Search

Interstellar dust links the formation of the first stars to the rocky planet we inhabit by playing a pivotal role in the cooling and fragmentation of molecular clouds, and catalyzing the formation of water and organic molecules. Despite its central role, the origin of dust and its formation timescale remain unknown. Some models favor rapid production in supernova ejecta as the primary origin of dust, while others invoke slower production by evolved asymptotic giant branch stars or grain growth in the interstellar medium (ISM). The dust content of young early-universe galaxies is highly sensitive to the dust formation timescales. Here, we evaluate the dust content of 631 galaxies at $3 < z_{\rm spec} < 14$ based on rest-UV to optical spectroscopy obtained with JWST NIRSpec. We find that dust appears rapidly. Attenuation immediately follows star formation on timescales shorter than $\sim 30$ Myr, favoring dust production by supernovae. The degree of attenuation is $\sim 30$ times lower than expected if the entire supernova dust yield were preserved in the ISM, and had Milky Way-like grain properties. This can be reconciled if the early-universe dust is composed mostly of silicate or grains much larger than those in the Milky Way, and if significant dust destruction or ejection by outflows takes place., Comment: Submitted!

This paper introduces a general method for the exploration of equivalence classes in the input space of Transformer models. The proposed approach is based on sound mathematical theory which describes the internal layers of a Transformer architecture as sequential deformations of the input manifold. Using eigendecomposition of the pullback of the distance metric defined on the output space through the Jacobian of the model, we are able to reconstruct equivalence classes in the input space and navigate across them. We illustrate how this method can be used as a powerful tool for investigating how a Transformer sees the input space, facilitating local and task-agnostic explainability in Computer Vision and Natural Language Processing tasks., Comment: 11 pages, 4 figures

Neutrinos described as an open quantum system may interact with the environment which introduces stochastic perturbations to their quantum phase. This mechanism leads to a loss of coherence along the propagation of the neutrino $-$ a phenomenon commonly referred to as decoherence $-$ and ultimately, to a modification of the oscillation probabilities. Fluctuations in space-time, as envisaged by various theories of quantum gravity, are a potential candidate for a decoherence-inducing environment. Consequently, the search for decoherence provides a rare opportunity to investigate quantum gravitational effects which are usually beyond the reach of current experiments. In this work, quantum decoherence effects are searched for in neutrino data collected by the KM3NeT/ORCA detector from January 2020 to November 2021. The analysis focuses on atmospheric neutrinos within the energy range of a few GeV to $100\,\mathrm{GeV}$. Adopting the open quantum system framework, decoherence is described in a phenomenological manner with the strength of the effect given by the parameters $\Gamma_{21}$ and $\Gamma_{31}$. Following previous studies, a dependence of the type $\Gamma_{ij} \propto (E/E_0)^n$ on the neutrino energy is assumed and the cases $n = -2,-1$ are explored. No significant deviation with respect to the standard oscillation hypothesis is observed. Therefore, $90\,\%$ CL upper limits are estimated as $\Gamma_{21} < 4.6\cdot 10^{-21}\,$GeV and $\Gamma_{31} < 8.4\cdot 10^{-21}\,$GeV for $n = -2$, and $\Gamma_{21} < 1.9\cdot 10^{-22}\,$GeV and $\Gamma_{31} < 2.7\cdot 10^{-22}\,$GeV for $n = -1$, respectively., Comment: 17 pages, 5 figures

We report the first successful ALMA follow-up observations of a secure $z > 10$ JWST-selected galaxy, by robustly detecting ($6.6\sigma$) the [OIII]$_{88\mu m}\,$ line in JADES-GS-z14-0 (hereafter GS-z14). The ALMA detection yields a spectroscopic redshift of $z=14.1793\pm0.0007$, and increases the precision on the prior redshift measurement of $z=14.32_{-0.20}^{+0.08}$ from NIRSpec by $\gtrsim$180$\times$. Moreover, the redshift is consistent with that previously determined from a tentative detection ($3.6\sigma$) of CIII]$_{1907,1909}$ ($z=14.178\pm0.013$), solidifying the redshift determination via multiple line detections. We measure a line luminosity of $L_\mathrm{[OIII]88} = (2.1 \pm 0.5)\times10^8\,L_\odot$, placing GS-z14 at the lower end, but within the scatter of, the local $L_\mathrm{[OIII]88}$-star formation rate relation. No dust continuum from GS-z14 is detected, suggesting an upper limit on the dust-to-stellar mass ratio of $< 2 \times 10^{-3}$, consistent with dust production from supernovae with a yield $y_d < 0.3\,M_\odot$. Combining a previous JWST/MIRI photometric measurement of the [OIII]$\lambda\lambda$4959,5007$\mathrm{\mathring{A}}$ and H$\beta$ lines with Cloudy models, we find GS-z14 to be surprisingly metal-enriched ($Z\sim0.05 - 0.2\,Z_\odot$) a mere $300\,\mathrm{Myr}$ after the Big Bang. The detection of a bright oxygen line in GS-z14 thus reinforces the notion that galaxies in the early Universe undergo rapid evolution., Comment: 11 Pages, 6 Figures, 1 Table. Submitted to The Astrophysical Journal

