Search

Hierarchical merging of galaxies plays an important role in galaxy formation and evolution. Mergers could trigger key evolutionary phases such as starburst activities and active accretion periods onto supermassive black holes at the centres of galaxies. We aim to detect mergers and merger stages (pre- and post-mergers) across cosmic history and test whether it is better to detect mergers and their merger stages simultaneously or hierarchically. In addition, we want to test the impact of merger time relative to the coalescence of merging galaxies. First, we generated realistic mock JWST images of simulated galaxies selected from the IllustrisTNG cosmological hydrodynamical simulations. Then we trained deep learning (DL) models in the Zoobot Python package to classify galaxies into merging/non-merging galaxies and their merger stages. We used two different set-ups: (i) two-stage, in which we classify galaxies into mergers and non-mergers and then classify the mergers into pre-mergers and post-mergers, and (ii) one-stage, in which merger/non-merger and merger stages are classified simultaneously. We found that the one-stage classification set-up moderately outperforms the two-stage set-up, offering better overall accuracy and precision, particularly for the non-merger class. Pre-mergers can be classified with the highest precision in both set-ups, possibly due to the more recognisable merging features and the presence of merging companions. The image signal-to-noise ratio affects the performance of the DL classifiers, but not much after a certain threshold is crossed. Both precision and recall of the classifiers depend strongly on merger time, finding it more difficult to identify true mergers observed at stages that are more distant to coalescence. For pre-mergers, we recommend selecting mergers which will merge in the next 0.4 Gyrs, to achieve a good balance between precision and recall.

We present Cryoscope -- a new 50 sq. deg field-of-view, 1.2 m aperture, K-dark survey telescope to be located at Dome C, Antarctica. Cryoscope has an innovative optical-thermal design wherein the entire telescope is cryogenically cooled. Cryoscope also explores new detector technology to cost-effectively tile the full focal plane. Leveraging the dark Antarctic sky and minimizing telescope thermal emission, Cryoscope achieves unprecedented deep, wide, fast and red observations, matching and exceeding volumetric survey speeds from the Ultraviolet Explorer, Vera Rubin Observatory, and Nancy Grace Roman Space Telescope. By providing coverage beyond wavelengths of 2 $\mu$m, we aim to create the most comprehensive dynamic movie of the most obscured reaches of the Universe. Cryoscope will be a dedicated discovery engine for electromagnetic emission from coalescing compact binaries, Earth-like exoplanets orbiting cold stars, and multiple facets of time-domain, stellar and solar system science. In this paper, we describe the scientific drivers and technical innovations for this new discovery engine operating in the K-dark passband, why we choose to deploy it in Antarctica, and the status of a fifth-scale prototype designed as a Pathfinder to retire technological risks prior to full-scale implementation., Comment: 36 pages, 20 figures, 4 tables; submitted to PASP on 2025-02-09

Aims: The relative roles of the physical mechanisms involved in quenching galaxy star formation are still unclear. We tackle this fundamental problem with our cosmological semi-empirical model DECODE (Discrete statistical sEmi-empiriCal mODEl), designed to predict galaxy stellar mass assembly histories, from minimal input assumptions. Methods: Specifically, in this work the star formation history of each galaxy is calculated along its progenitor dark matter halo by assigning at each redshift a star formation rate extracted from a monotonic star formation rate-halo accretion rate (SFR-HAR) relation derived from abundance matching between the (observed) SFR function and the (numerically predicted) HAR function, a relation that is also predicted by the TNG100 simulation. SFRs are integrated across cosmic time to build up the mass of galaxies, which may halt their star formation following input physical quenching recipes. Results: In this work we test the popular halo quenching scenario and we find that: 1) the assumption of a monotonic relation between SFR and HAR allows to reproduce the number densities of the bulk of star-forming galaxies in the local Universe; 2) the halo quenching is sufficient to reproduce the statistics of the quenched galaxies and flat (steep) high-mass end of the SMHM relation (SMF); and 3) to align with the observed steep (flat) low-mass end of the SMHM (SMF) additional quenching processes in the least massive haloes are needed. Conclusions: DECODE is an invaluable tool and will pave the way to investigate the origin of newly observed high-redshift objects from the latest ongoing facilities such as JWST and Euclid., Comment: 15 pages, 13 figures, accepted for publication in Astronomy & Astrophysics

Federated Learning presents a nascent approach to machine learning, enabling collaborative model training across decentralized devices while safeguarding data privacy. However, its distributed nature renders it susceptible to adversarial attacks. Integrating blockchain technology with Federated Learning offers a promising avenue to enhance security and integrity. In this paper, we tackle the potential of blockchain in defending Federated Learning against adversarial attacks. First, we test Proof of Federated Learning, a well known consensus mechanism designed ad-hoc to federated contexts, as a defense mechanism demonstrating its efficacy against Byzantine and backdoor attacks when at least one miner remains uncompromised. Second, we propose Krum Federated Chain, a novel defense strategy combining Krum and Proof of Federated Learning, valid to defend against any configuration of Byzantine or backdoor attacks, even when all miners are compromised. Our experiments conducted on image classification datasets validate the effectiveness of our proposed approaches., Comment: Submitted to Neural Networks

We present first results from James Webb Space Telescope (JWST) Near-Infrared Spectrograph (NIRSpec), Mid-Infrared Instrument (MIRI), and Keck Cosmic Webb Imager (KCWI) integral field spectroscopy of the powerful but highly obscured host-galaxy of the jetted radio source Cygnus A. We detect 169 infrared emission lines at 1.7--27 micron and explore the kinematics and physical properties of the extended narrow-line region (NLR) in unprecedented detail. The density-stratified NLR appears to be shaped by the initial blow-out and ongoing interaction of the radio jet with the interstellar medium, creating a multi-phase bicone with a layered structure composed of molecular and ionized gas. The NLR spectrum, with strong coronal emission at kpc-scale, is well-modeled by AGN photoionization. We find evidence that the NLR is rotating around the radio axis, perhaps mediated by magnetic fields and driven by angular momentum transfer from the radio jet. The overall velocity field of the NLR is well described by 250 km/s outflow along biconical spiral flow lines, combining both rotation and outflow signatures. There is particularly bright [Fe II] 1.644 micron emission from a dense, high-velocity dispersion, photoionized clump of clouds found near the projected radio axis. Outflows of 600--2000 km/s are found in bullets and streamers of ionized gas that may be ablated by the radio jet from these clouds, driving a local outflow rate of 40 Msun/yr., Comment: Submitted to Astrophysical Journal

