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Understanding faint dwarf galaxies is fundamental to the development of a robust theory of galaxy formation on small scales. Since the discovery of a population of ultra diffuse galaxies (UDGs) rich in globular clusters (GCs) in Coma, an increasing number of studies on low surface brightness dwarf galaxies (LSBds) have been published in recent years. The most massive LSBds have been observed predominantly in groups and clusters, with properties displaying dependence on the environment. In this work, we use deep DECam imaging to systematically identify LSBds and their GC populations around the low-density environment of NGC 3115. We carefully analyse the structure and morphology of 24 candidates, 18 of which are reported for the first time. Most candidates exhibit red colours suggesting a connection between their colour and distance to NGC 3115. We followed up with Gemini GMOS imaging 9 LSBds to properly identify their GC populations. We derive lower limits for the number of GCs associated with each galaxy. Our analysis reveals that they occur around of the same loci of Fornax LSB dwarf GC systems. The relationship between the number of GCs and total mass provides a tool in which, by counting the GCs in these galaxies, we estimate an upper limit for the total mass of these LSB dwarfs, obtaining the mean value of $\sim 3.3\times10^{10}$ M$_{\odot}$. Our results align with expectations for dwarf-sized galaxies, particularly regarding the distribution and specific frequency of their GC systems., Comment: 25 pages, 22 figures, accepted for publication in MNRAS

Let X_o be a weighted-homogeneous complete intersection germ in (R^N,o) or (C^N,o), with arbitrary singularities, possibly non-reduced. Take the foliation of the ambient space by weighted-homogeneous real arcs, \ga_s. Take a deformation of X_o by higher order terms, X_t. Does the foliation \ga_s deform compatibly with X_t? We identify the ``obstruction locus", \Sigma in X_o, outside of which such a deformation does exist, and possesses exceptionally nice properties. Using this deformed foliation we construct a contact trivialization of the family of defining equations by a homeomorphism that is real analytic (resp. Nash) off the origin, differentiable at the origin, whose presentation in weighted-polar coordinates is globally real-analytic (resp. globally Nash), and with controlled Lipschitz/C^1-properties., Comment: arXiv admin note: text overlap with arXiv:2311.13423

We present a new family of regular black holes (RBH) in Pure Lovelock gravity, where the energy density is determined by the gravitational vacuum tension, which varies for each value of $n$ in each Lovelock case. Speculatively, our model may capture quantum effects through gravitational tension. In this way, a hypothetical analogy is drawn between the pair production ratio in the Schwinger effect and our energy density. A notable feature of our model is that the regular solution closely resembles the vacuum solution before reaching the event horizon. For odd $n$, the transverse geometry is spherical, with phase transitions occurring during evaporation, and the final state of this process is a remnant. For even $n$, the transverse geometry is non trivial and corresponds to a hyperboloid. In the case of $d=2n+1$ with even $n$, we find an RBH without a dS core and no inner horizon (whose presence has been recently debated in the literature due to the question of whether its presence is unstable or not), and no phase transitions. For $d > 2n + 1$ with even $n$, the RBH possesses both an event horizon and a cosmological horizon, also with no inner horizon present. The existence of the cosmological horizon arises without the usual requirement of a positive cosmological constant. From both numerical and analytical analysis, we deduce that as the event horizon expands and the cosmological horizon contracts, thermodynamic equilibrium is achieved in a remnant when the two horizons coincide.

Three-dimensional (3D) simulations of the structure of liquid crystal (LC) torons, topologically protected distortions of the LC director field, under material flows are rare but essential in microfluidic applications. Here, we show that torons adopt a steady-state configuration at low flow velocity before disintegrating at higher velocities, in line with experimental results. Furthermore, we show that under partial slip conditions at the boundaries, the flow induces a reversible elongation of the torons, also consistent with the experimental observations. These results are in contrast with previous simulation results for 2D skyrmions under similar flow conditions, highlighting the need for a 3D description of this LC soliton in relation to its coupling to the material flow. These findings pave the way for future studies of other topological solitons, like hopfions and heliknotons, in flowing soft matter systems.

A fundamental task in mobile robotics is to keep an agent under surveillance using an autonomous robotic platform equipped with a sensing device. Using differential game theory, we study a particular setup of the previous problem. A Differential Drive Robot (DDR) equipped with a bounded range sensor wants to keep surveillance of an Omnidirectional Agent (OA). The goal of the DDR is to maintain the OA inside its detection region for as much time as possible, while the OA, having the opposite goal, wants to leave the regions as soon as possible. We formulate the problem as a zero-sum differential game, and we compute the time-optimal motion strategies of the players to achieve their goals. We focus on the case where the OA is faster than the DDR. Given the OA's speed advantage, a winning strategy for the OA is always moving radially outwards to the DDR's position. However, this work shows that even though the previous strategy could be optimal in some cases, more complex motion strategies emerge based on the players' speed ratio. In particular, we exhibit that four classes of singular surfaces may appear in this game: Dispersal, Transition, Universal, and Focal surfaces. Each one of those surfaces implies a particular motion strategy for the players., Comment: 14 pages, 9 figures

