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Since the advent of mobile robots, obstacle detection has been a topic of great interest. It has also been a subject of study in neuroscience, where flying insects and bats could be considered two of the most interesting cases in terms of vision-based and sound-based mechanisms for obstacle detection, respectively. Currently, many studies focus on vision-based obstacle detection, but not many can be found regarding sound-based obstacle detection. This work focuses on the latter approach, which also makes use of a Spiking Neural Network to exploit the advantages of these architectures and achieve an approach closer to biology. The complete system was tested through a series of experiments that confirm the validity of the spiking architecture for obstacle detection. It is empirically demonstrated that, when the distance between the robot and the obstacle decreases, the output firing rate of the system increases in response as expected, and vice versa. Therefore, there is a direct relation between the two. Furthermore, there is a distance threshold between detectable and undetectable objects which is also empirically measured in this work. An in-depth study on how this system works at low level based on the Inter-Spike Interval concept was performed, which may be useful in the future development of applications based on spiking filters., Comment: 22 pages, 8 figures

Instruments for gamma-ray astronomy at Very High Energies ($E>100\,{\rm GeV}$) have traditionally derived their scientific results through proprietary data and software. Data standardisation has become a prominent issue in this field both as a requirement for the dissemination of data from the next generation of gamma-ray observatories and as an effective solution to realise public data legacies of current-generation instruments. Specifications for a standardised gamma-ray data format have been proposed as a community effort and have already been successfully adopted by several instruments. We present the first production of standardised data from the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes. We converted $166\,{\rm h}$ of observations from different sources and validated their analysis with the open-source software Gammapy. We consider six data sets representing different scientific and technical analysis cases and compare the results obtained analysing the standardised data with open-source software against those produced with the MAGIC proprietary data and software. Aiming at a systematic production of MAGIC data in this standardised format, we also present the implementation of a database-driven pipeline automatically performing the MAGIC data reduction from the calibrated down to the standardised data level. In all the cases selected for the validation, we obtain results compatible with the MAGIC proprietary software, both for the manual and for the automatic data productions. Part of the validation data set is also made publicly available, thus representing the first large public release of MAGIC data. This effort and this first data release represent a technical milestone toward the realisation of a public MAGIC data legacy., Comment: Accepted for publication in the Journal of High Energy Astrophysics

Creation of synthetic data models has represented a significant advancement across diverse scientific fields, but this technology also brings important privacy considerations for users. This work focuses on enhancing a non-parametric copula-based synthetic data generation model, DPNPC, by incorporating Differential Privacy through an Enhanced Fourier Perturbation method. The model generates synthetic data for mixed tabular databases while preserving privacy. We compare DPNPC with three other models (PrivBayes, DP-Copula, and DP-Histogram) across three public datasets, evaluating privacy, utility, and execution time. DPNPC outperforms others in modeling multivariate dependencies, maintaining privacy for small $\epsilon$ values, and reducing training times. However, limitations include the need to assess the model's performance with different encoding methods and consider additional privacy attacks. Future research should address these areas to enhance privacy-preserving synthetic data generation., Comment: 12 pages, 5 figures, deciding 2025 conference to which to submit

In this theoretical article, a sociotechnical approach is proposed to characterize. First, the business ecosystem, focusing on the relationships among Providers, Enterprise, and Customers through SCM, ERP, and CRM platforms to align: (1) Business Intelligence (BI), Fuzzy Logic (FL), and TRIZ (Theory of Inventive Problem Solving), through the OID model, and (2) Knowledge Management (KM) and Imperfect Knowledge Management (IKM), through the OIDK model. Second, the article explores the E-GenAI business ecosystem, which integrates GenAI-based platforms for SCM, ERP, and CRM with GenAI-based platforms for BI, FL, TRIZ, KM, and IKM, to align Large Language Models (LLMs) through the E-GenAI (OID) model. Finally, to understand the dynamics of LLMs, we utilize finite automata to model the relationships between Followers and Followees. This facilitates the construction of LLMs that can identify specific characteristics of users on a social media platform.

Although language model (LM) agents are demonstrating growing potential in many domains, their success in cybersecurity has been limited due to simplistic design and the lack of fundamental features for this domain. We present EnIGMA, an LM agent for autonomously solving Capture The Flag (CTF) challenges. EnIGMA introduces new Agent-Computer Interfaces (ACIs) to improve the success rate on CTF challenges. We establish the novel Interactive Agent Tool concept, which enables LM agents to run interactive command-line utilities essential for these challenges. Empirical analysis of EnIGMA on over 350 CTF challenges from three different benchmarks indicates that providing a robust set of new tools with demonstration of their usage helps the LM solve complex problems and achieves state-of-the-art results on the NYU CTF and Intercode-CTF benchmarks. Finally, we discuss insights on ACI design and agent behavior on cybersecurity tasks that highlight the need to adapt real-world tools for LM agents.

This study examines the factors that determine workplace autonomy in the choice of schedules, using GEIH data for formal employees from January 2023 to February 2024. A logistic regression model identified significant relationships between autonomy and variables such as company size, income, tenure, workplace, and job satisfaction. The results highlight the complexity of these factors and suggest the need for improvements in the predictive model., Comment: The manuscript contains 15 pages and 3 figures. It analyzes workplace autonomy in schedule flexibility using GEIH data from January 2023 to February 2024. A logistic regression model evaluates the impact of variables like labor income, company size, tenure, workplace, and job satisfaction. Results suggest the need for further model refinements

SN 2023tsz is a Type Ibn supernova (SNe Ibn) discovered in an extremely low-mass host. SNe Ibn are an uncommon subtype of stripped-envelope core-collapse SNe. They are characterised by narrow helium emission lines in their spectra and are believed to originate from the collapse of massive Wolf-Rayet (WR) stars, though their progenitor systems still remain poorly understood. In terms of energetics and spectrophotometric evolution, SN 2023tsz is largely a typical example of the class, although line profile asymmetries in the nebular phase are seen, which may indicate the presence of dust formation or unshocked circumstellar material. Intriguingly, SN 2023tsz is located in an extraordinarily low-mass host galaxy that is in the 2nd percentile for SESN host masses and star formation rates (SFR). The host has a radius of 1.0 kpc, a $g$-band absolute magnitude of $-12.73$, and an estimated metallicity of $\log(Z_{*}/Z_{\odot}$) = $-1.56$. The SFR and metallicity of the host galaxy raise questions about the progenitor of SN 2023tsz. The low SFR suggests that a star with sufficient mass to evolve into a WR would be uncommon in this galaxy. Further, the very low-metallicity is a challenge for single stellar evolution to enable H and He stripping of the progenitor and produce a SN Ibn explosion. The host galaxy of SN 2023tsz adds another piece to the ongoing puzzle of SNe Ibn progenitors, and demonstrates that they can occur in hosts too faint to be observed in contemporary sky surveys at a more typical SN Ibn redshift., Comment: 14 pages, 10 figures, submitted to MNRAS

