on all validation instances, or risk sub-optimal selection of a prompt by using random subsets of validation instances. We introduce HbBoPs, a novel Hyperband-based Bayesian optimization method for black-box prompt selection addressing these key limitations. Our approach combines a structural-aware deep kernel Gaussian Process to model prompt performance with Hyperband as a multi-fidelity scheduler to select the number of validation instances for prompt evaluations. The structural-aware modeling approach utilizes separate embeddings for instructions and few-shot exemplars, enhancing the surrogate model's ability to capture prompt performance and predict which prompt to evaluate next in a sample-efficient manner. Together with Hyperband as a multi-fidelity scheduler we further enable query-efficiency by adaptively allocating resources across different fidelity levels, keeping the total number of validation instances prompts are evaluated on low. Extensive evaluation across ten benchmarks and three LLMs demonstrate that HbBoPs outperforms state-of-the-art methods.

AI-generated counterspeech offers a promising and scalable strategy to curb online toxicity through direct replies that promote civil discourse. However, current counterspeech is one-size-fits-all, lacking adaptation to the moderation context and the users involved. We propose and evaluate multiple strategies for generating tailored counterspeech that is adapted to the moderation context and personalized for the moderated user. We instruct an LLaMA2-13B model to generate counterspeech, experimenting with various configurations based on different contextual information and fine-tuning strategies. We identify the configurations that generate persuasive counterspeech through a combination of quantitative indicators and human evaluations collected via a pre-registered mixed-design crowdsourcing experiment. Results show that contextualized counterspeech can significantly outperform state-of-the-art generic counterspeech in adequacy and persuasiveness, without compromising other characteristics. Our findings also reveal a poor correlation between quantitative indicators and human evaluations, suggesting that these methods assess different aspects and highlighting the need for nuanced evaluation methodologies. The effectiveness of contextualized AI-generated counterspeech and the divergence between human and algorithmic evaluations underscore the importance of increased human-AI collaboration in content moderation., Comment: Article published in WebConf 25, 34th ACM Web Conference. Please, cite the published version

In this note, we study equivariant versions of Stolz' R-groups, the positive scalar curvature structure groups $R_n(X)^G$, for proper actions of discrete groups $G$. We define the concept of a fundamental groupoid functor for a $G$-space, encapsulating all the fundamental group information of all the fixed point sets and their relations. We construct classifying spaces for fundamental groupoid functors. As a geometric result, we show that Stolz' equivariant R-group $R_n(X)^G$ depends only on the fundamental groupoid functor of the reference space $X$. The proof covers at the same time in a concise and clear way the classical non-equivariant case., Comment: 24 pages, 3 figures

We report on a search for millicharged particles (mCPs) produced in cosmic ray proton atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes -- meson decay and proton bremsstrahlung -- are considered in this study. This search utilized a novel signature unique to liquid xenon (LXe) time projection chambers (TPCs), allowing sensitivity to mCPs with masses ranging from 10 to 1000 MeV/c$^2$ and fractional charges between 0.001 and 0.02 of the electron charge e. With an exposure of 60 live days and a 5.5 tonne fiducial mass, we observed no significant excess over background. This represents the first experimental search for atmospheric mCPs and the first search for mCPs using an underground LXe experiment.

In general relativity with vector and scalar fields given by the Lagrangian ${\cal L}(F,\phi,X)$, where $F$ is a Maxwell term and $X$ is a kinetic term of the scalar field, we study the linear stability of static and spherically symmetric objects without curvature singularities at their centers. We show that the background solutions are generally described by either purely electrically or magnetically charged objects with a nontrivial scalar-field profile. In theories with the Lagrangian $\tilde{{\cal L}}(F)+K(\phi, X)$, which correspond to nonlinear electrodynamics with a k-essence scalar field, angular Laplacian instabilities induced by vector-field perturbations exclude all the regular spherically symmetric solutions including nonsingular black holes. In theories described by the Lagrangian ${\cal L}=X+\mu(\phi)F^n$, where $\mu$ is a function of $\phi$ and $n$ is a constant, the absence of angular Laplacian instabilities of spherically symmetric objects requires that $n>1/2$, under which nonsingular black holes with event horizons are not present. However, for some particular ranges of $n$, there are horizonless compact objects with neither ghosts nor Laplacian instabilities in the small-scale limit. In theories given by ${\cal L}=X \kappa (F)$, where $\kappa$ is a function of $F$, regular spherically symmetric objects are prone to Laplacian instabilities either around the center or at spatial infinity. Thus, in our theoretical framework, we do not find any example of linearly stable nonsingular black holes., Comment: 21 pages, 7 figures