Active galactic nuclei (AGN) are the signposts of black hole growth, and likely play an important role in galaxy evolution. An outstanding question is whether AGN of different spectral types indicate different evolutionary stages in the coevolution of black holes and galaxies. We present the angular correlation function between an AGN sample selected from the Hyper Suprime Camera Subaru Strategic Program (HSC-SSP) optical + Wide-field Infrared Survey Explorer (WISE) mid-IR photometry, and a luminous red galaxy (LRG) sample from HSC-SSP. We investigate AGN clustering strength as a function of their luminosity and spectral features across three independent HSC fields totaling $\sim600\,{\rm deg^{2}}$, for $z\in0.6-1.2$ and AGN with $L_{6\mu m}>3\times10^{44}{\rm\,erg\,s^{-1}}$. There are $\sim28,500$ AGN and $\sim1.5$ million LRGs in our primary analysis. We determine the inferred average halo mass for the full AGN sample ($M_h \approx 10^{12.9}h^{-1}M_\odot$), and note that it does not evolve significantly as a function of redshift (over this narrow range) or luminosity. We find that, on average, unobscured AGN ($M_h \approx10^{13.3}h^{-1}M_\odot$) occupy $\sim4.5\times$ more massive halos than obscured AGN ($M_h \approx10^{12.6}h^{-1}M_\odot$), at $5\sigma$ statistical significance using 1-D uncertainties, and at $3\sigma$ using the full covariance matrix, suggesting a physical difference between unobscured and obscured AGN, beyond the line-of-sight viewing angle. Furthermore, we find evidence for a halo mass dependence on reddening level within the Type I AGN population, which could support the existence of a previously claimed dust-obscured phase in AGN-host galaxy coevolution. However, we also find that even quite small systematic shifts in the redshift distributions of the AGN sample could plausibly explain current and previously observed differences in $M_{h}$., Comment: 31 pages, 14 figures, submitted to ApJ

The development of a novel technique based on a cylindrical microwave resonator for high pressure phase equilibrium determination is described. Electric permittivity or dielectric constant is a physical property that depends on temperature and pressure $\epsilon$($p$,$T$). Based on this property, a measuring technique consisting of a cylindrical resonant cavity that works in the microwave spectrum has been developed. Equilibrium data of fluid mixtures are measured at high pressure using a synthetic method, where phase transition is determined under isothermal conditions due to the change of the dielectric constant. This technique may be a more accurate alternative to conventional visual synthetic methods. The technique was validated measuring pure $CO_{2}$, and phase behaviour was then determined for two binary mixtures [$CO_{2}$ (0.6) + $CH_{4}$ (0.4)] and [$CO_{2}$ (0.4) + $CH_{4}$ (0.6)], results for which are presented. These systems are interesting for the study of biogas-like mixtures. In addition, data were compared with the equation of state used for natural gas GERG-2008, and also, they were modelled using Peng-Robinson equation of state and Wong-Sandler mixing rules, which are widely employed in chemical industries and which give good results.

This work presents a deep reinforcement learning-based approach to train controllers for the autonomous loading of ore piles with a Load-Haul-Dump (LHD) machine. These controllers must perform a complete loading maneuver, filling the LHD's bucket with material while avoiding wheel drift, dumping material, or getting stuck in the pile. The training process is conducted entirely in simulation, using a simple environment that leverages the Fundamental Equation of Earth-Moving Mechanics so as to achieve a low computational cost. Two different types of policies are trained: one with a hybrid action space and another with a continuous action space. The RL-based policies are evaluated both in simulation and in the real world using a scaled LHD and a scaled muck pile, and their performance is compared to that of a heuristics-based controller and human teleoperation. Additional real-world experiments are performed to assess the robustness of the RL-based policies to measurement errors in the characterization of the piles. Overall, the RL-based controllers show good performance in the real world, achieving fill factors between 71-94%, and less wheel drift than the other baselines during the loading maneuvers. A video showing the training environment and the learned behavior in simulation, as well as some of the performed experiments in the real world, can be found in https://youtu.be/jOpA1rkwhDY., Comment: 19 pages, 19 figures

We present results of our dynamical stream modelling for the North West Stream in the outer halo of the Andromeda galaxy (M31). Comprising two main segments, the North West Stream was thought to be a single structured arching around M31. However, recent evidence suggests that it is two separate, unrelated, streams. To test this hypothesis we use observational data from 6 fields associated with the upper segment of the North West Stream together with 8 fields and 5 globular clusters associated with the lower segment to constrain model orbits. We fit both segments of the stream using a fixed potential model for M31 and an orbit integrator to compare orbits with the observed streams. We measure the central tracks and predict proper motions for for the upper segment (lower segment) finding ${\mu^*_{\alpha}}$ = 0.078$^{+0.015}_{-0.012}$ (0.085$^{+0.001}_{-0.002}$) mas/yr and ${\mu_{\delta}}$ = $-$0.05$^{+0.008}_{-0.009}$ ($-$0.095$^{+0.003}_{-0.005}$) mas/yr. Our results support the hypothesis that the dwarf spheroidal galaxy Andromeda XXVII is the progenitor of the upper segment of the North West Stream and that the upper and lower segments do not comprise a single structure. We propose that the upper segment, which appears to be on an infall trajectory with M31, be renamed the "Andromeda XXVII Stream" and the lower segment, also apparently infalling towards M31, retain the name "North West Stream"., Comment: 20 pages, 14 figures

We present a kinematic and spectroscopic analysis of 40 red giant branch stars, in 9 fields, exquisitely delineating the lower segment of the North West Stream (NW-K2), which extends for $\sim$80 kpc from the centre of the Andromeda galaxy. We measure the stream's systemic velocity as -439.3$^{+4.1}_{-3.8}$ km/s with a velocity dispersion = 16.4$^{+5.6}_{-3.8}$ km/s that is in keeping with its progenitor being a dwarf galaxy. We find no detectable velocity gradient along the stream. We determine $-$1.3$\pm$0.1 $\le$ <[Fe/H]$_{\rm spec}$> $\le$ $-$1.2$\pm$0.8 but find no metallicity gradient along the stream. We are able to plausibly associate NW-K2 with the globular clusters PandAS-04, PandAS-09, PAndAS-10, PAndAS-11, PandAS-12 but not with PandAS-13 or PandAS-15 which we find to be superimposed on the stream but not kinematically associated with it., Comment: 14 pages, 8 figures