In this article, we examine a stochastic partial differential equation (SPDE) driven by a symmetric $\alpha$-stable (S$\alpha$S) L\'evy noise, that is multiplied by a linear function $\sigma(u)=u$ of the solution. The solution is interpreted in the mild sense. For this models, in the case of the Gaussian noise, the solution has an explicit Wiener chaos expansion, and is studied using tools from Malliavin calculus. These tools cannot be used for an infinite-variance L\'evy noise. In this article, we provide sufficient conditions for the existence of a solution, and we give an explicit series expansion of this solution. To achieve this, we use the multiple stable integrals, which were developed in Samorodnitsky and Taqqu (1990, 1991), and originate from the LePage series representation of the noise. To give a meaning to the stochastic integral which appears in the definition of solution, we embed the space-time L\'evy noise into a L\'evy basis, and use the stochastic integration theory (Bichteler and Jacod 1983, Bichteler 2002) with respect to this object, as in other studies of SPDEs with heavy-tailed noise: Chong (2017a), Chong (2017b), Chong, Dalang and Humeau (2019). As applications, we consider the heat and wave equations with linear multiplicative noise, also called the parabolic/hyperbolic Anderson models., Comment: 56 pages, 2 figures

The Milky Way's Central Molecular Zone (CMZ) differs dramatically from our local solar neighbourhood, both in the extreme interstellar medium conditions it exhibits (e.g. high gas, stellar, and feedback density) and in the strong dynamics at play (e.g. due to shear and gas influx along the bar). Consequently, it is likely that there are large-scale physical structures within the CMZ that cannot form elsewhere in the Milky Way. In this paper, we present new results from the Atacama Large Millimeter/submillimeter Array (ALMA) large programme ACES (ALMA CMZ Exploration Survey) and conduct a multi-wavelength and kinematic analysis to determine the origin of the M0.8$-$0.2 ring, a molecular cloud with a distinct ring-like morphology. We estimate the projected inner and outer radii of the M0.8$-$0.2 ring to be 79" and 154", respectively (3.1 pc and 6.1 pc at an assumed Galactic Centre distance of 8.2 kpc) and calculate a mean gas density $> 10^{4}$ cm$^{-3}$, a mass of $\sim$ $10^6$ M$_\odot$, and an expansion speed of $\sim$ 20 km s$^{-1}$, resulting in a high estimated kinetic energy ($> 10^{51}$ erg) and momentum ($> 10^7$ M$_\odot$ km s$^{-1}$). We discuss several possible causes for the existence and expansion of the structure, including stellar feedback and large-scale dynamics. We propose that the most likely cause of the M0.8$-$0.2 ring is a single high-energy hypernova explosion. To viably explain the observed morphology and kinematics, such an explosion would need to have taken place inside a dense, very massive molecular cloud, the remnants of which we now see as the M0.8$-$0.2 ring. In this case, the structure provides an extreme example of how supernovae can affect molecular clouds., Comment: 17 pages, 13 figures, and 2 tables. Accepted for publication in Astronomy & Astrophysics

We introduce the Faetar Automatic Speech Recognition Benchmark, a benchmark corpus designed to push the limits of current approaches to low-resource speech recognition. Faetar, a Franco-Proven\c{c}al variety spoken primarily in Italy, has no standard orthography, has virtually no existing textual or speech resources other than what is included in the benchmark, and is quite different from other forms of Franco-Proven\c{c}al. The corpus comes from field recordings, most of which are noisy, for which only 5 hrs have matching transcriptions, and for which forced alignment is of variable quality. The corpus contains an additional 20 hrs of unlabelled speech. We report baseline results from state-of-the-art multilingual speech foundation models with a best phone error rate of 30.4%, using a pipeline that continues pre-training on the foundation model using the unlabelled set.

To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double S\'ersic profile, neglecting the influence of galaxy substructures and morphologies deviating from such a simplified parametric characterization. While this approximation may be sufficient for previous data sets, the stringent cosmic shear calibration requirements and the high quality of the data in the upcoming Euclid survey demand a consideration of the effects that realistic galaxy substructures have on shear measurement biases. Here we present a novel deep learning-based method to create such simulated galaxies directly from HST data. We first build and validate a convolutional neural network based on the wavelet scattering transform to learn noise-free representations independent of the point-spread function of HST galaxy images that can be injected into simulations of images from Euclid's optical instrument VIS without introducing noise correlations during PSF convolution or shearing. Then, we demonstrate the generation of new galaxy images by sampling from the model randomly and conditionally. Next, we quantify the cosmic shear bias from complex galaxy shapes in Euclid-like simulations by comparing the shear measurement biases between a sample of model objects and their best-fit double-S\'ersic counterparts. Using the KSB shape measurement algorithm, we find a multiplicative bias difference between these branches with realistic morphologies and parametric profiles on the order of $6.9\times 10^{-3}$ for a realistic magnitude-S\'ersic index distribution. Moreover, we find clear detection bias differences between full image scenes simulated with parametric and realistic galaxies, leading to a bias difference of $4.0\times 10^{-3}$ independent of the shape measurement method. This makes it relevant for stage IV weak lensing surveys such as Euclid., Comment: Submitted to A&A. 29 pages, 20 figures, 2 tables

This paper introduces the Relative Inequality Index at the Maximum (IDRM), a novel and intuitive measure designed to capture inequality within a population, such as income inequality. The index is based on the idea that individuals experience varying levels of inequality depending on their position within the distribution, particularly with respect to those at the top. The key assumption is that for individuals in lower positions, inequalities referenced to the top positions have greater impact on their well-being and the inequality relative to maximum is the most critical. The IDRM fulfills desirable theoretical properties which were used for its evaluation and comparison against widely accepted measures in inequality literature. From this perspective, the IDRM is shown to be as robust as traditional measures and outperforms the Gini and Dalton indices by satisfying eight out of nine key properties, including decomposability across population subgroups. In a comparative analysis using income data from 58 countries and microdata from Mexico, with the Gini, Theil, and Atkinson indices as benchmarks, the IDRM demonstrates superior consistency, sensitivity to inequality, reduced bias in grouped data, and enhanced precision. This index reflects the varying forms of income distribution, showing heightened sensitivity to the magnitude of inequality., Comment: 38 pages, in Spanish language, 6 tables, 4 figures

The mapping class group ${\Gamma}_g^ 1$ of a closed orientable surface of genus $g \geq 1$ with one marked point can be identified, by the Nielsen action, with a subgroup of the group of orientation preserving homeomorphims of the circle. This inclusion pulls back the powers of the discrete universal Euler class producing classes $\text{E}^n \in H^{2n}({\Gamma}_g^1;\mathbb{Z})$ for all $n\geq 1$. In this paper we study the power $n=g,$ and prove: $\text{E}^g$ is a torsion class which generates a cyclic subgroup of $H^{2g}({\Gamma}_g^1;\mathbb{Z})$ whose order is a positive integer multiple of $4g(2g+1)(2g-1)$., Comment: 23 pages, 2 figures. Comments welcome!