In a cosmological context, the Einstein-Gauss-Bonnet theory contains, in $d+4$ dimensions, a dynamical compactification scenario in which the additional dimensions settle down to a configuration with a constant radion/scale factor. Sadly however this work demonstrates that such a quite appealing framework is plagued by instabilities, either from the background configuration's unsteadiness or the ghostly behaviors of the tensorial perturbations. New and stable solutions are found by relaxing one of the hypotheses defining the original compactification scenario. However, such configurations do not respect the current bounds on the speed of propagation of gravitational waves, and thus have to be discarded. Those results thus advocate for a comprehensive study of compactification scenarios in the Gauss-Bonnet framework, their stability, and the effects of matter inclusion., Comment: 19 pages, uses RevTeX

We take up the investigation we left in the future-work stack in Giordano \textit{et al.} [``Fluid statics of a self-gravitational isothermal sphere of van der Waals' gas,'' Phys. Fluids \textbf{36}, 056127 (2024)], in which we pointed out the obvious necessity to inquire about the existence or absence of values of the characteristic numbers \itm{\alphay} and \itm{\betay} in correspondence to which the perfect-gas model's self gravitational effects, namely, upper boundedness of the gravitational number, spiraling behavior of peripheral density, oscillating behavior of central density, and the existence of multiple solutions corresponding to the same value of the gravitational number, appear also for the van der Waals' model. The development of our investigation brings to the conversion of our M$_{2}$ scheme based on a second-order differential equation into an equivalent system of two first-order differential equations that incorporates Milne's homology invariant variables. The converted scheme \fomt\ turns out to be much more efficacious than the M$_{2}$ scheme in terms of numerical calculations' easiness and richness of results. We use the perfect-gas model as benchmark to test the \fomt\ scheme; we re-derive familiar results and put them in a more general and rational perspective that paves the way to deal with the van der Waals' gas model. We introduce variable transformations that turn out to be the key to study (almost) analytically the monotonicity of the peripheral density with respect to variations of the gravitational number. The study brings to the proof that the gravitational number is not constrained by upper boundedness, the peripheral density does not spiral, and the central density does not oscillate for any couple of values assumed by the characteristic numbers $\alpha$ and $\beta$; however, multiple solutions ..., Comment: Revision according to published article (21 pages, 26 figures, 1 table)

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

While teleparallel $f(T)$ gravity has shown considerable potential in addressing cosmological tensions, such as the $H_0$ and $S_8$ discrepancies, we explore previously overlooked solution spaces within this framework that hold further promise. Specifically, we examine the case where the customary assumption of a strictly positive effective dark energy (DE) density -- natural in general relativity -- may not apply, offering new possibilities. Focusing on the exponential infrared model $f(T)=Te^{\beta T_0/T}$, where $T$ is the torsion scalar, we investigate cosmological solutions characterized by the constant $\beta$, which is controlled by the present-day matter density parameter $\Omega_{\rm m0}$. Ensuring consistency with CMB data, we find that the widely studied $\beta_{+}$ (positive exponent) case exhibits phantom behavior, while the previously overlooked $\beta_{-}$ (negative exponent) case -- sufficient to avoid instabilities or ghosts -- features a sign-changing DE density that transitions smoothly from negative to positive values at redshift $z_{\dagger} \sim 1.5$, consistent with recent approaches to alleviating cosmological tensions. Though the sign-changing DE density in the $f(T)$ model leads to a larger-than-expected enhancement, we further extend the analysis by incorporating a cosmological constant, $\Lambda$. This extension, $f(T) \rightarrow f(T) + 2\Lambda$, broadens the solution space consistent with the SH0ES $H_0$ measurement while maintaining consistency with CMB power spectra. Additionally, it introduces richer phenomenological possibilities, including the potential moderation or cessation of cosmic acceleration at very low redshifts, aligning with recent observational analyses, such as those from DESI BAO data. Our findings also suggest that existing $f(T)$ cosmological models should be revisited in light of the novel theoretical insights presented here., Comment: 22 pages, 8 figures, and no tables