Terahertz pulses generated by two-color laser plasmas have reported peak field strengths exceeding MV/cm, and when illuminating metal nanotips the near-field enhancement at the tip apex should result in extremely high bunch charges and electron energies via sub-cycle cold field emission. Here, electron emission from tungsten nanotips driven by THz pulses generated by a long filament air-plasma are reported. Electron energies up to 1.1 keV and bunch charges up to 2x$10^5$ electrons per pulse were detected, well below values expected for peak field calculated via the time averaged Poynting vector. Investigations revealed a failure in the use of the time-averaged Poynting vector when applied to long filament THz pulses, due to spatio-temporal restructuring of the THz pulse in the focus. Accounting for this restructuring significantly reduces the field strength to approximately 160 ~kV/cm, consistent with the observed electron bunch charges, peak energies and their dependence on the tip position in the THz focus. Despite these findings, our results surpass previous THz plasma-driven electron generation by an order of magnitude in both electron energy and bunch charge and a path to increasing these by an additional order of magnitude by modification of the THz optics is proposed.

We present a freely available spontaneous speech corpus for the Brazilian Portuguese language and report preliminary automatic speech recognition (ASR) results, using both the Wav2Vec2-XLSR-53 and Distil-Whisper models fine-tuned and trained on our corpus. The NURC-SP Audio Corpus comprises 401 different speakers (204 females, 197 males) with a total of 239.30 hours of transcribed audio recordings. To the best of our knowledge, this is the first large Paulistano accented spontaneous speech corpus dedicated to the ASR task in Portuguese. We first present the design and development procedures of the NURC-SP Audio Corpus, and then describe four ASR experiments in detail. The experiments demonstrated promising results for the applicability of the corpus for ASR. Specifically, we fine-tuned two versions of Wav2Vec2-XLSR-53 model, trained a Distil-Whisper model using our dataset with labels determined by Whisper Large-V3 model, and fine-tuned this Distil-Whisper model with our corpus. Our best results were the Distil-Whisper fine-tuned over NURC-SP Audio Corpus with a WER of 24.22% followed by a fine-tuned versions of Wav2Vec2-XLSR-53 model with a WER of 33.73%, that is almost 10% point worse than Distil-Whisper's. To enable experiment reproducibility, we share the NURC-SP Audio Corpus dataset, pre-trained models, and training recipes in Hugging-Face and Github repositories.

In this paper, we obtain new measurements of the angular homogeneity scale ($\theta_H$) from the BOSS DR12 and eBOSS DR16 catalogs of Luminous Red Galaxies of the Sloan Digital Sky Survey. Considering the flat $\Lambda$CDM model, we use the $\theta_H(z)$ data to constrain the matter density parameter ($\Omega_{m0}$) and the Hubble constant ($H_{0}$). We find $H_0 = 65.7 \pm 7.0$ km s$^{-1}$ Mpc$^{-1}$ and $\Omega_{m0}>0.293$. By combining the $\theta_H$ measurements with current Baryon Acoustic Oscillations (BAO) and Type Ia Supernova (SN) data, we obtain $H_{0}= 69.9^{+4.9}_{-4.5}$ km s$^{-1}$ Mpc$^{-1}$ and $\Omega_{m0} = 0.294^{+0.013}_{-0.015}$ ($\theta_H$ + BAO) and $H_{0}=70.7^{+5.2}_{-4.1}$ km s$^{-1}$ Mpc$^{-1}$ and $\Omega_{m0}=0.299 \pm 0.022$ ($\theta_H$ + SN). We show that $\theta_H$ measurements help break the BAO and SN degeneracies concerning $H_0$, as they do not depend on the sound horizon scale at the drag epoch or the SN absolute magnitude value obtained from the distance ladder method. Hence, despite those constraints being loose compared to other probes, $\theta_H$ data may provide an independent cosmological probe of $H_0$ in light of the Hubble tension. For completeness, we also forecast the constraining power of future $\theta_H$ data via Monte Carlo simulations. Considering a relative error of the order of 1$\%$, we obtain competitive constraints on $\Omega_{m0}$ and $H_0$ ($\approx 5\%$ error) from the joint analysis with current SN and BAO measurements., Comment: 10 pages, 10 figures

The Epoch of Reionization Spectrometer (EoR-Spec) is an upcoming Line Intensity Mapping (LIM) instrument designed to study the evolution of the early universe (z = 3.5 to 8) by probing the redshifted [CII] 158 $\mu$m fine-structure line from aggregates of galaxies. The [CII] emission is an excellent tracer of star formation since it is the dominant cooling line from neutral gas heated by OB star light and thus can be used to probe the reionization of the early Universe due to star formation. EoR-Spec will be deployed on Prime-Cam, a modular direct-detection receiver for the 6-meter Fred Young Submillimeter Telescope (FYST), currently under construction by CPI Vertex Antennentechnik GmbH and to be installed near the summit of Cerro Chajnantor in the Atacama Desert. This instrument features an image plane populated with more than 6500 Microwave Kinetic Inductance Detectors (MKIDs) that are illuminated by a 4-lens optical design with a cryogenic, scanning Fabry-Perot Interferometer (FPI) at the pupil of the optical system. The FPI is designed to provide a spectral resolving power of $R\sim100$ over the full spectral range of 210--420 GHz. EoR-Spec will tomographically survey the E-COSMOS and E-CDFS fields with a depth of about 4000 hours over a 5 year period. Here we give an update on EoR-Spec's final mechanical/optical design and the current status of fabrication, characterization and testing towards first light in 2026., Comment: Presented at SPIE Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy XII