Forbidden coronal lines has traditionally called the attention due to the high-energy photons required for their production (IP $>$100~eV, where IP is the ionisation potential of the transitions that originate the line). As such, they are regarded as the most highly ionised component of Active Galactic Nuclei (AGN). For decades, it was thought that they were only formed in the inner portions of the narrow line region (NLR). Nowadays, due to the larger sensitivity of the detectors and the availability of integral field unit (IFU) spectrographs, that emission in addition to the nuclear component is found to be extended up to a few kiloparsecs away from the active centre. In this review, we highlight the most important aspects of the coronal emission and discuss the recent developments in the field. In particular, we emphasize the discovery that they can be used to determine the mass of the central supermassive black hole, to reconstruct the SED, as well as to trace the most energetic feedback component of the ionised gas in AGN., Comment: 10 pages. Mini review, Accepted in Frontiers in Astronomy and Space Sciences, section Extragalactic Astronomy

This article presents the first search for neutrino-induced neutral current coherent single-photon production (NC coherent 1$\gamma$). The search makes use of data from the MicroBooNE 85-tonne active volume liquid argon time projection chamber detector, situated in the Fermilab Booster Neutrino Beam (BNB), with an average neutrino energy of $\langle E_{\nu}\rangle \sim 0.8$ GeV. A targeted selection of candidate neutrino interactions with a single photon-like electromagnetic shower in the final state and no visible vertex activity was developed to search for the NC coherent 1$\gamma$ process, along with two auxiliary selections used to constrain the dominant background from NC$\pi^0$ production. With an integrated exposure of $6.87 \times 10^{20}$ protons on target delivered by the BNB, we set the world's first limit for this rare process, corresponding to an upper limit on the flux-averaged cross section of $\sigma<1.49 \times 10^{-41}\text{cm}^2$ at 90\% C.L., Comment: 20 pages, 17 figures

We present an inclusive search for anomalous production of single-photon events from neutrino interactions in the MicroBooNE experiment. The search and its signal definition are motivated by the previous observation of a low-energy excess of electromagnetic shower events from the MiniBooNE experiment. We use the Wire-Cell reconstruction framework to select a sample of inclusive single-photon final-state interactions with a final efficiency and purity of 7.0% and 40.2%, respectively. We leverage simultaneous measurements of sidebands of charged current $\nu_{\mu}$ interactions and neutral current interactions producing $\pi^{0}$ mesons to constrain signal and background predictions and reduce uncertainties. We perform a blind analysis using a dataset collected from February 2016 to July 2018, corresponding to an exposure of $6.34\times10^{20}$ protons on target from the Booster Neutrino Beam (BNB) at Fermilab. In the full signal region, we observe agreement between the data and the prediction, with a goodness-of-fit $p$-value of 0.11. We then isolate a sub-sample of these events containing no visible protons, and observe $93\pm22\text{(stat.)}\pm35\text{(syst.)}$ data events above prediction, corresponding to just above $2\sigma$ local significance, concentrated at shower energies below 600 MeV., Comment: 9 pages, 6 figures

We present a dedicated automated pipeline to construct spatially resolved emission H$\alpha$+[NII] maps and to derive the spectral energy distributions (SEDs) in 12 optical filters (five broad and seven narrow/medium) of H$\alpha$ emission line regions in nearby galaxies (z $<$ 0.0165) observed by the Javalambre Photometric Local Universe Survey (J-PLUS). We used the $J0660$ filter of $140${\AA} width centered at $6600${\AA} to trace H$\alpha$ + [NII] emission and $r$ and $i$ broad bands were used to estimate the stellar continuum. We create pure emission line images after the continnum subtraction, where the H$\alpha$ emission line regions were detected. This method was also applied to Integral Field Unit (IFU) spectroscopic data from PHANGS-MUSE, CALIFA and MaNGA surveys by building synthetic narrow-bands based on J-PLUS filters. The studied sample includes the cross-matched catalog of these IFU surveys with J-PLUS third data release (DR3), amounting to $2$ PHANGS-MUSE, $78$ CALIFA, and $78$ MaNGA galaxies at $z < 0.0165$, respectively. We compared the H$\alpha$+[NII] radial profiles from J-PLUS and the IFU surveys, finding good agreement within the expected uncertainties. We also compared the SEDs from the emission line regions detected in J-PLUS images, reproducing the main spectral features present in the spectroscopic data. Finally, we compared the emission fluxes from the J-PLUS and IFU surveys accounting for scale differences, finding a difference of only 2% with a dispersion of 7% in the measurements. The J-PLUS data provides reliable spatially resolved H$\alpha$+[NII] emission maps for nearby galaxies. We provide the J-PLUS DR3 catalog for the $158$ galaxies with IFU data, including emission maps, SEDs of star-forming clumps, and radial profiles., Comment: 20 pages, 17 figures, 3 tables, Accepted for publication in Astronomy and Astrophysics

We report results from an updated search for neutral current (NC) resonant $\Delta$(1232) baryon production and subsequent $\Delta$ radiative decay (NC $\Delta\rightarrow N \gamma$). We consider events with and without final state protons; events with a proton can be compared with the kinematics of a $\Delta(1232)$ baryon decay, while events without a visible proton represent a more generic phase space. In order to maximize sensitivity to each topology, we simultaneously make use of two different reconstruction paradigms, Pandora and Wire-Cell, which have complementary strengths, and select mostly orthogonal sets of events. Considering an overall scaling of the NC $\Delta\rightarrow N \gamma$ rate as an explanation of the MiniBooNE anomaly, our data exclude this hypothesis at 94.4% CL. When we decouple the expected correlations between NC $\Delta\rightarrow N \gamma$ events with and without final state protons, and allow independent scaling of both types of events, our data exclude explanations in which excess events have associated protons, and do not exclude explanations in which excess events have no associated protons.

Physical-Layer Network Coding (PNC) is an effective technique to improve the throughput and latency in wireless networks. However, there are two major challenges for PNC, especially when using higher order modulations: 1) phase synchronization and power control at the paired User Equipments (UEs); and 2) the ambiguity removal of the PNC mapping at the relay node. To address these challenges, in this paper, we apply power control at transmitting UEs and exploit Reconfigurable Intelligent Surfaces (RISs) to synchronize the phase of the transmitted signals and ensure that they arrive at the relay with the same power and phase rotation. Then, we employ modular addition for an unambiguous PNC mapping for M-ary Quadrature Amplitude Modulations (M-QAM). We evaluate the performance of the system in the framework of Orthogonal Frequency Division Multiplexing (OFDM)-PNC for different RIS sizes and modulation orders. Furthermore, we study the sensitivity of PNC systems for Channel Estimation Error (CEE). The results reveal that 1) PNC systems show quite higher sensitivity to CEE compared with RIS-assisted one-way relay channel systems; 2) when the CEE is low, RIS can considerably enhance the Signal-to-Noise Ratio (SNR) of the PNC system, e.g., for a Bit Error Rate (BER) of $10^{-3}$ (without channel coding), increasing the RIS size from one to 256 elements in 28 GHz band leads to 200% improvement in SNR., Comment: Article presented at IEEE GLOBECOM 2023