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

We report results of a search for nuclear recoils induced by weakly interacting massive particle (WIMP) dark matter using the LUX-ZEPLIN (LZ) two-phase xenon time projection chamber. This analysis uses a total exposure of $4.2\pm0.1$ tonne-years from 280 live days of LZ operation, of which $3.3\pm0.1$ tonne-years and 220 live days are new. A technique to actively tag background electronic recoils from $^{214}$Pb $\beta$ decays is featured for the first time. Enhanced electron-ion recombination is observed in two-neutrino double electron capture decays of $^{124}$Xe, representing a noteworthy new background. After removal of artificial signal-like events injected into the data set to mitigate analyzer bias, we find no evidence for an excess over expected backgrounds. World-leading constraints are placed on spin-independent (SI) and spin-dependent WIMP-nucleon cross sections for masses $\geq$9 GeV/$c^2$. The strongest SI exclusion set is $2.1\times10^{-48}$ cm$^{2}$ at the 90% confidence level at a mass of 36 GeV/$c^2$, and the best SI median sensitivity achieved is $5.0\times10^{-48}$ cm$^{2}$ for a mass of 40 GeV/$c^2$., Comment: 9 pages, 7 figures. See https://www.hepdata.net/record/155182 for a data release related to this paper

In forecasting multiple time series, accounting for the individual features of each sequence can be challenging. To address this, modern deep learning methods for time series analysis combine a shared (global) model with local layers, specific to each time series, often implemented as learnable embeddings. Ideally, these local embeddings should encode meaningful representations of the unique dynamics of each sequence. However, when these are learned end-to-end as parameters of a forecasting model, they may end up acting as mere sequence identifiers. Shared processing blocks may then become reliant on such identifiers, limiting their transferability to new contexts. In this paper, we address this issue by investigating methods to regularize the learning of local learnable embeddings for time series processing. Specifically, we perform the first extensive empirical study on the subject and show how such regularizations consistently improve performance in widely adopted architectures. Furthermore, we show that methods attempting to prevent the co-adaptation of local and global parameters by means of embeddings perturbation are particularly effective in this context. In this regard, we include in the comparison several perturbation-based regularization methods, going as far as periodically resetting the embeddings during training. The obtained results provide an important contribution to understanding the interplay between learnable local parameters and shared processing layers: a key challenge in modern time series processing models and a step toward developing effective foundation models for time series., Comment: Accepted at TMLR

The rapid expansion of the Internet of Things (IoT) has led to significant data reliability and system transparency challenges, aggravated by the centralized nature of existing IoT architectures. This centralization often results in siloed data ecosystems, where interoperability issues and opaque data handling practices compromise both the utility and trustworthiness of IoT applications. To address these issues, we introduce ZONIA (Zero-trust Oracle Network for IoT Applications), a novel blockchain oracle system designed to enhance data integrity and decentralization in IoT environments. Unlike traditional approaches that rely on Trusted Execution Environments and centralized data sources, ZONIA utilizes a decentralized, zero-trust model that allows for anonymous participation and integrates multiple data sources to ensure fairness and reliability. This paper outlines ZONIA's architecture, which supports semantic and geospatial queries, details its data reliability mechanisms, and presents a comprehensive evaluation demonstrating its scalability and resilience against data falsification and collusion attacks. Both analytical and experimental results demonstrate ZONIA's scalability, showcasing its feasibility to handle an increasing number of nodes in the system under different system conditions and workloads. Furthermore, the implemented reputation mechanism significantly enhances data accuracy, maintaining high reliability even when 40\% of nodes exhibit malicious behavior.

We present a method for gradient computation in quantum algorithms implemented on linear optical quantum computing platforms. While parameter-shift rules have become a staple in qubit gate-based quantum computing for calculating gradients, their direct application to photonic platforms has been hindered by the non-unitary nature of differentiated phase-shift operators in Fock space. We introduce a photonic parameter-shift rule that overcomes this limitation, providing an exact formula for gradient computation in linear optical quantum processors. Our method scales linearly with the number of input photons and utilizes the same parameterized photonic circuit with shifted parameters for each evaluation. This advancement bridges a crucial gap in photonic quantum computing, enabling efficient gradient-based optimization for variational quantum algorithms on near-term photonic quantum processors. We demonstrate the efficacy of our approach through numerical simulations in quantum chemistry and generative modeling tasks, showing superior optimization performance as well as robustness to noise from finite sampling and photon distinguishability compared to other gradient-based and gradient-free methods., Comment: 12 pages (incl. 4 pages for appendices), 3 figures