3C 264 is one of the few FRI radio galaxies with detected TeV emission. It is a low-luminosity AGN (LLAGN) and is generally associated with a radiatively inefficient accretion flow (RIAF). Earlier multiwavelength studies suggest that the X-ray emission originates from a jet. However, the possibility that the RIAF can significantly contribute to the X-rays cannot be ruled out. In particular, hard X-ray emission $\gtrsim$10 keV has never been detected, making it challenging to distinguish between X-ray models. Here we report a NuSTAR detection up to 25 keV from 3C 264. We also present subpixel deconvolved Chandra images to resolve jet emission down to ~0.2 arcsec from the center of the unresolved X-ray core. Together with a simultaneous Swift observation, we have constrained the dominant hard X-ray emission to be from its unresolved X-ray core, presumably in its quiescent state. We found evidence of a cutoff in the energy around 20 keV, indicating that at least some of the X-rays from the core can be attributed to the RIAF. The Comptonization model suggests an electron temperature of about 15 keV and an optical depth ranging between 4 and 7, following the universality of coronal properties of black hole accretion. The cutoff energy or electron temperature of 3C 264 is the lowest among those of other LLAGNs. The detected hard X-ray emission is at least an order of magnitude higher than that predicted by synchrotron self-Compton models introduced to explain $\gamma$-ray and TeV emission, suggesting that the synchrotron electrons might be accelerated to higher energies than previously thought., Comment: 19 pages, 6 figures, 2 tables, accepted for publication in ApJ, figure 3 is compressed to reduce file size

We measure the speed of light with current observations, such as Type Ia Supernova, galaxy ages, radial BAO mode, as well as simulations of forthcoming redshift surveys and gravitational waves as standard sirens. By means of a Gaussian Process reconstruction, we find that the precision of such measurements can be improved from roughly 6\% and to about $2-2.5\%$ when the gravitational wave simulations are considered, and to $1.5-2\%$ when redshift survey are included in the analysis as well. This result demonstrates that we will be able to perform a cosmological measurement of a fundamental physical constant with significantly improved precision, which will help us underpinning if its value is truly consistent with local measurements, as predicted by the standard model of Cosmology., Comment: 12 pages, 1 appendix, 9 figures. Accepted for publication in JCAP

We show that continuous epimorphisms between a class of subgroups of mapping class groups of orientable infinite-genus 2-manifolds with no planar ends are always induced by homeomorphisms. This class of subgroups includes the pure mapping class group, the closure of the compactly supported mapping classes, and the full mapping class group in the case that the underlying manifold has a finite number of ends or is perfectly self-similar. As a corollary, these groups are Hopfian topological groups., Comment: 26 pages, 3 figures

Planning methods struggle with computational intractability in solving task-level problems in large-scale environments. This work explores leveraging the commonsense knowledge encoded in LLMs to empower planning techniques to deal with these complex scenarios. We achieve this by efficiently using LLMs to prune irrelevant components from the planning problem's state space, substantially simplifying its complexity. We demonstrate the efficacy of this system through extensive experiments within a household simulation environment, alongside real-world validation using a 7-DoF manipulator (video https://youtu.be/6ro2UOtOQS4)., Comment: 8 pages, 6 figures

This work presents phase envelope and speed of sound data for the (0.60 $CO_{2}$ + 0.40 $C_{3}H_{8}$) and (0.80 $CO_{2}$ + 0.20 $C_{3}H_{8}$) binary mixtures. Phase equilibria was measured using a cylindrical resonator working in the microwave band whereas an acoustic resonator was used for speed of sound measurements. The experimental results were compared with GERG-2008 equation of state, obtaining average absolute deviations by 0.24% in pressure for phase equilibria data and 0.025% for speed of sound data. Speed of sound values were used to derive perfect-gas heat capacities, acoustic virial coefficients, and second density virial coefficients. In addition, AGA8-DC92 equation of state performance was checked for the results derived from speeds of sound.

In this paper, we explored an extension of the classical non-standard cosmological scenario in which the new field, $\phi$, which interacts with the radiation component in the early universe, experiences dissipative processes in the form of a bulk viscosity. Assuming an interaction term given by $\Gamma_{\phi}\rho_{\phi}$, where $\Gamma_{\phi}$ accounts for the decay rate of the field and $\rho_{\phi}$ corresponds to its energy density, and a bulk viscosity according to the expression $\xi=\xi_{0}\rho_{\phi}^{1/2}$ in the framework of Eckart's theory, we apply this novel non-standard cosmology to study the parameters space for WIMPs Dark Matter candidate production. This parameter space shows deviations from the classical non-standard cosmological scenario, obtaining new regions to search for this candidate. In particular, for certain combinations of the free parameters, we found large regions in which the model can establish the DM and reproduce the current observable relic density., Comment: 15 pages and 19 figures. We added new references and comments in this new version

Context: This study explores how software professionals identify and address biases in AI systems within the software industry, focusing on practical knowledge and real-world applications. Goal: We aimed to understand the strategies employed by practitioners to manage bias and their implications for fairness debt. Method: We used a qualitative research method, gathering insights from industry professionals through interviews and employing thematic analysis to explore the collected data. Findings: Professionals identify biases through discrepancies in model outputs, demographic inconsistencies, and issues with training data. They address these biases using strategies such as enhanced data management, model adjustments, crisis management, improving team diversity, and ethical analysis. Conclusion: Our paper presents initial evidence on addressing fairness debt and provides a foundation for developing structured guidelines to manage fairness-related issues in AI systems.