We study the design of robust and agile controllers for hybrid underactuated systems. Our approach breaks down the task of creating a stabilizing controller into: 1) learning a mapping that is invariant under optimal control, and 2) driving the actuated coordinates to the output of that mapping. This approach, termed Zero Dynamics Policies, exploits the structure of underactuation by restricting the inputs of the target mapping to the subset of degrees of freedom that cannot be directly actuated, thereby achieving significant dimension reduction. Furthermore, we retain the stability and constraint satisfaction of optimal control while reducing the online computational overhead. We prove that controllers of this type stabilize hybrid underactuated systems and experimentally validate our approach on the 3D hopping platform, ARCHER. Over the course of 3000 hops the proposed framework demonstrates robust agility, maintaining stable hopping while rejecting disturbances on rough terrain., Comment: 8 pages, 6 figures, IROS '24

The Gemini South telescope is now equipped with a new high-resolution spectrograph called GHOST (the Gemini High-resolution Optical SpecTrograph). This instrument provides high-efficiency, high-resolution spectra covering 347-1060 nm in a single exposure of either one or two targets simultaneously, along with precision radial velocity spectroscopy utilizing an internal calibration source. It can operate at a spectral element resolving power of either 76000 or 56000, and can reach a SNR$\sim$5 in a 1hr exposure on a V$\sim$20.8 mag target in median site seeing, and dark skies (per resolution element). GHOST was installed on-site in June 2022, and we report performance after full integration to queue operations in November 2023, in addition to scientific results enabled by the integration observing runs. These results demonstrate the ability to observe a wide variety of bright and faint targets with high efficiency and precision. With GHOST, new avenues to explore high-resolution spectroscopy have opened up to the astronomical community. These are described, along with the planned and potential upgrades to the instrument., Comment: Accepted in the Astronomical Journal; 26 pages, 24 figures and 4 tables

While elementary particles are the favored candidate for the elusive dark matter, primordial black holes (PBHs) have also been considered to fill that role. Gravitational microlensing is a very well-suited tool to detect and measure the abundance of compact objects in galaxies. Previous studies based on quasar microlensing exclude a significant presence of substellar to intermediate-mass BHs ($\lesssim 100\,\mathrm{M}_\odot$). However, these studies were based on a spatially uniform distribution of BHs while, according to current theories of PBHs formation, they are expected to appear in clusters. We study the impact of clustering in microlensing flux magnification finding that at large scales clusters act like giant pseudo-particles, strongly affecting the emission coming from the Broad Line Region, which can no longer be used to define the zero microlensing baseline. As an alternative, we set this baseline from the intrinsic magnification ratios of quasar images predicted by macro lens models and compare them with the observed flux ratios in emission lines, infrared (IR), and radio. The (magnitude) differences are the flux-ratio anomalies attributable to microlensing, which we estimate for 35 image pairs corresponding to 12 lens systems. A Bayesian analysis indicates that the observed anomalies are incompatible with the existence of a significant population of clustered PBHs. Furthermore, we find that more compact clusters exhibit a stronger microlensing impact. Consequently, we conclude that clustering makes the existence of a significant population of BHs in the substellar to intermediate mass range even more unlikely.

In \cite{Ha86} Harer explicitly constructed a spine for the decorated Teichm\"uller space of orientable surfaces with at least one puncture and negative Euler characteristic. In this paper we point out some instances where his computation of the dimension of this spine is off by $1$ and give the correct dimension., Comment: 10 pages, 4 figures

We present a benchmarking protocol that combines the characterization of boron nitride (BN) crystals and films with the evaluation of the electronic properties of graphene on these substrates. Our study includes hBN crystals grown under different conditions and scalable BN films deposited by either chemical or physical vapor deposition (CVD or PVD). We explore the complete process from boron nitride growth, over its optical characterization by time-resolved cathodoluminescence (TRCL), to the optical and electronic characterization of graphene by Raman spectroscopy after encapsulation and Hall bar processing. Within our benchmarking protocol we achieve a homogeneous electronic performance within each Hall bar device through a fast and reproducible processing routine. We find that a free exciton lifetime of 1 ns measured on as-grown hBN crystals by TRCL is sufficient to achieve high graphene room temperature charge carrier mobilities of 80,000 cm$^2$/(Vs) at a carrier density of |n| = 10$^{12}$ cm$^{-2}$, while respective exciton lifetimes around 100 ps yield mobilities up to 30,000 cm$^2$/(Vs). For scalable PVD-grown BN films, we measure carrier mobilities exceeding 10,000 cm$^2$/(Vs) which correlates with a graphene Raman 2D peak linewidth of 22 cm$^{-1}$. Our work highlights the importance of the Raman 2D linewidth of graphene as a critical metric that effectively assesses the interface quality (i.e. surface roughness) to the BN substrate, which directly affects the charge carrier mobility of graphene. Graphene 2D linewidth analysis is suitable for all BN substrates and is particularly advantageous when TRCL or BN Raman spectroscopy cannot be applied to specific BN materials such as amorphous or thin films. This underlines the superior role of spatially-resolved spectroscopy in the evaluation of BN crystals and films for the use of high-mobility graphene devices., Comment: 18 pages, 11 figures

We study the constraint on $f(R)$ gravity that can be obtained by photometric primary probes of the Euclid mission. Our focus is the dependence of the constraint on the theoretical modelling of the nonlinear matter power spectrum. In the Hu-Sawicki $f(R)$ gravity model, we consider four different predictions for the ratio between the power spectrum in $f(R)$ and that in $\Lambda$CDM: a fitting formula, the halo model reaction approach, ReACT and two emulators based on dark matter only $N$-body simulations, FORGE and e-Mantis. These predictions are added to the MontePython implementation to predict the angular power spectra for weak lensing (WL), photometric galaxy clustering and their cross-correlation. By running Markov Chain Monte Carlo, we compare constraints on parameters and investigate the bias of the recovered $f(R)$ parameter if the data are created by a different model. For the pessimistic setting of WL, one dimensional bias for the $f(R)$ parameter, $\log_{10}|f_{R0}|$, is found to be $0.5 \sigma$ when FORGE is used to create the synthetic data with $\log_{10}|f_{R0}| =-5.301$ and fitted by e-Mantis. The impact of baryonic physics on WL is studied by using a baryonification emulator BCemu. For the optimistic setting, the $f(R)$ parameter and two main baryon parameters are well constrained despite the degeneracies among these parameters. However, the difference in the nonlinear dark matter prediction can be compensated by the adjustment of baryon parameters, and the one-dimensional marginalised constraint on $\log_{10}|f_{R0}|$ is biased. This bias can be avoided in the pessimistic setting at the expense of weaker constraints. For the pessimistic setting, using the $\Lambda$CDM synthetic data for WL, we obtain the prior-independent upper limit of $\log_{10}|f_{R0}|< -5.6$. Finally, we implement a method to include theoretical errors to avoid the bias., Comment: 24 pages, 16 figures, submitted on behalf of the Euclid Collaboration