Integrated Access and Backhaul (IAB) has been recently proposed by 3GPP to enable network operators to deploy fifth generation (5G) mobile networks with reduced costs. In this paper, we propose to use IAB to build a dynamic wireless backhaul network capable to provide additional capacity to those Base Stations (BS) experiencing congestion momentarily. As the mobile traffic demand varies across time and space, and the number of slice combinations deployed in a BS can be prohibitively high, we propose to use Deep Reinforcement Learning (DRL) to select, from a set of candidate BSs, the one that can provide backhaul capacity for each of the slices deployed in a congested BS. Our results show that a Double Deep Q-Network (DDQN) agent using a fully connected neural network and the Rectified Linear Unit (ReLU) activation function with only one hidden layer is capable to perform the BS selection task successfully, without any failure during the test phase, after being trained for around 20 episodes., Comment: Article presented at IEEE GLOBECOM 2023

The main objective of this paper is to study the relationship between a solvable evolution algebra and its subalgebra lattice, emphasizing two of its main properties: distributivity and modularity. First, we will focus on the nilpotent case, where distributivity is characterised, and a necessary condition for modularity is deduced. Subsequently, we comment on some results for solvable non-nilpotent evolution algebras, finding that the ones with maximum index of solvability have the best properties. Finally, we characterise modularity in this particular case by introducing supersolvable evolution algebras and computing the terms of the derived series., Comment: 20 pages

White dwarfs (WDs) showing transits from orbiting planetary debris provide significant insights into the structure and dynamics of debris disks. This is a rare class of objects with only eight published systems. In this work, we perform a systematic search for such systems within 500 pc in the Gaia-eDR3 catalog of WDs using the light curves from the Zwicky Transient Facility (ZTF) and present six new candidates. Our selection process targets the top 1% most photometrically variable sources identified using a combined variability metric from ZTF and Gaia eDR3 photometry, boosted by a metric space we define using von Neumann statistics and Pearson-Skew as a novel discovery tool to identify these systems. This is followed by optical spectroscopic observations of visually selected variables to confirm metal pollution. Four of the six systems show long-timescale photometric variability spanning several months to years, resulting either from long-term evolution of transit activity or dust and debris clouds at wide orbits. Among them, WD J1013-0427 shows an indication of reddening during the long-duration dip. Interpreting this as dust extinction makes it the first system to indicate an abundance of small dust grains (radius $\lesssim$$0.3~{\rm \mu m}$) in the occulting material. The same object also shows metal emission lines that map an optically thick eccentric gas disk orbiting within the star's Roche limit. For each candidate, we infer the abundances of the photospheric metals and estimate accretion rates. We show that transiting debris systems tend to have higher inferred accretion rates compared to the general population of metal-polluted WDs. Growing the number of these systems will further illuminate such comparative properties in the near future. Separately, we also serendipitously discovered an AM CVn showing a very long-duration outburst $-$ only the fourth such system to be known., Comment: 28 pages, 16 figures, 6 tables. Version after first PASP review. Resubmitting in a week. Comments are Welcome!

The emergence and stability of static hexadecapole deformations as well as the impact in the development of dynamic deformation due to collective motion considering quadrupole-hexadecapole coupling are studied for a selected set of radium, thorium, uranium and plutonium isotopes, using the Gogny Hartree-Fock-Bogoliubov and Generator Coordinate Method frameworks. Sizable hexadecapole deformations are found to play a significant role in the ground and excited states of nuclei in the neighborhood of $^{238}$U. For each of the studied isotopic chains, it is shown that a region with small negative hexadecapole deformation, just below the neutron magic number $N =184$, remains stable once zero-point quadrupole-hexadecapole fluctuations are taken into account. A transition is predicted, with increasing mass number, from a regime in which the quadrupole and hexadecapole degrees of freedom are interwoven to a regime in which they are decoupled, accompanied by an enhanced shape coexistence in the more neutron-rich sectors of the isotopic chains. It is also shown, that quadrupole-hexadecapole configuration mixing brings a nontrivial additional correlation energy gain comparable to the quadrupole correlation energy itself., Comment: 14 pages, 8 figures

The bio-inspired engineering of ovipositing wasps, which employ a reciprocating motion for soft tissue insertion, offers potential advantages in reducing insertion force and minimizing tissue damage. However, the underlying mechanisms of tissue interaction and sparing are not fully understood. In this study, we aim to investigate a multi-part probe designed to mimic the reciprocating motion of ovipositors. A reciprocal insertion model was developed to study the interaction between the probe and soft tissue, and experimental testing was conducted using a force sensor and laser optical technique to gain insights into interacting forces and tissue deformation. The results reveal that during the cutting phase of reciprocal motion, the peak force and average displacement of the soft substrate were approximately 19% and 20% lower, respectively, compared to direct insertion at an overall probe velocity of 1 mm/s. This study presents a novel approach combining mechanical modeling and experimental analysis to explore the force mechanics of the reciprocating insertion method, providing a better understanding of the interaction between the probe and soft tissue., Comment: 11 pages with 11 figures

This paper presents a novel approach to efficiently parameterize and estimate the state of a hanging tether for path and trajectory planning of a UGV tied to a UAV in a marsupial configuration. Most implementations in the state of the art assume a taut tether or make use of the catenary curve to model the shape of the hanging tether. The catenary model is complex to compute and must be instantiated thousands of times during the planning process, becoming a time-consuming task, while the taut tether assumption simplifies the problem, but might overly restrict the movement of the platforms. In order to accelerate the planning process, this paper proposes defining an analytical model to efficiently compute the hanging tether state, and a method to get a tether state parameterization free of collisions. We exploit the existing similarity between the catenary and parabola curves to derive analytical expressions of the tether state., Comment: Submitted in T-RO

Let $(X,T,\mu,d)$ be a metric measure-preserving system for which $3$-fold correlations decay exponentially for Lipschitz continuous observables. Suppose that $(M_k)$ is a sequence satisfying some weak decay conditions and suppose there exist open balls $B_k(x)$ around $x$ such that $\mu(B_k(x)) = M_k$. Under a short return time assumption, we prove a strong Borel--Cantelli lemma, including an error term, for recurrence, i.e., for $\mu$-a.e. $x \in X$, \[ \sum_{k=1}^{n} \mathbf{1}_{B_k(x)} (T^k x) = \Phi(n) + O \bigl( \Phi(n)^{1/2} (\log \Phi(n))^{3/2 + \varepsilon} \bigr), \] where $\Phi(n) = \sum_{k=1}^{n} \mu(B_k(x))$. Applications to systems include some non-linear piecewise expanding interval maps and hyperbolic automorphisms of $\mathbf{T}^2$., Comment: 22 pages, 3 figures