Ethereum is a distributed, peer-to-peer blockchain infrastructure that has attracted billions of dollars. Perhaps due to its success, Ethereum has become a target for various kinds of attacks, motivating researchers to explore different techniques to identify vulnerabilities in EVM bytecode (the language of the Ethereum Virtual Machine), including formal verification, symbolic execution, and fuzz testing. Although recent studies empirically compare smart contract fuzzers, there is a lack of literature investigating how simpler greybox fuzzers compare to more advanced ones. To fill this gap, in this paper, we present DogeFuzz, an extensible infrastructure for fuzzing Ethereum smart contracts, currently supporting black-box fuzzing and two grey-box fuzzing strategies: coverage-guided grey-box fuzzing (DogeFuzz-G) and directed grey-box fuzzing (DogeFuzz-DG). We conduct a series of experiments using benchmarks already available in the literature and compare the DogeFuzz strategies with state-of-the-art fuzzers for smart contracts. Surprisingly, although DogeFuzz does not leverage advanced techniques for improving input generation (such as symbolic execution or machine learning), DogeFuzz outperforms sFuzz and ILF, two state-of-the-art fuzzers. Nonetheless, the Smartian fuzzer shows higher code coverage and bug-finding capabilities than DogeFuzz., Comment: 16 pages,published in SBSEG 2024 conference

Prime-Cam, a first-generation science instrument for the Atacama-based Fred Young Submillimeter Telescope, is being built by the CCAT Collaboration to observe at millimeter and submillimeter wavelengths using kinetic inductance detectors (KIDs). Prime-Cam's 280 GHz instrument module will deploy with two aluminum-based KID arrays and one titanium nitride-based KID array, totaling approximately 10,000 detectors at the focal plane, all of which have been fabricated and are currently undergoing testing. One complication of fielding large arrays of KIDs under dynamic loading conditions is tuning the detector tone powers to maximize signal-to-noise while avoiding bifurcation due to the nonlinear kinetic inductance. For aluminum-based KIDs, this is further complicated by additional nonlinear effects which couple tone power to resonator quality factors and resonant frequencies. While both nonequilibrium quasiparticle dynamics and two-level system fluctuations have been shown to give rise to qualitatively similar distortions, modeling these effects alongside nonlinear kinetic inductance is inefficient when fitting thousands of resonators on-sky with existing models. For this reason, it is necessary to have a detailed understanding of the nonlinear effects across relevant detector loading conditions, including how they impact on on-sky noise and how to diagnose the detector's relative performance. We present a study of the competing nonlinearities seen in Prime-Cam's 280 GHz aluminum KIDs, with a particular emphasis on the resulting distortions to the resonator line shape and how these impact detector parameter estimation., Comment: 10 pages, 4 figures, Conference proceedings from SPIE Astronomical Telescopes + Instrumentation (AS24)

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

This paper introduces the MERIT Dataset, a multimodal (text + image + layout) fully labeled dataset within the context of school reports. Comprising over 400 labels and 33k samples, the MERIT Dataset is a valuable resource for training models in demanding Visually-rich Document Understanding (VrDU) tasks. By its nature (student grade reports), the MERIT Dataset can potentially include biases in a controlled way, making it a valuable tool to benchmark biases induced in Language Models (LLMs). The paper outlines the dataset's generation pipeline and highlights its main features in the textual, visual, layout, and bias domains. To demonstrate the dataset's utility, we present a benchmark with token classification models, showing that the dataset poses a significant challenge even for SOTA models and that these would greatly benefit from including samples from the MERIT Dataset in their pretraining phase.

There are holographic superconformal theories in all dimensions between two and six which allow arbitrary tree-level four-point functions to be fixed by basic consistency conditions. Although Mellin space is usually the most efficient setting for imposing these contraints, four-point functions in two dimensions have thus far been an exception due to their more intricate dependence on the conformal cross-ratios. In this paper, we introduce a simple fix which exploits the relation between a parity-odd conformal block in two dimensions and a parity-even conformal block in four dimensions. We then apply the resulting toolkit to a study of the paradigmatic holographic theory in two dimensions which is the D1-D5 CFT. For correlators involving Kaluza-Klein modes of the tensor multiplet, this analysis reproduces results which were previously obtained using hidden conformal symmetry. With four Kaluza-Klein modes of the graviton multiplet, it yields new results including a compact formula for the correlators of all pairwise identical operators, Comment: 30 + 19 pages, 3 figures. Fixed typos and improved explanations

The attractive Hubbard model has become a model readily realizable with ultracold atoms on optical lattices. However, the superconducting (superfluid) critical temperatures, $T_c$'s, are still somewhat smaller than the lowest temperatures achieved in experiments. Here we consider two possible routes, generically called layering, to increase $T_c$: a bilayer and a simple cubic lattice, both with tunable hopping, $t_z$, between attractive Hubbard planes. We have performed minus-sign--free determinant quantum Monte Carlo simulations to calculate response functions such as pairing correlation functions, uniform spin susceptibility, and double occupancy, through which we map out some physical properties. We have found that by a judicious choice of fillings and intensity of on-site attraction, a bilayer can exhibit $T_c$'s between 1.5 and 1.7 times those of the single layer; for the simple-cubic lattice the enhancement can be 30\% larger than the maximum for the single layer. We also check the accuracy of both a BCS-like estimate for $T_c$ in the attractive Hubbard model, as well as of an upper bound for $T_c$ based on the superfluid density., Comment: 12 pages, 17 figures