The Euclid mission will measure cosmological parameters with unprecedented precision. To distinguish between cosmological models, it is essential to generate realistic mock observables from cosmological simulations that were run in both the standard $\Lambda$-cold-dark-matter ($\Lambda$CDM) paradigm and in many non-standard models beyond $\Lambda$CDM. We present the scientific results from a suite of cosmological N-body simulations using non-standard models including dynamical dark energy, k-essence, interacting dark energy, modified gravity, massive neutrinos, and primordial non-Gaussianities. We investigate how these models affect the large-scale-structure formation and evolution in addition to providing synthetic observables that can be used to test and constrain these models with Euclid data. We developed a custom pipeline based on the Rockstar halo finder and the nbodykit large-scale structure toolkit to analyse the particle output of non-standard simulations and generate mock observables such as halo and void catalogues, mass density fields, and power spectra in a consistent way. We compare these observables with those from the standard $\Lambda$CDM model and quantify the deviations. We find that non-standard cosmological models can leave significant imprints on the synthetic observables that we have generated. Our results demonstrate that non-standard cosmological N-body simulations provide valuable insights into the physics of dark energy and dark matter, which is essential to maximising the scientific return of Euclid., Comment: 22 pages, 7 figures

To constrain models beyond $\Lambda$CDM, the development of the Euclid analysis pipeline requires simulations that capture the nonlinear phenomenology of such models. We present an overview of numerical methods and $N$-body simulation codes developed to study the nonlinear regime of structure formation in alternative dark energy and modified gravity theories. We review a variety of numerical techniques and approximations employed in cosmological $N$-body simulations to model the complex phenomenology of scenarios beyond $\Lambda$CDM. This includes discussions on solving nonlinear field equations, accounting for fifth forces, and implementing screening mechanisms. Furthermore, we conduct a code comparison exercise to assess the reliability and convergence of different simulation codes across a range of models. Our analysis demonstrates a high degree of agreement among the outputs of different simulation codes, providing confidence in current numerical methods for modelling cosmic structure formation beyond $\Lambda$CDM. We highlight recent advances made in simulating the nonlinear scales of structure formation, which are essential for leveraging the full scientific potential of the forthcoming observational data from the Euclid mission., Comment: 20 pages, 7 figures, 1 appendix; submitted on behalf of the Euclid Collaboration

In the present work, we further study the computational power of virus machines (VMs in short). VMs provide a computing paradigm inspired by the transmission and replication networks of viruses. VMs consist of process units (called hosts) structured by a directed graph whose arcs are called channels and an instruction graph that controls the transmissions of virus objects among hosts. The present work complements our understanding of the computing power of VMs by introducing normal forms; these expressions restrict the features in a given computing model. Some of the features that we restrict in our normal forms include (a) the number of hosts, (b) the number of instructions, and (c) the number of virus objects in each host. After we recall some known results on the computing power of VMs we give our normal forms, such as the size of the loops in the network, proving new characterisations of family of sets, such as the finite sets, semilinear sets, or NRE.

We investigate the level of accuracy and precision of cluster weak-lensing (WL) masses measured with the \Euclid data processing pipeline. We use the DEMNUni-Cov $N$-body simulations to assess how well the WL mass probes the true halo mass, and, then, how well WL masses can be recovered in the presence of measurement uncertainties. We consider different halo mass density models, priors, and mass point estimates. WL mass differs from true mass due to, e.g., the intrinsic ellipticity of sources, correlated or uncorrelated matter and large-scale structure, halo triaxiality and orientation, and merging or irregular morphology. In an ideal scenario without observational or measurement errors, the maximum likelihood estimator is the most accurate, with WL masses biased low by $\langle b_M \rangle = -14.6 \pm 1.7 \, \%$ on average over the full range $M_\text{200c} > 5 \times 10^{13} \, M_\odot$ and $z < 1$. Due to the stabilising effect of the prior, the biweight, mean, and median estimates are more precise. The scatter decreases with increasing mass and informative priors significantly reduce the scatter. Halo mass density profiles with a truncation provide better fits to the lensing signal, while the accuracy and precision are not significantly affected. We further investigate the impact of additional sources of systematic uncertainty on the WL mass, namely the impact of photometric redshift uncertainties and source selection, the expected performance of \Euclid cluster detection algorithms, and the presence of masks. Taken in isolation, we find that the largest effect is induced by non-conservative source selection. This effect can be mostly removed with a robust selection. As a final \Euclid-like test, we combine systematic effects in a realistic observational setting and find results similar to the ideal case, $\langle b_M \rangle = - 15.5 \pm 2.4 \, \%$, under a robust selection.

High-harmonic generation from solids can be utilized as probe of ultrafast dynamics, but thus far only over extended sample areas, since its spatial resolution is diffraction-limited. Here we propose spatial polarization gating, that is using a spatially varying ellipticity of a driving laser pulse to reduce the spatial profile of high-harmonic emission below the diffraction limit and hence increase spatial resolution. We show experimentally and by numerical simulations that our method is generally applicable as suppressing high harmonics in elliptical fields is a common response in all solids. We also briefly explore the possibility of applying this technique to widefield imaging, specifically to nonlinear structured illumination microscopy. Our findings indicate that spatial polarization gating can enable all-optical femto-to-attosecond label-free imaging beyond the Abbe limit., Comment: 11 pages, 8 figures (incl. supplementary material)