While the sources of the diffuse astrophysical neutrino flux detected by the IceCube Neutrino Observatory are still largely unknown, one of the promising methods used towards understanding this is investigating the potential temporal and spatial correlations between neutrino alerts and the electromagnetic radiation from blazars. We report on the multiwavelength target-of-opportunity observations of the blazar B3 2247+381, taken in response to an IceCube multiplet alert for a cluster of muon neutrino events compatible with the source location between May 20, 2022 and November 10, 2022. B3 2247+381 was not detected with VERITAS during this time period. The source was found to be in a low-flux state in the optical, ultraviolet and gamma-ray bands for the time interval corresponding to the neutrino event, but was detected in the hard X-ray band with NuSTAR during this period. We find the multiwavelength spectral energy distribution is well described using a simple one-zone leptonic synchrotron self-Compton radiation model. Moreover, assuming the neutrinos originate from hadronic processes within the jet, the neutrino flux would be accompanied by a photon flux from the cascade emission, and the integrated photon flux required in such a case would significantly exceed the total multiwavelength fluxes and the VERITAS upper limits presented here. The lack of flaring activity observed with VERITAS, combined with the low multiwavelength flux levels, and given the significance of the neutrino excess is at 3$\sigma$ level (uncorrected for trials), makes B3 2247+381 an unlikely source of the IceCube multiplet. We conclude that the neutrino excess is likely a background fluctuation., Comment: 26 pages, 5 figures. Accepted for publication in the Astrophysical Journal (ApJ)

Astrometry from Gaia has enabled discovery of three dormant black holes (BHs) in au-scale binaries. Numerous models have been proposed to explain their formation, including several that have forecasted Gaia detections. However, previous works have used simplified detectability metrics that do not capture key elements of the Gaia astrometric orbit selection function. We apply a realistic forward-model of Gaia astrometric orbit catalogs to BH binary populations generated through (a) isolated binary evolution (IBE) and (b) dynamical formation in star clusters. For both formation channels, we analyze binary populations in a simulated Milky Way-like galaxy with a realistic metallicity-dependent star formation history and 3D dust map. We generate epoch astrometry for each binary from the Gaia scanning law and fit it with the cascade of astrometric models used in Gaia DR3. The IBE model of Chawla et al. (2022) predicts that no BH binaries should have been detected in DR3 and thus significantly underpredicts the formation rate of Gaia BHs. In contrast, the dynamical model of Di Carlo et al. (2024) overpredicts the number of BHs receiving DR3 orbital solutions by a factor of $\sim$8. The two models predict very different orbital period distributions, with the IBE model predicting only binaries that avoided common envelope evolution and have $P_{\text{orb}} \gtrsim 2,000$ d to be detectable, and the dynamical formation model predicting a period distribution that is roughly log-uniform. Adopting the dynamical channel as a fiducial model and rescaling by a factor of 1/8 to match DR3, we predict that $\sim$30 BH binaries will be detected in Gaia DR4, representing $\sim0.1\%$ of Milky Way BHs with luminous companions in au-scale orbits., Comment: 17 pages, 14 figures, Submitted to PASP

This paper studies the Bayesian regret of the Thompson Sampling algorithm for bandit problems, building on the information-theoretic framework introduced by Russo and Van Roy (2015). Specifically, it extends the rate-distortion analysis of Dong and Van Roy (2018), which provides near-optimal bounds for linear bandits. A limitation of these results is the assumption of a finite action space. We address this by extending the analysis to settings with infinite and continuous action spaces. Additionally, we specialize our results to bandit problems with expected rewards that are Lipschitz continuous with respect to the action space, deriving a regret bound that explicitly accounts for the complexity of the action space., Comment: 5 pages, accepted to ICASSP

We have generated 2D-multicomponent surface brightness (SB) modelling for 100 galaxies in the Large Galaxy Atlas (LGA) together with 19 nearby cD galaxies using the near-infrared (NIR) images from 2MASS (J, H and Ks ). Our final sample of 119 galaxies includes cD galaxies, Virgo cluster galaxies, group galaxies, and field galaxies. We revisited known scaling relations (SRs) involving structural parameters, as well as those involving supermassive black holes (SMBHs) and ultramassive black holes (UMBHs). Refining the SRs, we also revisited the bulge classification and considered the Fundamental Plane (FP) and its projections, as well as other SRs, such as the colour-magnitude relation (CMR), Tully-Fisher relation (TFR) and luminosity concentration relation (LCR). Classical bulges follow the same relations as elliptical galaxies, while pseudobulges are usually outliers. The NIR colours of classical bulges and pseudobulges indicate that their ages are not radically different despite their spread in luminosity, but we noticed that classical bulges are more luminous than pseudobulges, therefore, this property provides a complementary bulge classification criterion. We included pseudobulges from other studies to strengthen the tendencies seen for pseudobulges in our sample. From the SRs for BHs, we found that pseudobulges do not follow SRs for early-type galaxies and classical bulges. Additionally, the lack of correlation between BHs and discs may indicate these structures have not coevolved. From the revision of SRs, we present a sample of galaxies likely to host SMBHs or UMBHs, which are suitable for dynamical BH mass determination from the ground., Comment: 25 pages, 16 figures, Accepted in MNRAS. Accepted 2025 February 14. Received 2025 January 30; in original form 2024 June 06

Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges on having a good connector that maps visual features generated by a vision encoder to a shared embedding space with the LLM while preserving semantic similarity. Existing connectors, such as multilayer perceptrons (MLPs), often produce out-of-distribution or noisy inputs, leading to misalignment between the modalities. In this work, we propose a novel vision-text alignment method, AlignVLM, that maps visual features to a weighted average of LLM text embeddings. Our approach leverages the linguistic priors encoded by the LLM to ensure that visual features are mapped to regions of the space that the LLM can effectively interpret. AlignVLM is particularly effective for document understanding tasks, where scanned document images must be accurately mapped to their textual content. Our extensive experiments show that AlignVLM achieves state-of-the-art performance compared to prior alignment methods. We provide further analysis demonstrating improved vision-text feature alignment and robustness to noise.

Criteria for a diffeomorphism of a smooth manifold $M$ to be lifted to a linear automorphism of a given real vector bundle $p\colon V\rightarrow M$, are stated. Examples are included and the metric and complex vector-bundle cases are also considered.