We show that nonsingular black holes realized in nonlinear electrodynamics are always prone to Laplacian instability around the center because of a negative squared sound speed in the angular direction. This is the case for both electric and magnetic BHs, where the instability of one of the vector-field perturbations leads to enhancing a dynamical gravitational perturbation in the even-parity sector. Thus, the background regular metric is no longer maintained in a steady state. Our results suggest that the construction of stable, nonsingular black holes with regular centers, if they exist, requires theories beyond nonlinear electrodynamics., Comment: 7 pages, 1 figure

Thanks to the rapidly increasing time-domain facilities, we are entering a golden era of research on gamma-ray bursts (GRBs). In this Letter, we report our observations of GRB 240529A with the Burst Optical Observer and Transient Exploring System, the 1.5-meter telescope at Observatorio Sierra Nevada, the 2.5-meter Wide Field Survey Telescope of China, the Large Binocular Telescope, and the Telescopio Nazionale Galileo. The prompt emission of GRB 240529A shows two comparable energetic episodes separated by a quiescence time of roughly 400 s. Combining all available data on the GRB Coordinates Network, we reveal the simultaneous apparent X-ray plateau and optical re-brightening around $10^3-10^4$ s after the burst. Rather than the energy injection from the magnetar as widely invoked for similar GRBs, the multi-wavelength emissions could be better explained as two shocks launched from the central engine separately. The optical peak time and our numerical modeling suggest that the initial bulk Lorentz factor of the later shock is roughly 50, which indicates that the later jet should be accretion-driven and have a higher mass loading than a typical one. The quiescence time between the two prompt emission episodes may be caused by the transition between different accretion states of a central magnetar or black hole, or the fall-back accretion process. A sample of similar bursts with multiple emission episodes in the prompt phase and sufficient follow-up could help to probe the underlying physics of GRB central engines., Comment: Resubmitted to ApJL after addressing the referee's comments; comments are welcome

In a subclass of generalized Proca theories where a cubic vector Galileon term breaks the $U(1)$ gauge invariance, it is known that there are static and spherically symmetric black hole (BH) solutions endowed with nonvanishing temporal and longitudinal vector components. Such hairy BHs are present for a vanishing vector-field mass ($m=0$) with a non-zero cubic Galileon coupling $\beta_3$. We study the linear stability of those hairy BHs by considering even-parity perturbations in the eikonal limit. In the angular direction, we show that one of the three dynamical perturbations has a nontrivial squared propagation speed $c_{\Omega,1}^2$, while the other two dynamical modes are luminal. We could detect two different unstable behaviors of perturbations in all the parameter spaces of hairy asymptotically flat BH solutions we searched for. In the first case, an angular Laplacian instability on the horizon is induced by negative $c_{\Omega,1}^2$. For the second case, it is possible to avoid this horizon instability, but in such cases, the positivity of $c_{\Omega,1}^2$ is violated at large distances. Hence these hairy BHs are generally prone to Laplacian instabilities along the angular direction in some regions outside the horizon. Moreover, we also encounter a pathological behavior of the radial propagation speeds $c_r$ possessing two different values of $c_r^2$ for one of the dynamical perturbations. Introducing the vector-field mass $m$ to cubic vector Galileons, however, we show that the resulting no-hair Schwarzschild BH solution satisfies all the linear stability conditions in the small-scale limit, with luminal propagation speeds of three dynamical even-parity perturbations., Comment: 19 pages, 2 figures

Photonics offers a promising platform for implementations of measurement-based quantum computing. Recently proposed fusion-based architectures aim to achieve universality and fault-tolerance. In these approaches, computation is carried out by performing fusion and single-qubit measurements on a resource graph state. The verification of these architectures requires linear algebraic, probabilistic, and control flow structures to be combined in a unified formal language. This paper develops a framework for photonic quantum computing by bringing together linear optics, ZX calculus, and dataflow programming. We characterize fusion measurements that induce Pauli errors and show that they are correctable using a novel flow structure for fusion networks. We prove the correctness of new repeat-until-success protocols for the realization of arbitrary fusions and provide a graph-theoretic proof of universality for linear optics with entangled photon sources. The proposed framework paves the way for the development of compilation algorithms for photonic quantum computing.