The Euclid mission, designed to map the geometry of the dark Universe, presents an unprecedented opportunity for advancing our understanding of the cosmos through its photometric galaxy cluster survey. This paper focuses on enhancing the precision of halo bias (HB) predictions, which is crucial for deriving cosmological constraints from the clustering of galaxy clusters. Our study is based on the peak-background split (PBS) model linked to the halo mass function (HMF); it extends with a parametric correction to precisely align with results from an extended set of $N$-body simulations carried out with the OpenGADGET3 code. Employing simulations with fixed and paired initial conditions, we meticulously analyze the matter-halo cross-spectrum and model its covariance using a large number of mock catalogs generated with Lagrangian Perturbation Theory simulations with the PINOCCHIO code. This ensures a comprehensive understanding of the uncertainties in our HB calibration. Our findings indicate that the calibrated HB model is remarkably resilient against changes in cosmological parameters including those involving massive neutrinos. The robustness and adaptability of our calibrated HB model provide an important contribution to the cosmological exploitation of the cluster surveys to be provided by the Euclid mission. This study highlights the necessity of continuously refining the calibration of cosmological tools like the HB to match the advancing quality of observational data. As we project the impact of our model on cosmological constraints, we find that, given the sensitivity of the Euclid survey, a miscalibration of the HB could introduce biases in cluster cosmology analyses. Our work fills this critical gap, ensuring the HB calibration matches the expected precision of the Euclid survey. The implementation of our model is publicly available in https://github.com/TiagoBsCastro/CCToolkit., Comment: 20 pages; 12 figures; accepted for publication in A&A; abstract abridged for arXiv submission

This article is interested in internality to the constants of systems of autonomous algebraic ordinary differential equations. Roughly, this means determining when can all solutions of such a system be written as a rational function of finitely many fixed solutions (and their derivatives) and finitely many constants. If the system is a single order one equation, the answer was given in an old article of Rosenlicht. In the present work, we completely answer this question for a large class of systems. As a corollary, we obtain a necessary condition for the generic solution to be Liouvillian. We then apply these results to determine exactly when solutions to Poizat equations (a special case of Li\'enard equations) are internal, answering a question of Freitag, Jaoui, Marker and Nagloo, and to the classic Lotka-Volterra system, showing that its generic solutions are almost never Liouvillian., Comment: 29 pages

Diffusion MRI is a powerful tool that serves as a bridge between brain microstructure and cognition. Recent advancements in cognitive neuroscience have highlighted the persistent challenge of understanding how individual differences in brain structure influence behavior, especially in healthy people. While traditional linear models like Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS) have been fundamental in this analysis, they face limitations, particularly with high-dimensional data analysis outside the training sample. To address these issues, we introduce a novel approach using deep learninga multivariate autoencoder model-to explore the complex non-linear relationships between brain microstructure and cognitive functions. The model's architecture involves separate encoder modules for brain structure and cognitive data, with a shared decoder, facilitating the analysis of multivariate patterns across these domains. Both encoders were trained simultaneously, before the decoder, to ensure a good latent representation that captures the phenomenon. Using data from the Human Connectome Project, our study centres on the insula's role in cognitive processes. Through rigorous validation, including 5 sample analyses for out-of-sample analysis, our results demonstrate that the multivariate autoencoder model outperforms traditional methods in capturing and generalizing correlations between brain and behavior beyond the training sample. These findings underscore the potential of deep learning models to enhance our understanding of brain-behavior relationships in cognitive neuroscience, offering more accurate and comprehensive insights despite the complexities inherent in neuroimaging studies.

Quantum Communications is a field that promises advances in cryptography, quantum computing and clock synchronisation, among other potential applications. However, communication based on quantum phenomena requires an extreme level of isolation from external disturbances, making the transmission of quantum signals together with classical ones difficult. A range of techniques has been tested to introduce quantum communications in already deployed optical networks which also carry legacy traffic. This comes with challenges, not only at the physical layer but also at the operations and management layer. To achieve a broad acceptance among network operators, the joint management and operation of quantum and classical resources, compliance with standards, and quality and legal assurance need to be addressed. This article presents a detailed account of solutions to the above issues, deployed and evaluated in the MadQCI (Madrid Quantum Communication Infrastructure) testbed. This network is designed to integrate quantum communications in the telecommunications ecosystem by installing quantum-key-distribution modules from multiple providers in production nodes of two different operators. The modules were connected through an optical-switched network with more than 130 km of deployed optical fibre. The tests were done in compliance with strict service level agreements that protected the legacy traffic of the pre-existing classical network. The goal was to achieve full quantum-classical compatibility at all levels, while limiting the modifications of optical transport and encryption and complying with as many standards as possible. This effort was intended to serve as a blueprint, which can be used as the foundation of large-scale quantum network deployments. To demonstrate the capabilities of MadQCI, end-to-end encryption services were deployed and a variety of use-cases were showcased., Comment: 16 pages

Star formation in the central molecular zone (CMZ) is suppressed with respect to that of the Galactic disk, and this is likely related to its high turbulent environment. This turbulence impedes the potential detection of prestellar cores. We present the temperature, density, and spatial structure of the CMZ molecular cloud G+0.693-0.027, which has been proposed to host a prestellar cluster in the Sgr B2 region. We analysed multiple HC$_{3}$N rotational transitions that were observed with the IRAM 30m, APEX, Yebes 40m, and GBT radio telescopes, together with SMA+APEX spatially resolved maps. The spectral shape of HC$_{3}$N lines shows three velocity components: a broad component with a line width of 23 km s$^{-1}$ (C1), and two narrow components with line widths of 7.2 and 8.8 km s$^{-1}$ (C2 and C3). This suggests that a fraction of the molecular gas in this cloud is undergoing turbulence dissipation. From a non-local thermodynamic equilibrium analysis we have found H$_{2}$ densities of 2$\times$10$^{4}$ cm$^{-3}$, 5$\times$10$^{4}$ cm$^{-3}$, and 4$\times$10$^{5}$ cm$^{-3}$ and kinetic temperatures of 140 K, 30 K, and 80 K for C1, C2, and C3, respectively. The spatially resolved maps confirm that the colder and high-density condensations C2 and C3, which peak in the 70-85 km s$^{-1}$ velocity range, are embedded in a more diffuse and warmer gas (C1). The larger-scale structure of the Sgr B2 region shows a hole at 40-50 km s$^{-1}$ that is likely due to a small cloud that shocked the Sgr B2 region and is spatially related with a massive cloud at 60-80 km s$^{-1}$. We propose that the impacting small cloud sequentially triggered the formation of Sgr B2(M), (N), and (S) and the condensations in G+0.693-0.027 during its passage. Based on the analysis of the masses of the two condensations and on the virial parameters, C2 might expand, while C3 might further fragment or collapse., Comment: 21 pages, 16 figures, 5 tables, 4 appendix - Accepted for publication in A&A

In this work we revise the concept of foliation and related aspects that are crucial when formulating the Hamiltonian evolution for various theories beyond General Relativity. In particular, we show the relation between the kinematic characteristics of timelike congruences (observers) and the existence of foliations orthogonal to them. We then explore how local Lorentz transformations acting on observers affect the existence of transversal foliations, provide examples and discuss about the implications of these results for the 3+1 formulation of tetrad modified theories of gravity., Comment: 17 pages