We present a search for the diffuse extremely-high-energy neutrino flux using $12.6$ years of IceCube data. The non-observation of neutrinos with energies well above $10 \, \mathrm{PeV}$ constrains the all-flavor neutrino flux at $10^{18} \, \mathrm{eV}$ to a level of $E^2 \Phi_{\nu_e + \nu_\mu + \nu_\tau} \simeq 10^{-8} \, \mathrm{GeV} \, \mathrm{cm}^{-2} \, \mathrm{s}^{-1} \, \mathrm{sr}^{-1}$, the most stringent limit to date. Using this data, we constrain the proton fraction of ultra-high-energy cosmic rays (UHECRs) above $\simeq 30 \, \mathrm{EeV}$ to be $\lesssim 70\,$% (at $90\,$% CL) if the cosmological evolution of the sources is comparable to or stronger than the star formation rate. This result complements direct air-shower measurements by being insensitive to uncertainties associated with hadronic interaction models. It is the first such result to disfavor the ``proton-only" hypothesis for UHECRs using neutrino data.

State of the art large language models rely on randomization to respond to a prompt. As an immediate consequence, a model may respond differently to the same prompt if asked multiple times. In this work, we argue that the evaluation and ranking of large language models should control for the randomization underpinning their functioning. Our starting point is the development of a causal model for coupled autoregressive generation, which allows different large language models to sample responses with the same source of randomness. Building upon our causal model, we first show that, on evaluations based on benchmark datasets, coupled autoregressive generation leads to the same conclusions as vanilla autoregressive generation but using provably fewer samples. However, we further show that, on evaluations based on (human) pairwise comparisons, coupled and vanilla autoregressive generation can surprisingly lead to different rankings when comparing more than two models, even with an infinite amount of samples. This suggests that the apparent advantage of a model over others in existing evaluation protocols may not be genuine but rather confounded by the randomness inherent to the generation process. To illustrate and complement our theoretical results, we conduct experiments with several large language models from the Llama family. We find that, across multiple knowledge areas from the popular MMLU benchmark dataset, coupled autoregressive generation requires up to 40% fewer samples to reach the same conclusions as vanilla autoregressive generation. Further, using data from the LMSYS Chatbot Arena platform, we find that the win-rates derived from pairwise comparisons by a strong large language model to prompts differ under coupled and vanilla autoregressive generation.

In this work, we introduce a novel framework for privately optimizing objectives that rely on Wasserstein distances between data-dependent empirical measures. Our main theoretical contribution is, based on an explicit formulation of the Wasserstein gradient in a fully discrete setting, a control on the sensitivity of this gradient to individual data points, allowing strong privacy guarantees at minimal utility cost. Building on these insights, we develop a deep learning approach that incorporates gradient and activations clipping, originally designed for DP training of problems with a finite-sum structure. We further demonstrate that privacy accounting methods extend to Wasserstein-based objectives, facilitating large-scale private training. Empirical results confirm that our framework effectively balances accuracy and privacy, offering a theoretically sound solution for privacy-preserving machine learning tasks relying on optimal transport distances such as Wasserstein distance or sliced-Wasserstein distance.

Large Language Models (LLMs) have gained unprecedented prominence, achieving widespread adoption across diverse domains and integrating deeply into society. The capability to fine-tune general-purpose LLMs, such as Generative Pre-trained Transformers (GPT), for specific tasks has facilitated the emergence of numerous Custom GPTs. These tailored models are increasingly made available through dedicated marketplaces, such as OpenAI's GPT Store. However, their black-box nature introduces significant safety and compliance risks. In this work, we present a scalable framework for the automated evaluation of Custom GPTs against OpenAI's usage policies, which define the permissible behaviors of these systems. Our framework integrates three core components: (1) automated discovery and data collection of models from the GPT store, (2) a red-teaming prompt generator tailored to specific policy categories and the characteristics of each target GPT, and (3) an LLM-as-a-judge technique to analyze each prompt-response pair for potential policy violations. We validate our framework with a manually annotated ground truth, and evaluate it through a large-scale study with 782 Custom GPTs across three categories: Romantic, Cybersecurity, and Academic GPTs. Our manual annotation process achieved an F1 score of 0.975 in identifying policy violations, confirming the reliability of the framework's assessments. The results reveal that 58.7% of the analyzed models exhibit indications of non-compliance, exposing weaknesses in the GPT store's review and approval processes. Furthermore, our findings indicate that a model's popularity does not correlate with compliance, and non-compliance issues largely stem from behaviors inherited from base models rather than user-driven customizations. We believe this approach is extendable to other chatbot platforms and policy domains, improving LLM-based systems safety.

Low-Rank Adaptation (LoRA) and its variants have shown impressive results in reducing the number of trainable parameters and memory requirements of large transformer networks while maintaining fine-tuning performance. However, the low-rank nature of the weight update inherently limits the representation power of fine-tuned models, potentially compromising performance on complex tasks. This raises a critical question: when a performance gap between LoRA and standard fine-tuning is observed, is it due to the reduced number of trainable parameters or the rank deficiency? This paper aims to answer this question by introducing RandLoRA, a parameter-efficient method that performs full-rank updates using a learned linear combinations of low-rank, non-trainable random matrices. Our method limits the number of trainable parameters by restricting optimization to diagonal scaling matrices applied to the fixed random matrices. This allows us to effectively overcome the low-rank limitations while maintaining parameter and memory efficiency during training. Through extensive experimentation across vision, language, and vision-language benchmarks, we systematically evaluate the limitations of LoRA and existing random basis methods. Our findings reveal that full-rank updates are beneficial across vision and language tasks individually, and even more so for vision-language tasks, where RandLoRA significantly reduces -- and sometimes eliminates -- the performance gap between standard fine-tuning and LoRA, demonstrating its efficacy., Comment: To appear at the International Conference on Learning Representations (ICLR) 2025

This paper investigates two FEM-BEM coupling formulations for acoustic fluid-structure interaction (FSI) problems, using the Finite Element Method (FEM) to model the structure and the Boundary Element Method (BEM) to represent a linear acoustic fluid. The coupling methods described interconnect fluid and structure using classical or localized Lagrange multipliers, allowing the connection of non-matching interfaces. First coupling technique is the well known mortar method, that uses classical multipliers and is compared with a new formulation of the method of localized Lagrange multipliers (LLM) for FSI applications with non-matching interfaces. The proposed non-overlapping domain decomposition technique uses a classical non-symmetrical acoustic BEM formulation for the fluid, although a symmetric Galerkin BEM formulation could be used as well. A comparison between the localized methodology and the mortar method in highly non conforming interface meshes is presented. Furthermore, the methodology proposes an iterative preconditioned and projected bi-conjugate gradient solver which presents very good scalability properties in the solution of this kind of problems.