This paper presents an examination of State Space Models (SSM) and Koopman-based deep learning methods for modelling the dynamics of both linear and non-linear stiff strings. Through experiments with datasets generated under different initial conditions and sample rates, we assess the capacity of these models to accurately model the complex behaviours observed in string dynamics. Our findings indicate that our proposed Koopman-based model performs as well as or better than other existing approaches in non-linear cases for long-sequence modelling. We inform the design of these architectures with the structure of the problems at hand. Although challenges remain in extending model predictions beyond the training horizon (i.e., extrapolation), the focus of our investigation lies in the models' ability to generalise across different initial conditions within the training time interval. This research contributes insights into the physical modelling of dynamical systems (in particular those addressing musical acoustics) by offering a comparative overview of these and previous methods and introducing innovative strategies for model improvement. Our results highlight the efficacy of these models in simulating non-linear dynamics and emphasise their wide-ranging applicability in accurately modelling dynamical systems over extended sequences., Comment: Accepted to DAFx2024

Competition between magnetism and superconductivity can lead to unconventional and topological superconductivity. However, the experimental confirmation of the presence of Majorana edge states and unconventional pairing currently poses a major challenge. Here we consider a two-dimensional lattice model for a superconductor with spin-orbit coupling and exchange coupling to randomly distributed magnetic impurities. Depending on parameters of the model, this system may display topologically trivial or nontrivial edge states. We map out the phase diagram by computing the Bott index, a topological invariant defined in real space. We then use machine learning (ML) algorithms to predict the Bott index from the local density of states (LDOS) at zero energy, obtaining high-accuracy results. We also train ML models to predict the amplitude of odd-frequency pairing in the anomalous Green's function at zero energy. Once the ML models are trained using the LDOS, which is experimentally accessible via scanning tunneling spectroscopy, our method could be applied to predict the number of Majorana edge states and to estimate the magnitude of odd-frequency pairing in real materials., Comment: 10 pages, 8 figures

Document summarization is a task to shorten texts into concise and informative summaries. This paper introduces a novel dataset designed for summarizing multiple scientific articles into a section of a survey. Our contributions are: (1) SurveySum, a new dataset addressing the gap in domain-specific summarization tools; (2) two specific pipelines to summarize scientific articles into a section of a survey; and (3) the evaluation of these pipelines using multiple metrics to compare their performance. Our results highlight the importance of high-quality retrieval stages and the impact of different configurations on the quality of generated summaries., Comment: 15 pages, 6 figures, 1 table. Submitted to BRACIS 2024

The infinite spin problem is a problem concerning the rotational behavior of total collision orbits in the $n$-body problem. The question also makes sense for partial collision. When a~cluster of bodies tends to a (partial) collision, then its normalized shape curve tends to the set of normalized central configurations, which in the planar case has $SO(2)$ symmetry. This leaves a possibility that the normalized shape curve tends to the circle obtained by rotation of some central configuration instead of a particular point on it. This is the \emph{infinite spin problem}. We show that it is not possible if the limiting circle is isolated from other connected components of the set of normalized central configuration. Our approach extends the method from recent work for total collision by Moeckel and Montgomery, which was based on a combination of the center manifold theorem with {\L}ojasiewicz inequality. To that we add a shadowing result for pseudo-orbits near normally hyperbolic manifold and careful estimates on the influence of other bodies on the cluster of colliding bodies.

We investigate nucleosynthesis in the sub-relativistic outflows from black hole (BH) accretion disks formed in failed supernovae from rapidly-rotating Wolf-Rayet stars. These disks reach the neutrino-cooled regime during a portion of their evolution, undergoing significant neutronization and thus having the potential to support the $r$-process. Here, we analyze the formation of heavy elements in the ejecta from global, axisymmetric, long-term, viscous hydrodynamic simulations of these systems that include neutrino emission and absorption, Newtonian self-gravity, a pseudo-Newtonian potential for the BH gravity, and a 19-isotope nuclear network. Tracer particles are used for post-processing with a larger network. In addition to analyzing models from a previous paper, we present new models in which we modify the rotation profile of the progenitor star, to maximize neutrino reprocessing of circularized mass shells. All of our models produce several $M_\odot$ of O, followed by about a solar mass of C, Ne, and Ni, with other alpha elements produced in smaller quantities. Only one of our models, with the lowest viscosity, yields significant amounts of first $r$-process peak elements, with negligible yields at higher nuclear masses. The rest of the set, including models with a modified rotation profile, produces very small or negligible quantities of elements beyond the iron group. Models that produce the heaviest elements (up to $A\sim200$) do so along the proton-rich side of the valley of stability at high entropy ($s/k_B\sim80$), pointing to the $rp$-process as a mechanism that operates in collapsars. The absence of neutron-rich ejecta proves to be insensitive to changes in the rotation profile of the star, suggesting that heavy $r$-process elements are difficult to produce in collapsars if no large-scale poloidal magnetic field is present in the disk to drive outflows during neutronization., Comment: Accepted by PRD, Part 2 of arXiv:2403.08877. Minor changes in the arXiv abstract to fit character limit