Anthropogenic climate change (ACC) is altering the frequency and intensity of extreme weather events. Attributing individual extreme events (EEs) to ACC is becoming crucial to assess the risks of climate change. Traditional attribution methods often suffer from a selection bias, are computationally demanding, and provide answers after the EE occurs. This study presents a ground-breaking hybrid attribution method by combining physics-based ACC estimates from global climate models with deep-learning weather forecasts. This hybrid approach circumvents the framing choices and accelerates the attribution process, paving the way for operational anticipated global forecast-based attribution. We apply this methodology to three distinct high-impact weather EEs. Despite some limitations in predictability, the method uncovers ACC fingerprints in the forecasted fields of EEs. Specifically, forecasts successfully anticipate that ACC exacerbated the 2018 Iberian heatwave, deepened hurricane Florence, and intensified the wind and precipitable water of the explosive cyclone Ciar\'an.

We present the framework for measuring angular power spectra in the Euclid mission. The observables in galaxy surveys, such as galaxy clustering and cosmic shear, are not continuous fields, but discrete sets of data, obtained only at the positions of galaxies. We show how to compute the angular power spectra of such discrete data sets, without treating observations as maps of an underlying continuous field that is overlaid with a noise component. This formalism allows us to compute exact theoretical expectations for our measured spectra, under a number of assumptions that we track explicitly. In particular, we obtain exact expressions for the additive biases ("shot noise") in angular galaxy clustering and cosmic shear. For efficient practical computations, we introduce a spin-weighted spherical convolution with a well-defined convolution theorem, which allows us to apply exact theoretical predictions to finite-resolution maps, including HEALPix. When validating our methodology, we find that our measurements are biased by less than 1% of their statistical uncertainty in simulations of Euclid's first data release., Comment: 27 pages, 12 figures. Submitted to A&A. Code available at https://github.com/heracles-ec/heracles

Given an open subset $U$ of a complex Banach space $E$, a weight $v$ on $U$, and a complex Banach space $F$, let $\mathcal{H}^\infty_v(U,F)$ denote the Banach space of all weighted holomorphic mappings $f\colon U\to F$, under the weighted supremum norm $\left\|f\right\|_v:=\sup\left\{v(x)\left\|f(x)\right\|\colon x\in U\right\}$. In this paper, we introduce and study the classes of weighted holomorphic mappings $\mathcal{H}^\infty_{v\mathcal{K}_{p}}(U,F)$ (resp., $\mathcal{H}^\infty_{v\mathcal{K}_{wp}}(U,F)$ and $\mathcal{H}^\infty_{v\mathcal{K}_{up}}(U,F)$) for which the set $(vf)(U)$ is relatively $p$-compact (resp., relatively weakly $p$-compact and relatively unconditionally $p$-compact). We prove that these mapping classes are characterized by $p$-compact (resp., weakly $p$-compact and unconditionally $p$-compact) linear operators defined on a Banach predual space of $\mathcal{H}^\infty_v(U)$ by linearization. We show that $\mathcal{H}^\infty_{v\mathcal{K}_{p}}$ (resp., $\mathcal{H}^\infty_{v\mathcal{K}_{wp}}$ and $\mathcal{H}^\infty_{v\mathcal{K}_{up}}$) is a Banach ideal of weighted holomorphic mappings which is generated by composition with the ideal of $p$-compact (resp., weakly $p$-compact and unconditionally $p$-compact) linear operators and contains the Banach ideal of all right $p$-nuclear weighted holomorphic mappings. We also prove that these weighted holomorphic mappings can be factorized through a quotient space of $l_{p^*}$, and $f\in\mathcal{H}^\infty_{v\mathcal{K}_{p}}(U,F)$ (resp., $f\in\mathcal{H}^\infty_{v\mathcal{K}_{up}}(U,F))$ if and only if its transposition $f^t$ is quasi $p$-nuclear (resp., quasi unconditionally $p$-nuclear)., Comment: 16 pages

Stabilizing underactuated systems is an inherently challenging control task due to fundamental limitations on how the control input affects the unactuated dynamics. Decomposing the system into actuated (output) and unactuated (zero) coordinates provides useful insight as to how input enters the system dynamics. In this work, we leverage the structure of this decomposition to formalize the idea of Zero Dynamics Policies (ZDPs) -- a mapping from the unactuated coordinates to desired actuated coordinates. Specifically, we show that a ZDP exists in a neighborhood of the origin, and prove that combining output stabilization with a ZDP results in stability of the full system state. We detail a constructive method of obtaining ZDPs in a neighborhood of the origin, and propose a learning-based approach which leverages optimal control to obtain ZDPs with much larger regions of attraction. We demonstrate that such a paradigm can be used to stabilize the canonical underactuated system of the cartpole, and showcase an improvement over the nominal performance of LQR., Comment: 8 pages, 2 figures, CDC 2024

We explain the large variety of star formation laws in terms of one single, simple law that can be inferred from the definition of the star formation rate and basic algebra. The resulting equation, $\SFR = \eff\ \Mcollapsing/\tauff$, although it has been presented elsewhere, is interpreted in terms of clouds undergoing collapse { rather than being turbulence-supported, an idea that different groups have pursued this century}. Under such assumption, one can explain the constancy of $\eff$, the different intra-cloud correlations observed in Milky Way's molecular clouds, as well as the resolved and unresolved extragalactic relationships between SFR and a measurement of the mass in CO, HCN, and CO+HI. We also explain why the slope of the correlation changes when the orbital time $\tauorb$ is considered instead of the free-fall time, and why estimations of the free-fall time from extragalactic observations skew the correlation, providing a false sublinear correlation. We furthermore show that the apparent nearly linear correlation between the star formation rate and the dynamical equilibrium pressure in the midplane of the galaxies, $\PDE$, is just a consequence of $\PDE$ values being dominated by the variation of the column density of molecular gas. All in all, we argue that the star formation law is driven by the collapse of cold, dense gas, which happens to be primarily molecular in the present Universe, and that the role of stellar feedback is just to shut down the star formation process, not to shape the star formation law., Comment: 17 pages, one figure. Accepted by MNRAS