Seismic data preconditioning is essential for subsurface interpretation. It enhances signal quality while attenuating noise, improving the accuracy of geophysical tasks that would otherwise be biased by noise. Although classical poststack seismic data enhancement methods can effectively reduce noise, they rely on predefined statistical distributions, which often fail to capture the complexity of seismic noise. On the other hand, deep learning methods offer an alternative but require large and diverse data sets. Typically, static databases are used for training, introducing domain bias, and limiting adaptability to new noise poststack patterns. This work proposes a novel two-process dynamic training method to overcome these limitations. Our method uses a dynamic database that continuously generates clean and noisy patches during training to guide the learning of a supervised enhancement network. This dynamic-guided learning workflow significantly improves generalization by introducing variability into the training data. In addition, we employ a domain adaptation via a neural style transfer strategy to address the potential challenge of encountering unknown noise domains caused by specific geological configurations. Experimental results demonstrate that our method outperforms state-of-the-art solutions on both synthetic and field data, within and outside the training domain, eliminating reliance on known statistical distributions and enhancing adaptability across diverse data sets of poststack data., Comment: 13 pages, 10 figures. Submitted to Geophysics

Due to the lack of a net magnetic moment, antiferromagnets possess a unique robustness to external magnetic fields and are thus predicted to play an important role in future magnetic technologies. However, this robustness also makes them quite difficult to control, and the development of novel methods to manipulate these systems with external stimuli is a fundamental goal of antiferromagnetic spintronics. In this work, we report evidence for a metastable reorientation of the order parameter in an antiferromagnetic semiconductor triggered by an ultrafast quench of the equilibrium order via photoexcitation above the band gap. The metastable state forms less than 10 ps after the excitation pulse, and persists for longer than 150 ps before decaying to the ground state via thermal fluctuations. Importantly, this transition cannot be induced thermodynamically, and requires the system to be driven out of equilibrium. Broadly speaking, this phenomenology is ultimately the result of large magnetoelastic coupling in combination with a relatively low symmetry of the magnetic ground state. Since neither of these properties are particularly uncommon in magnetic materials, the observations presented here imply a generic path toward novel device technology enabled by ultrafast dynamics in antiferromagnets., Comment: 41 pages, 27 figures

A coupled boundary spectral element method (BSEM) and spectral element method (SEM) formulation for the propagation of small-amplitude water waves over variable bathymetries is presented in this work. The wave model is based on the mild-slope equation (MSE), which provides a good approximation of the propagation of water waves over irregular bottom surfaces with slopes up to 1:3. In unbounded domains or infinite regions, space can be divided into two different areas: a central region of interest, where an irregular bathymetry is included, and an exterior infinite region with straight and parallel bathymetric lines. The SEM allows us to model the central region, where any variation of the bathymetry can be considered, while the exterior infinite region is modelled by the BSEM which, combined with the fundamental solution presented by Cerrato et al. [A. Cerrato, J. A. Gonz\'alez, L. Rodr\'iguez-Tembleque, Boundary element formulation of the mild-slope equation for harmonic water waves propagating over unidirectional variable bathymetries, Eng. Anal. Boundary Elem. 62 (2016) 22-34.] can include bathymetries with straight and parallel contour lines. This coupled model combines important advantages of both methods; it benefits from the flexibility of the SEM for the interior region and, at the same time, includes the fulfilment of the Sommerfeld's radiation condition for the exterior problem, that is provided by the BSEM. The solution approximation inside the elements is constructed by high order Legendre polynomials associated with Legendre-Gauss-Lobatto quadrature points, providing a spectral convergence for both methods. The proposed formulation has been validated in three different benchmark cases with different shapes of the bottom surface. The solutions exhibit the typical p-convergence of spectral methods.

This paper presents a boundary element formulation for the solution of the Mild-Slope equation in wave propagation problems with variable water depth in one direction. Based on the Green's function approximation proposed by Belibassakis \cite{Belibassakis2000}, a complete fundamental-solution kernel is developed and combined with a boundary element scheme for the solution of water wave propagation problems in closed and open domains where the bathymetry changes arbitrarily and smoothly in a preferential direction. The ability of the proposed formulation to accurately represent wave phenomena like refraction, reflection, diffraction and shoaling, is demonstrated with the solution of some example problems, in which arbitrary geometries and variable seabed profiles with slopes up to 1:3 are considered. The obtained results are also compared with theoretical solutions, showing an excellent agreement that demonstrates its potential.

P300 is an Event-Related Potential widely used in Brain-Computer Interfaces, but its detection is challenging due to inter-subject and temporal variability. This work introduces a clustering methodology based on Normalized Compression Distance (NCD) to extract the P300 structure, ensuring robustness against variability. We propose a novel signal-to-ASCII transformation to generate compression-friendly objects, which are then clustered using a hierarchical tree-based method and a multidimensional projection approach. Experimental results on two datasets demonstrate the method's ability to reveal relevant P300 structures, showing clustering performance comparable to state-of-the-art approaches. Furthermore, analysis at the electrode level suggests that the method could assist in electrode selection for P300 detection. This compression-driven clustering methodology offers a complementary tool for EEG analysis and P300 identification.

Text datasets can be represented using models that do not preserve text structure, or using models that preserve text structure. Our hypothesis is that depending on the dataset nature, there can be advantages using a model that preserves text structure over one that does not, and viceversa. The key is to determine the best way of representing a particular dataset, based on the dataset itself. In this work, we propose to investigate this problem by combining text distortion and algorithmic clustering based on string compression. Specifically, a distortion technique previously developed by the authors is applied to destroy text structure progressively. Following this, a clustering algorithm based on string compression is used to analyze the effects of the distortion on the information contained in the texts. Several experiments are carried out on text datasets and artificially-generated datasets. The results show that in strongly structural datasets the clustering results worsen as text structure is progressively destroyed. Besides, they show that using a compressor which enables the choice of the size of the left-context symbols helps to determine the nature of the datasets. Finally, the results are contrasted with a method based on multidimensional projections and analogous conclusions are obtained.

In this paper an extensive database of SPARC H-modes confinement predictions has been provided, to assess its variability with respect to few input assumptions. The simulations have been performed within the ASTRA framework, using the quasi-linear model TGLF SAT2, including electromagnetic effects, for the core transport, and a neural network trained on EPED simulations to predict the pedestal height and width self-consistently. The database has been developed starting from two SPARC H-mode discharges (12.2 T, i.e. Primary Reference Discharge or PRD, and 8 T, i.e. reduced field) and permuting 4 input parameters (W concentration, DT mixture concentration, temperature ratio at top of pedestal and deviation of pedestal pressure from the EPED prediction), to perform a sensitivity study. For the PRD a scan of auxiliary input power (ion cyclotron heating) has been performed up to 25MW, to keep highly radiative plasmas above the LH power threshold as predicted by Martin and Schmidtmayr power scalings. A scan of pedestal density has then been performed for both PRD and 8T databases. ptop/pEPED and Ti/Te at top of pedestal showed the biggest impact on the fusion gain. Significant variation is observed across the database, highlighting the importance of sensitivity studies. Below a certain W concentration, the 12T database shows that Q > 5 is consistently achieved for full-field H-modes with 11 MW of auxiliary power, and values of Q > 2 are assured when increasing the input power to keep the plasma in H-mode. The 8T database demonstrates that SPARC can access a Q > 1 operational window with low W concentration, making it a potentially interesting scenario for obtaining breakeven conditions.