Quantum machine learning QML algorithms promise to deliver near-term, applicable quantum computation on noisy, intermediate-scale systems. While most of these algorithms leverage quantum circuits for generic applications, a recent set of proposals, called analog quantum machine learning (AQML) algorithms, breaks away from circuit-based abstractions and favors leveraging the natural dynamics of quantum systems for computation, promising to be noise-resilient and suited for specific applications such as quantum simulation. Recent AQML studies have called for determining best ansatz selection practices and whether AQML algorithms have trap-free landscapes based on theory from quantum optimal control (QOC). We address this call by systematically studying AQML landscapes on two models: those admitting black-boxed expressivity and those tailored to simulating a specific unitary evolution. Numerically, the first kind exhibits local traps in their landscapes, while the second kind is trap-free. However, both kinds violate QOC theory's key assumptions for guaranteeing trap-free landscapes. We propose a methodology to co-design AQML algorithms for unitary evolution simulation using the ansatz's Magnus expansion. We show favorable convergence in simulating dynamics with applications to metrology and quantum chemistry. We conclude that such co-design is necessary to ensure the applicability of AQML algorithms., Comment: 10 pages, 6 figures

The Cosmic Distance Duality Relation (CDDR) defines a fundamental link in modern cosmology between geometric distance measurements and redshift. Any observed deviations from the CDDR could suggest the need for new physics beyond the $\Lambda$CDM model. In this study, we model geometric distances (both luminosity and angular) using a cosmographic expansion with the Pade method, which stabilizes the behavior of the cosmographic series at high redshifts. This approach allows us to determine geometric distances independently of any specific model. By incorporating updated data from supernovae (SN), baryon acoustic oscillations (BAO), and cosmic chronometers (CC), we obtain observational constraints for parametric models that quantify the CDDR, alongside all baseline cosmographic parameters. We find that potential CDDR violations introduce new statistical correlations in the cosmographic parameters ($H_0$, $q_0$, and $j_0$). However, within this framework, we do not observe significant deviations from the CDDR, and our results remain consistent with the predictions of the $\Lambda$CDM model. This work offers a novel and straightforward method for testing the CDDR by fixing background evolution through cosmographic techniques, thereby paving the way for new geometric observational tests of possible deviations from standard cosmology., Comment: 9 pages, 5 figures

Active galactic nuclei (AGN) are among the main candidates for ultra-high-energy cosmic ray (UHECR) sources. However, while theoretical and some phenomenological works favor AGNs as the main sources, recent works have shown that using the very-high-energy (VHE) $\gamma$-ray flux as a proxy for the UHECR flux leads to a bad agreement with data. In this context, the energy spectrum and composition data are hardly fitted. At the same time, the arrival directions map is badly described and a spurious dipole direction is produced. In this work, we propose a possible solution to these contradictions. Using the observed $\gamma$-ray flux as a proxy may carry the implicit assumption of beamed UHECR emission and, consequently, its beam will remain collimated up to its detection on Earth. We show that assuming an isotropic UHECR emission and correcting the $\gamma$-ray emission proxy by Doppler boosting can overcome the problem. The combined fit of the spectrum and composition is improved by $3.56\sigma$, while the predicted arrival directions agree much better with the data. In particular, a spurious direction of the dipole can be reduced from $10.3 \ (5.4)\sigma$ away from the data to $2.2 \ (1.5)\sigma$ for $E > 8$ EeV ($E > 32$ EeV). We also show that this effect is particularly important when including AGNs of different classes in the same analysis, such as radio galaxies and blazars.

Deformable image registration is a standard engineering problem used to determine the distortion experienced by a body by comparing two images of it in different states. This study introduces two new DIR methods designed to capture non-affine deformations using Radon transform-based similarity measures and a classical regularizer based on linear elastic deformation energy. It establishes conditions for the existence and uniqueness of solutions for both methods and presents synthetic experimental results comparing them with a standard method based on the sum of squared differences similarity measure. These methods have been tested to capture various non-affine deformations in images, both with and without noise, and their convergence rates have been analyzed. Furthermore, the effectiveness of these methods was also evaluated in a lung image registration scenario.

In this paper we give a characterization of $\log J_f$ belongs to $\widetilde{\mathcal{B}}_p$ or $\widetilde{\mathcal{Q}}_p$ spaces for any locally univalent sense-preserving harmonic mappings $f$ defined in the unit disk, using the Schwarzian derivative of $f$ and Carleson meseaure. In addition, we introduce the classes $\mathcal{BT}_p$ and $\mathcal{QT}_p$, based on the Jacobian operator, and begin a study of these.

Periodically driven systems offer a perfect breeding ground for out-of-equilibrium engineering of topological boundary states at zero energy ($0$-mode), as well as finite energy ($\pi$-mode), with the latter having no static analog. The Floquet operator and the effective Floquet Hamiltonian, which encapsulate the stroboscopic features of the driven system, capture both spectral and localization properties of the $0$- and $\pi$-modes but sometimes fail to provide complete topological characterization, especially when $0$- and $\pi$-modes coexist. In this work, we utilize the spectral localizer, a powerful local probe that can provide numerically efficient, spatially local, and energy-resolved topological characterization. In particular, we apply the spectral localizer to the effective Floquet Hamiltonian for driven one- and two-dimensional topological systems with no or limited symmetries and are able to assign topological invariants, or local markers, that characterize the $0$- and the $\pi$-boundary modes individually and unambiguously. Due to the spatial resolution, we also demonstrate that the extracted topological invariants are suitable for studying driven disordered systems and can even capture disorder-induced phase transitions., Comment: 11 pages, 6 figures; Comments are welcome