In a conventional Federated Learning framework, client selection for training typically involves the random sampling of a subset of clients in each iteration. However, this random selection often leads to disparate performance among clients, raising concerns regarding fairness, particularly in applications where equitable outcomes are crucial, such as in medical or financial machine learning tasks. This disparity typically becomes more pronounced with the advent of performance-centric client sampling techniques. This paper introduces two novel methods, namely SUBTRUNC and UNIONFL, designed to address the limitations of random client selection. Both approaches utilize submodular function maximization to achieve more balanced models. By modifying the facility location problem, they aim to mitigate the fairness concerns associated with random selection. SUBTRUNC leverages client loss information to diversify solutions, while UNIONFL relies on historical client selection data to ensure a more equitable performance of the final model. Moreover, these algorithms are accompanied by robust theoretical guarantees regarding convergence under reasonable assumptions. The efficacy of these methods is demonstrated through extensive evaluations across heterogeneous scenarios, revealing significant improvements in fairness as measured by a client dissimilarity metric., Comment: 13 pages

In this correspondence, we analyze the performance of a reconfigurable intelligent surface (RIS)-aided communication system that involves a fluid antenna system (FAS)-enabled receiver. By applying the central limit theorem (CLT), we derive approximate expressions for the system outage probability when the RIS has a large number of elements. Also, we adopt the block-correlation channel model to simplify the outage probability expressions, reducing the computational complexity and shedding light on the impact of the number of ports. Numerical results validate the effectiveness of our analysis, especially in scenarios with a large number of RIS elements.

Modern processors implement a decoupled front-end in the form of Fetch Directed Instruction Prefetching (FDIP) to avoid front-end stalls. FDIP is driven by the Branch Prediction Unit (BPU), relying on the BPU's accuracy and branch target tracking structures to speculatively fetch instructions into the Instruction Cache (L1I). As data center applications become more complex, their code footprints also grow, resulting in an increase in Branch Target Buffer (BTB) misses. FDIP can alleviate L1I cache misses, but when it encounters a BTB miss, the BPU may not identify the current instruction as a branch to FDIP. This can prevent FDIP from prefetching or cause it to speculate down the wrong path, further polluting the L1I cache. We observe that the vast majority, 75%, of BTB-missing, unidentified branches are actually present in instruction cache lines that FDIP has previously fetched but, these missing branches have not yet been decoded and inserted into the BTB. This is because the instruction line is decoded from an entry point (which is the target of the previous taken branch) till an exit point (the taken branch). Branch instructions present in the ignored portion of the cache line we call them "Shadow Branches". Here we present Skeia, a novel shadow branch decoding technique that identifies and decodes unused bytes in cache lines fetched by FDIP, inserting them into a Shadow Branch Buffer (SBB). The SBB is accessed in parallel with the BTB, allowing FDIP to speculate despite a BTB miss. With a minimal storage state of 12.25KB, Skeia delivers a geomean speedup of ~5.7% over an 8K-entry BTB (78KB) and ~2% versus adding an equal amount of state to the BTB across 16 front-end bound applications. Since many branches stored in the SBB are unique compared to those in a similarly sized BTB, we consistently observe greater performance gains with Skeia across all examined sizes until saturation., Comment: 13 pages, 16 figures, Submitted to ASPLOS 2025

We investigate the behavior of the dense and cold (normalized) QCD trace anomaly, $\Delta$, in the interior of twin neutron stars (obtained from several sets of equations of state in agreement with modern compact-star and multimessenger data) satisfying static and dynamic stability conditions. We scan this parameter space in order to look for effects caused by the presence of a strong first-order phase transition connecting hadron and quark phases by means of a Maxwell construction. We found robustly that $\Delta$ suffers an abrupt decrease around the transition point, even reaching large negative values ($\Delta\simeq-0.35$), in marked contrast to current studies pointing out a smooth behavior with $\Delta\gtrsim 0$ at all densities. Besides, we characterize the behavior of conformal factor, $d_{c}$, in twin stars for which we perform comparisons with theoretical constraints, e.g. from Bayesian studies adjusted to agree with pQCD. All this allows us to infer and hypothesize modifications in the strong QCD coupling in dense nuclear matter with a strong thermodynamic discontinuity., Comment: 18 pages, 10 figures

Cold ($\sim$10 K) and dense ($\sim$10$^{5}$ cm$^{-3}$) cores of gas and dust within molecular clouds, known as starless and dynamically evolved prestellar cores, are the birthplaces of low-mass ($M$ $\leq$ few M$_\odot$) stars. As detections of interstellar complex organic molecules, or COMs, in starless cores has increased, abundance comparisons suggest that some COMs might be seeded early in the star formation process and inherited to later stages (i.e., protostellar disks and eventually comets). To date observations of COMs in starless cores have been limited, with most detections reported solely in the Taurus Molecular Cloud. It is therefore still a question whether different environments affect abundances. We have surveyed 35 starless and prestellar cores in the Perseus Molecular Cloud with the Arizona Radio Observatory (ARO) 12m telescope detecting both methanol, CH$_3$OH, and acetaldehyde, CH$_3$CHO, in 100% and 49% of the sample, respectively. In the sub-sample of 15 cores where CH$_3$CHO was detected at $>3\sigma$ ($\sim$18 mK) with the ARO 12m, follow-up observations with the Yebes 40m telescope were carried out. Detections of formic acid, $t$-HCOOH, ketene, H$_2$CCO, methyl cyanide, CH$_3$CN, vinyl cyanide, CH$_2$CHCN, methyl formate, HCOOCH$_3$, and dimethyl ether, CH$_3$OCH$_3$, are seen in at least $20\%$ of the cores. We discuss detection statistics, calculate column densities, and compare abundances across various stages of low-mass star formation. Our findings have more than doubled COM detection statistics in cold cores and show COMs are prevalent in the gas before star and planet formation in the Perseus Molecular Cloud., Comment: Accepted for publication in MNRAS. 25-page main text (w/ 18 figures) and 25-page appendix (w/ 7 figures) for a total of 50 pages

Astronomical impacts by small solar system bodies (meteoroids, asteroids, comets, and transitional objects) are considered a mechanism for delivering amino acids and their polymerization to proteins in early Earth conditions. High-pressure torsion (HPT) is a new methodology to simulate such impacts and clarify the behavior of biomolecules. In this study, two amino acids, crystalline L-serine and L-glutamic acid that were detected in meteorites, are processed by HPT and examined by ex situ X-ray diffraction, Raman spectroscopy, nuclear magnetic resonance, Fourier transform infrared spectroscopy, and in situ mechanical shear testing. No polymerization, chemical reactions, or phase transformations are detected after HPT, indicating that the stability and presence of these two amino acids in meteorites are quite reasonable. However, some microstructural and mechanical changes like crystal size reduction to the nanometer level, crystal defect formation, lattice expansion by vacancy formation, and shear strength enhancement to the steady state are found which are similar to the behaviors reported in metals and ceramics after HPT processing.