In this paper, we offer a wider perspective of the general problem of distributed runtime verification of distributed systems, in fully asynchronous fault-tolerant environments. We study this problem in an asynchronous shared memory model with crash failures where correctness properties are defined as languages, and the aim is to design wait-free algorithms that decide languages in a distributed manner. A decidability definition states the possible values processes can report in an execution, and provides semantics to the values. We propose several decidability definitions, study the relations among them, and prove possibility and impossibility results. One of our main results is a characterization of the correctness properties that can be decided asynchronously. Remarkably, it applies to any language decidability definition. Intuitively, the characterization is that only properties with no real-time order constraints can be decided in asynchronous fault-tolerant settings. As a consequence, there are correctness properties, like linearizability and strong eventual counters, that are unverifiable, no matter the number of possible values processes can report in an execution, and the semantics one gives to those values. We present, however, techniques to evade this strong impossibility result, that combine an indirect runtime verification approach and relaxed decidability definitions. All possibility results use only read/write registers, hence can be simulated in asynchronous messages-passing systems where less than half of the processes can crash, and the impossibility results hold even if processes use powerful operations with arbitrary large consensus number. The results presented here also serve to put previous work in a more general context.

Wakefield wavelengths associated with solid-state plasmas greatly limit the accelerating length. An alternative approach employs 2D carbon-based nanomaterials, like graphene or carbon nanotubes (CNTs), configured into structured targets. These nanostructures are designed with voids or low-density regions to effectively reduce the overall plasma density. This reduction enables the use of longer-wavelength lasers and also extends the plasma wavelength and the acceleration length. In this study, we present, to our knowledge, the first numerical demonstration of electron acceleration via self-injection into a wakefield bubble driven by an infrared laser pulse in structured CNT targets, similar to the behavior observed in gaseous plasmas for LWFA in the nonlinear (or bubble) regime. Using the PIConGPU code, bundles of CNTs are modeled in a 3D geometry as 25 nm-thick carbon tubes with an initial density of $10^{22}$ cm$^{-3}$. The carbon plasma is ionized by a three-cycle, 800 nm wavelength laser pulse with a peak intensity of $10^{21}$ W cm$^{-2}$, achieving an effective plasma density of $10^{20}$ cm$^{-3}$. The same laser also drives the wakefield bubble, responsible for the electron self-injection and acceleration. Simulation results indicate that fs-long electron bunches with hundreds of pC charge can be self-injected and accelerated at gradients exceeding 1~TeV$/$m. Both charge and accelerating gradient figures are unprecedented when compared with LWFA in gaseous plasma., Comment: 11 pages, 8 figures

Direct fidelity estimation is a protocol that estimates the fidelity between an experimental quantum state and a target pure state. By measuring the expectation values of Pauli operators selected through importance sampling, the method is exponentially faster than full quantum state tomography. We propose an enhanced direct fidelity estimation protocol that uses fewer copies of the experimental state by grouping Pauli operators before the sampling process. We derive analytical bounds on the measurement cost and estimator variance, showing improvements over the standard method. Numerical simulations validate our approach, demonstrating that for 8-qubit Haar-random states, our method achieves a one-third reduction in the required number of copies and reduces variance by an order of magnitude using only local measurements. These results underscore the potential of our protocol to enhance the efficiency of fidelity estimation in current quantum devices.

Spallation-induced fission reactions, in combination with state-of-the-art dynamical calculations, provide a robust framework for studying the nuclear dissipation mechanism in nuclear matter over a wide range of deformations. Experimental datasets of neutron- and proton-induced fission, accumulated over the last decades, cover a wide range of nuclei from the pre-actinide to the actinide region. In this work, these datasets are used to benchmark the dynamical Li\`ege intranuclear cascade model INCL++ integrated with the ABLA++ de-excitation code, which has been improved by updating the calculation of particle separation energies to the atomic mass evaluation (AME2020) and incorporating phenomenological corrections to enhance the description of fission-barrier heights. The results show good agreement with the available experimental data of total fission cross sections induced by protons and neutrons, thereby confirming the applicability and predictive capability of these models. Moreover, the improved framework is used to investigate the fission cross sections of exotic heavy fissile nuclei, far from the valley of stability, with masses $A > 155$., Comment: 17 pages, 12 figures

Amorphous and amorphous porous palladium are key materials for catalysis, hydrogen storage, and functional applications, but their complex structures present computational challenges. This study employs a deep neural network trained on 33,310 atomic configurations from ab initio molecular dynamics simulations to model their interatomic potential. The AI-driven approach accurately predicts structural and thermal properties while significantly reducing computational costs. Validation against density functional theory confirms its reliability in reproducing forces, energies, and structural distributions. These findings highlight AI's potential in accelerating the study of amorphous materials and advancing their applications in energy and catalysis., Comment: 8 pages, 4 figures

This paper presents a novel hybrid approach to solving real-world drone routing problems by leveraging the capabilities of quantum computing. The proposed method, coined Quantum for Drone Routing (Q4DR), integrates the two most prominent paradigms in the field: quantum gate-based computing, through the Eclipse Qrisp programming language; and quantum annealers, by means of D-Wave System's devices. The algorithm is divided into two different phases: an initial clustering phase executed using a Quantum Approximate Optimization Algorithm (QAOA), and a routing phase employing quantum annealers. The efficacy of Q4DR is demonstrated through three use cases of increasing complexity, each incorporating real-world constraints such as asymmetric costs, forbidden paths, and itinerant charging points. This research contributes to the growing body of work in quantum optimization, showcasing the practical applications of quantum computing in logistics and route planning., Comment: 8 pages, 5 figures. Paper submitted to IEEE Congress on Evolutionary Computation (IEEE CEC 2025)

In this study, we expand upon the FLIP benchmark-designed for evaluating protein fitness prediction models in small, specialized prediction tasks-by assessing the performance of state-of-the-art large protein language models, including ESM-2 and SaProt on the FLIP dataset. Unlike larger, more diverse benchmarks such as ProteinGym, which cover a broad spectrum of tasks, FLIP focuses on constrained settings where data availability is limited. This makes it an ideal framework to evaluate model performance in scenarios with scarce task-specific data. We investigate whether recent advances in protein language models lead to significant improvements in such settings. Our findings provide valuable insights into the performance of large-scale models in specialized protein prediction tasks.