We present a method to prepare non-classical states of the electromagnetic field in a microwave resonator. It is based on a controlled gate that applies a squeezing operation on a SQUID-terminated resonator conditioned on the state of a dispersively coupled qubit. This controlled-squeeze gate, when combined with Gaussian operations on the resonator, is universal. We explore the use of this tool to map an arbitrary qubit state into a superposition of squeezed states. In particular, we target a bosonic code with well-defined superparity which makes photon losses detectable by nondemolition parity measurements. We analyze the possibility of implementing this using state-of-the-art circuit QED tools and conclude that it is within reach of current technologies., Comment: 5 pages, 2 figures and supplemental material

The intersection of computer vision and machine learning has emerged as a promising avenue for advancing historical research, facilitating a more profound exploration of our past. However, the application of machine learning approaches in historical palaeography is often met with criticism due to their perceived ``black box'' nature. In response to this challenge, we introduce NeuroPapyri, an innovative deep learning-based model specifically designed for the analysis of images containing ancient Greek papyri. To address concerns related to transparency and interpretability, the model incorporates an attention mechanism. This attention mechanism not only enhances the model's performance but also provides a visual representation of the image regions that significantly contribute to the decision-making process. Specifically calibrated for processing images of papyrus documents with lines of handwritten text, the model utilizes individual attention maps to inform the presence or absence of specific characters in the input image. This paper presents the NeuroPapyri model, including its architecture and training methodology. Results from the evaluation demonstrate NeuroPapyri's efficacy in document retrieval, showcasing its potential to advance the analysis of historical manuscripts.

The study of Greek papyri from ancient Egypt is fundamental for understanding Graeco-Roman Antiquity, offering insights into various aspects of ancient culture and textual production. Palaeography, traditionally used for dating these manuscripts, relies on identifying chronologically relevant features in handwriting styles yet lacks a unified methodology, resulting in subjective interpretations and inconsistencies among experts. Recent advances in digital palaeography, which leverage artificial intelligence (AI) algorithms, have introduced new avenues for dating ancient documents. This paper presents a comparative analysis between an AI-based computational dating model and human expert palaeographers, using a novel dataset named Hell-Date comprising securely fine-grained dated Greek papyri from the Hellenistic period. The methodology involves training a convolutional neural network on visual inputs from Hell-Date to predict precise dates of papyri. In addition, experts provide palaeographic dating for comparison. To compare, we developed a new framework for error analysis that reflects the inherent imprecision of the palaeographic dating method. The results indicate that the computational model achieves performance comparable to that of human experts. These elements will help assess on a more solid basis future developments of computational algorithms to date Greek papyri.

This work is concerned with the realization and stability of a non-Abelian chiral quantum spin liquid phase. To do so, we cast the problem in a quantum wires framework, which is a dimensional deconstruction framework that allows us to study the (2+1) dimensional spin liquids phase from a series of coupled (1+1) dimensional theories. The lower dimension grants us the ability to perform a bosonization procedure, which yields two different partition functions connected by a strong-weak duality transformation. This bosonization procedure is illuminating in that it makes the fixed point structure of the model unequivocal. Then, we proceed by studying the RG flow through the $ \beta $-functions, which we use to determine the phase structure. We find that the quantum spin liquid phase is realized and stable in the deep IR limit., Comment: 27 pages, 5 figures

Surrogate models that combine dimensionality reduction and regression techniques are essential to reduce the need for costly high-fidelity computational fluid dynamics data. New approaches using $\beta$-Variational Autoencoder ($\beta$-VAE) architectures have shown promise in obtaining high-quality low-dimensional representations of high-dimensional flow data while enabling physical interpretation of their latent spaces. We propose a surrogate model based on latent space regression to predict pressure distributions on a transonic wing given the flight conditions: Mach number and angle of attack. The $\beta$-VAE model, enhanced with Principal Component Analysis (PCA), maps high-dimensional data to a low-dimensional latent space, showing a direct correlation with flight conditions. Regularization through $\beta$ requires careful tuning to improve overall performance, while PCA preprocessing helps to construct an effective latent space, improving autoencoder training and performance. Gaussian Process Regression is used to predict latent space variables from flight conditions, showing robust behavior independent of $\beta$, and the decoder reconstructs the high-dimensional pressure field data. This pipeline provides insight into unexplored flight conditions. Furthermore, a fine-tuning process of the decoder further refines the model, reducing the dependence on $\beta$ and enhancing accuracy. Structured latent space, robust regression performance, and significant improvements in fine-tuning collectively create a highly accurate and efficient surrogate model. Our methodology demonstrates the effectiveness of $\beta$-VAEs for aerodynamic surrogate modeling, offering a rapid, cost-effective, and reliable alternative for aerodynamic data prediction., Comment: 19 pages, 12 figures

The predictions of hadronic interaction models for cosmic-ray induced air showers contain inherent uncertainties due to limitations of available accelerator data. This leads to differences in shower simulations using each of those models. Many studies have been carried out to track those differences by investigating the shower development or the particle content. In this work, we propose a new approach to search for discrepancies and similarities between the models, via the IACT images resulting from the observations of hadronic air showers. We use simulations of H.E.S.S. as a show-case scenario and, by investigating variables of the camera images, we find potential indicators to highlight differences between models. Number of pixels, Hillas image size, and density showed the largest difference between the models. We then further explore the (in)compatibility of the models by combining all the variables and using Boosted Decision Trees. For protons, a significant difference in the classifier output is found for EPOS-LHC when compared to both QGSJET-II04 and Sybill 2.3d. For helium and nitrogen, QGSJET-II04 is shown to be the outlier case. No significant differences are found for silicon and iron. The distribution of (in)compatibility between the models in the phase space of shower parameters shows that a targeted search can be fruitful, with showers with energies of a few TeV and core closer to the large telescope presenting the largest power of separation.