The broad physics reach of the LUX-ZEPLIN (LZ) experiment covers rare phenomena beyond the direct detection of dark matter. We report precise measurements of the extremely rare decay of $^{124}$Xe through the process of two-neutrino double electron capture (2$\nu$2EC), utilizing a $1.39\,\mathrm{kg} \times \mathrm{yr}$ isotopic exposure from the first LZ science run. A half-life of $T_{1/2}^{2\nu2\mathrm{EC}} = (1.09 \pm 0.14_{\text{stat}} \pm 0.05_{\text{sys}}) \times 10^{22}\,\mathrm{yr}$ is observed with a statistical significance of $8.3\,\sigma$, in agreement with literature. First empirical measurements of the KK capture fraction relative to other K-shell modes were conducted, and demonstrate consistency with respect to recent signal models at the $1.4\,\sigma$ level., Comment: 15 pages, 3 figures

Retrieval Augmented Generation (RAG) is a technique used to augment Large Language Models (LLMs) with contextually relevant, time-critical, or domain-specific information without altering the underlying model parameters. However, constructing RAG systems that can effectively synthesize information from large and diverse set of documents remains a significant challenge. We introduce a novel data-centric RAG workflow for LLMs, transforming the traditional retrieve-then-read system into a more advanced prepare-then-rewrite-then-retrieve-then-read framework, to achieve higher domain expert-level understanding of the knowledge base. Our methodology relies on generating metadata and synthetic Questions and Answers (QA) for each document, as well as introducing the new concept of Meta Knowledge Summary (MK Summary) for metadata-based clusters of documents. The proposed innovations enable personalized user-query augmentation and in-depth information retrieval across the knowledge base. Our research makes two significant contributions: using LLMs as evaluators and employing new comparative performance metrics, we demonstrate that (1) using augmented queries with synthetic question matching significantly outperforms traditional RAG pipelines that rely on document chunking (p < 0.01), and (2) meta knowledge-augmented queries additionally significantly improve retrieval precision and recall, as well as the final answers breadth, depth, relevancy, and specificity. Our methodology is cost-effective, costing less than $20 per 2000 research papers using Claude 3 Haiku, and can be adapted with any fine-tuning of either the language or embedding models to further enhance the performance of end-to-end RAG pipelines., Comment: Accepted in Workshop on Generative AI for Recommender Systems and Personalization, KDD 2024

Mobile app review analysis presents unique challenges due to the low quality, subjective bias, and noisy content of user-generated documents. Extracting features from these reviews is essential for tasks such as feature prioritization and sentiment analysis, but it remains a challenging task. Meanwhile, encoder-only models based on the Transformer architecture have shown promising results for classification and information extraction tasks for multiple software engineering processes. This study explores the hypothesis that encoder-only large language models can enhance feature extraction from mobile app reviews. By leveraging crowdsourced annotations from an industrial context, we redefine feature extraction as a supervised token classification task. Our approach includes extending the pre-training of these models with a large corpus of user reviews to improve contextual understanding and employing instance selection techniques to optimize model fine-tuning. Empirical evaluations demonstrate that this method improves the precision and recall of extracted features and enhances performance efficiency. Key contributions include a novel approach to feature extraction, annotated datasets, extended pre-trained models, and an instance selection mechanism for cost-effective fine-tuning. This research provides practical methods and empirical evidence in applying large language models to natural language processing tasks within mobile app reviews, offering improved performance in feature extraction., Comment: 46 pages, 7 tables, 11 figures

Recently, machine learning had a remarkable impact, from scientific to everyday-life applications. However, complex tasks often imply unfeasible energy and computational power consumption. Quantum computation might lower such requirements, although it is unclear whether enhancements are reachable by current technologies. Here, we demonstrate a kernel method on a photonic integrated processor to perform a binary classification. We show that our protocol outperforms state-of-the-art kernel methods including gaussian and neural tangent kernels, exploiting quantum interference, and brings a smaller improvement also by single photon coherence. Our scheme does not require entangling gates and can modify the system dimension through additional modes and injected photons. This result opens to more efficient algorithms and to formulating tasks where quantum effects improve standard methods.

Over summer 2024, the world will be looking at Paris to encourage their favorite athletes win the Olympic gold medal. In handball, few nations will fight hard to win the precious metal with speculations predicting the victory for France or Denmark for men and France or Norway for women. However, there is so far no scientific method proposed to predict the final results of the competition. In this work, we leverage a deep learning model to predict the results of the handball tournament of the 2024 Olympic Games. This model, coupled with explainable AI (xAI) techniques, allows us to extract insightful information about the main factors influencing the outcome of each match. Notably, xAI helps sports experts understand how factors like match information or individual athlete performance contribute to the predictions. Furthermore, we integrate Large Language Models (LLMs) to generate human-friendly explanations that highlight the most important factors impacting the match results. By providing human-centric explanations, our approach offers a deeper understanding of the AI predictions, making them more actionable for coaches and analysts.