Pollution that originates from automobiles is a concern in the current world, not only because of global warming, but also due to the harmful effects on people's health and lives. Despite regulations on exhaust gas emissions being applied, minimizing unsuitable driving habits that cause elevated fuel consumption and emissions would achieve further reductions. For that reason, this work proposes a self-organized map (SOM)-based intelligent system in order to provide drivers with eco-driving-intended driving style (DS) recommendations. The development of the DS advisor uses driving data from the Uyanik instrumented car. The system classifies drivers regarding the underlying causes of non-optimal DSs from the eco-driving viewpoint. When compared with other solutions, the main advantage of this approach is the personalization of the recommendations that are provided to motorists, comprising the handling of the pedals and the gearbox, with potential improvements in both fuel consumption and emissions ranging from the 9.5\% to the 31.5\%, or even higher for drivers that are strongly engaged with the system. It was successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx ZynQ programmable system-on-a-chip (PSoC) family. This SOM-based system allows for real-time implementation, state-of-the-art timing performances, and low power consumption, which are suitable for developing advanced driving assistance systems (ADASs).

A new geometric approach to systems with boundary energy flow is developed using infinite-dimensional Dirac structures within the Lagrangian formalism. This framework satisfies a list of consistency criteria with the geometric setting of finite-dimensional mechanics. In particular, the infinite-dimensional Dirac structure can be constructed from the canonical symplectic form on the system's phase space; the system's evolution equations can be derived equivalently from either a variational perspective or a Dirac structure perspective; the variational principle employed is a direct extension of Hamilton's principle in classical mechanics; and the approach allows for a process of system interconnection within its formulation. This is achieved by developing an appropriate infinite dimensional version of the previously developed Lagrange-Dirac systems. A key step in this construction is the careful choice of a suitable dual space to the configuration space, specifically, a subspace of the topological dual that captures the system's behavior in both the interior and the boundary, while allowing for a natural extension of the canonical geometric structures of mechanics. This paper focuses on systems where the configuration space consists of differential forms on a smooth manifold with a boundary. To illustrate our theory, several examples, including nonlinear wave equations, the telegraph equation, and the Maxwell equations are presented.

A discrete time crystal (DTC) is the paradigmatic example of a phase of matter that occurs exclusively in systems out of equilibrium. This phenomenon is characterized by the spontaneous symmetry breaking of discrete time-translation and provides a rich playground to study a fundamental question in statistical physics: what mechanism allows for driven quantum systems to exhibit emergent behavior that deviates from their counterparts with time-independent evolution? Unlike equilibrium phases, DTCs exhibit macroscopic manifestations of coherent quantum dynamics, challenging the conventional narrative that thermodynamic behavior universally erases quantum signatures. However, due to the difficulty of simulating these systems with either classical or quantum computers, previous studies have been limited to a set of models with Ising-like couplings -- and mostly only in one dimension -- thus precluding our understanding of the existence (or not) of DTCs in models with interactions that closely align with what occurs in nature. In this work, by combining the latest generation of IBM quantum processors with state-of-the-art tensor network methods, we are able to demonstrate the existence of a DTC in a two-dimensional system governed by anisotropic Heisenberg interactions. Our comprehensive analysis reveals a rich phase diagram encompassing spin-glass, ergodic, and time-crystalline phases, highlighting the tunability of these phases through multiple control parameters. Crucially, our results emphasize the interplay of initialization, interaction anisotropy, and driving protocols in stabilizing the DTC phase. By extending the study of Floquet matter beyond simplified models, we lay the groundwork for exploring how driven systems bridge the gap between quantum coherence and emergent non-equilibrium thermodynamics.

High-speed trains are one of the most relevant scenarios for the fifth-generation (5G) mobile communications and the "smart rail mobility" vision, where a high-data-rate wireless connectivity with up to several GHz bandwidths will be required. This is a strong motivation for the exploration of millimeter wave (mmWave) band. In this article, we identify the main challenges and make progress towards realistic 5G mmWave channel models for railway use cases. In order to cope with the challenge of including the railway features in the channel models, we define reference scenarios to help the parameterization of channel models for railway use at mmWave band. Simulations and the subsequent measurements used to validate the model reflect the detailed influence of railway objects and the accuracy of the simulations. Finally, we point out the future directions towards the full version of the smart rail mobility which will be powered by terahertz (THz) communications.

In the design of unmanned aerial vehicle (UAV) wireless communications, a better understanding of propagation characteristics and an accurate channel model are required. Measurements and comprehensive analysis for the UAV-based air-ground (AG) propagation channel in the vertical dimension are presented in this letter. Based on the measurement data at 1 and 4 GHz, the large-scale and small-scale channel parameters are extracted in the line-of-sight (LOS) and nonLOS case, respectively. The altitude-dependent path loss model is proposed herein. Furthermore, shadow fading and fast fading are statistically analyzed for comprehensively describing the fading behavior. Our results will be useful in the modeling of AG channels and the performance analysis for UAV-enabled wireless communication systems.

The Generalized SU(2) Proca (GSU2P) theory has recently garnered attention for its potential to describe key phases of cosmic evolution, including primordial inflation and late-time accelerated expansion. However, its full cosmological implications remain unexplored. In this work, we perform a comprehensive analysis of the dynamical properties of the GSU2P theory in a flat, homogeneous, and isotropic spacetime, through a dynamical-system approach. Our analysis reveals the presence of three pairs of fixed points, one of them corresponding to de-Sitter expansion which may represent either a stable or unstable phase in the evolution of the universe. These points, nonetheless, give rise to an indeterminate or infinite Hubble parameter, which renders them cosmologically unviable. Additionally, we find two key pseudostationary states: the ``attractor lines'', along which the system exhibits constant-roll dynamics, and the ``central zone'', characterized by oscillatory radiation-like behaviour of the field. The dynamics within the central zone could represent a graceful exit from the primordial inflationary phase to a radiation dominated phase, or a state of the dark energy component prior to the late-time cosmic acceleration. However, within the central zone, the dynamics of the vector field leads to recurrent instances of a nonphysical expansion rate. The absence of a limit cycle in the central zone further exacerbates the issue, as the system may follow unbounded phase-space trajectories, and the expansion rate becomes complex once it escapes the region. Collectively, these challenges undermine the viability of the GSU2P theory as a cosmological model for cosmic acceleration., Comment: LaTeX file in RevTeX 4.1 style, 22 pages, 11 figures