We present the most detailed spectrum of the intracluster light (ICL) in an individual cluster to date, the relaxed system RX J2129.7+0005, at $z\sim 0.234$. Using 15 broad-band, deep images observed with HST and JWST in the optical and the infrared, plus deep integral field spectroscopy from MUSE, we computed a total of 3696 ICL maps spanning the spectral range $\sim 0.4-5$ $\mu$m with our algorithm CICLE, a method that is extremely well suited to analyzing large samples of data in a fully automated way. We used both parametric and non-parametric approaches to fit the spectral energy distribution of the ICL and infer its physical properties, yielding a stellar mass $log_{10}(M_*/M_{\odot})$ between $11.5-12.7$ and an average age between $9.7-10.5$ Gyr, from CIGALE and Prospector results. This implies that the ICL in RX J2129.7+0005 is, on average, older than that of disturbed clusters, suggesting that the contribution from different stellar populations to the ICL are at play depending on the cluster's dynamical state. Coupled with X-ray observations of the hot gas distribution, we confirm the relaxed state of RX J2129.7+0005, showing clear signs of sloshing after a last major merger with a high-mass ratio satellite that could have happened $\sim 6.6$ Gyr ago in a relatively radial orbit. The presence of substructure in the ICL, such as shells, clouds with different densities and a certain degree of boxyness, and a clump, supports this scenario., Comment: 21 pages, 12 figures, 3 tables, accepted for publication in ApJ

We present an algorithm to solve the problem of locating the source, or maxima, of a scalar field using a robot swarm. We demonstrate how the robot swarm determines its direction of movement to approach the source using only field intensity measurements taken by each robot. In contrast with the current literature, our algorithm accommodates a generic (non-degenerate) geometry for the swarm's formation. Additionally, we rigorously show the effectiveness of the algorithm even when the dynamics of the robots are complex, such as a unicycle with constant speed. Not requiring a strict geometry for the swarm significantly enhances its resilience. For example, this allows the swarm to change its size and formation in the presence of obstacles or other real-world factors, including the loss or addition of individuals to the swarm on the fly. For clarity, the article begins by presenting the algorithm for robots with free dynamics. In the second part, we demonstrate the algorithm's effectiveness even considering non-holonomic dynamics for the robots, using the vector field guidance paradigm. Finally, we verify and validate our algorithm with various numerical simulations.

The behavior of velocity fluctuations near a wall has long fascinated the turbulence community, because the prevalent theoretical framework of an attached-eddy hierarchy appears to predict infinite intensities as the Reynolds number tends to infinity. Although an unbounded infinite limit is not a problem in itself, it raises the possibility of unfamiliar phenomena when the Reynolds number is large, and has motivated attempts to avoid it. We review the subject and point to possible pitfalls stemming from uncritical extrapolation from low Reynolds numbers, or from an over-simplification of the multiscale nature of turbulence. It is shown that large attached eddies dominate the high-Reynolds-number regime of the near-wall layer, and that they behave differently from smaller-scale ones. In that limit, the near-wall layer is controlled by the outer flow, the large-scale fluctuations reduce to turbulent layers modulated by a variable friction velocity, and the kinetic-energy peak is substituted by a deeper structure with a secondary outer maximum. The friction velocity is then not necessarily the best velocity scale. While the near-wall energy peak probably becomes unbounded in wall units, it tends to zero when expressed in terms of the outer velocity.

In any extension of General Relativity (GR), extra fundamental degrees of freedom couple to gravity. Besides deforming GR forecasts in a theory-dependent way, this coupling generically introduces extra modes in the gravitational-wave signal. We propose a novel theory-agnostic test of gravity to search for these nongravitational modes in black hole merger ringdown signals. To leading order in the GR deviations, their frequencies and damping times match those of a test scalar or vector field in a Kerr background, with only amplitudes and phases as free parameters. This test will be highly valuable for future detectors, which will achieve signal-to-noise ratios higher than 100 (and as high as 1000 for space-based detectors such as LISA). Such sensitivity will allow measurement of these modes with amplitude ratios as low as 0.05 for ground-based detectors (and as low as 0.008 for LISA), relative to the fundamental mode, enabling stringent agnostic constraints or detection of scalar/vector modes. By applying this test to GW150914, GW190521, and GW200129, we find that the current evidence for an extra mode is comparable to that for the first gravitational overtone, but its inclusion modifies the inferred remnant spin., Comment: 10 pages, 9 figures. v2: minor modifications, new explicit example, and new results on precessing binaries. Submitted version

Finding the eigenvalues connected to the covariance operator of a centred Hilbert-space valued Gaussian process is genuinely considered a hard problem in several mathematical disciplines. In statistics this problem arises for instance in the asymptotic null distribution of goodness-of-fit test statistics of weighted $L^2$-type. For this problem we present the Rayleigh-Ritz method to approximate the eigenvalues. The usefulness of these approximations is shown by high lightening implications such as critical value approximation and theoretical comparison of test statistics by means of Bahadur efficiencies., Comment: 18 pages, 16 tables

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

Ontology alignment is integral to achieving semantic interoperability as the number of available ontologies covering intersecting domains is increasing. This paper proposes OWL2Vec4OA, an extension of the ontology embedding system OWL2Vec*. While OWL2Vec* has emerged as a powerful technique for ontology embedding, it currently lacks a mechanism to tailor the embedding to the ontology alignment task. OWL2Vec4OA incorporates edge confidence values from seed mappings to guide the random walk strategy. We present the theoretical foundations, implementation details, and experimental evaluation of our proposed extension, demonstrating its potential effectiveness for ontology alignment tasks., Comment: Submitted to a conference

Modern OOO CPUs have very deep pipelines with large branch misprediction recovery penalties. Speculatively executed instructions on the wrong path can significantly change cache state, depending on speculation levels. Architects often employ trace-driven simulation models in the design exploration stage, which sacrifice precision for speed. Trace-driven simulators are orders of magnitude faster than execution-driven models, reducing the often hundreds of thousands of simulation hours needed to explore new micro-architectural ideas. Despite this strong benefit of trace-driven simulation, these often fail to adequately model the consequences of wrong path because obtaining them is nontrivial. Prior works consider either a positive or negative impact of wrong path but not both. Here, we examine wrong path execution in simulation results and design a set of infrastructure for enabling wrong-path execution in a trace driven simulator. Our analysis shows the wrong path affects structures on both the instruction and data sides extensively, resulting in performance variations ranging from $-3.05$\% to $20.9$\% when ignoring wrong path. To benefit the research community and enhance the accuracy of simulators, we opened our traces and tracing utility in the hopes that industry can provide wrong-path traces generated by their internal simulators, enabling academic simulation without exposing industry IP., Comment: 5 pages, 7 Figures, Submited to Computer Architecture Letters