The abundance and blue color of super-early (redshift $z>10$), luminous galaxies discovered by JWST can be explained if radiation-driven outflows have ejected their dust on kpc-scales. To test this hypothesis, we predict the ALMA detectability of such extended dust component. Given the observed properties of the galaxy, its observed continuum flux at 88 $\mu$m, $F_{88}$, depends on the dust-to-stellar mass ratio, $\xi_d$, and extent of the dust distribution, $r_d$. Once applied to the most distant galaxy known, GS-z14-0 at $z=14.32$, the fiducial model ($\xi_d = 1/529$) predicts $F_{88}^{\rm fid} = 14.9\, \mu$Jy, and a dust extent $r_d=1.4$ kpc. If the galaxy is very dust-rich ($\xi_d =1/40$), $F_{88}^{\rm max} = 40.1\, \mu$Jy. These values are smaller ($F_{88}^{\rm fid} = 9.5\, \mu$Jy) if the dust is predominantly made of large grains as those formed in SN ejecta. Forthcoming ALMA observations might come very close to constraining the fiducial predictions of the outflow-based attenuation-free model. Other super-early galaxies are predicted to be fainter at 88 $\mu$m, mostly because of their lower SFR compared to GS-z14-0, with fiducial fluxes in the range $2-5.2\ \mu$Jy., Comment: 9 pages, 2 figures, submitted to A&A, comments welcome

Boron Neutron Capture Therapy (BNCT) is an innovative binary form of radiation therapy with high selectivity towards cancer tissue based on the neutron capture reaction 10B(n,$\alpha$)7Li, consisting in the exposition of patients to neutron beams after administration of a boron compound with preferential accumulation in cancer cells. The high linear energy transfer products of the ensuing reaction deposit their energy at cell level, sparing normal tissue. Although progress in accelerator-based BNCT has led to renewed interest in this cancer treatment modality, in vivo dose monitoring during treatment still remains not feasible and several approaches are under investigation. While Compton imaging presents various advantages over other imaging methods, it typically requires long reconstruction times, comparable with BNCT treatment duration. This study aims to develop deep neural network models to estimate the dose distribution by using a simulated dataset of BNCT Compton camera images. The models pursue the avoidance of the iteration time associated with the maximum-likelihood expectation-maximization algorithm (MLEM), enabling a prompt dose reconstruction during the treatment. The U-Net architecture and two variants based on the deep convolutional framelets framework have been used for noise and artifacts reduction in few-iterations reconstructed images, leading to promising results in terms of reconstruction accuracy and processing time., Comment: 16 pages, 12 figures, preprint

Information Operations (IOs) pose a significant threat to the integrity of democratic processes, with the potential to influence election-related online discourse. In anticipation of the 2024 U.S. presidential election, we present a study aimed at uncovering the digital traces of coordinated IOs on $\mathbb{X}$ (formerly Twitter). Using our machine learning framework for detecting online coordination, we analyze a dataset comprising election-related conversations on $\mathbb{X}$ from May 2024. This reveals a network of coordinated inauthentic actors, displaying notable similarities in their link-sharing behaviors. Our analysis shows concerted efforts by these accounts to disseminate misleading, redundant, and biased information across the Web through a coordinated cross-platform information operation: The links shared by this network frequently direct users to other social media platforms or suspicious websites featuring low-quality political content and, in turn, promoting the same $\mathbb{X}$ and YouTube accounts. Members of this network also shared deceptive images generated by AI, accompanied by language attacking political figures and symbolic imagery intended to convey power and dominance. While $\mathbb{X}$ has suspended a subset of these accounts, more than 75% of the coordinated network remains active. Our findings underscore the critical role of developing computational models to scale up the detection of threats on large social media platforms, and emphasize the broader implications of these techniques to detect IOs across the wider Web., Comment: The 2024 Election Integrity Initiative: HUMANS Lab - Working Paper No. 2024.4 - University of Southern California

We present a new method to determine the star formation rate (SFR) density of the Universe at $z \gtrsim 5$ that includes the contribution of dust-obscured star formation. For this purpose, we use a [CII] (158 $\mu$m) selected sample of galaxies serendipitously identified in the fields of known $z\gtrsim 4.5$ objects to characterize the fraction of obscured SFR. The advantage of a [CII] selection is that our sample is SFR-selected, in contrast to a UV-selection that would be biased towards unobscured star formation. We obtain a sample of 23 [CII] emitters near star-forming (SF) galaxies and QSOs -- three of which we identify for the first time -- using previous literature and archival ALMA data. 18 of these serendipitously identified galaxies have sufficiently deep rest-UV data and are used to characterize the obscured fraction of the star formation in galaxies with SFRs $\gtrsim 30\ \text{M}_{\odot} \ \text{yr}^{-1}$. We find that [CII] emitters identified around SF galaxies have $\approx$63\% of their SFR obscured, while [CII] emitters around QSOs have $\approx$93\% of their SFR obscured. By forward modeling existing wide-area UV luminosity function (LF) determinations, we derive the intrinsic UV LF using our characterization of the obscured SFR. Integrating the intrinsic LF to $M_{UV}$ = $-$20 we find that the obscured SFRD contributes to $>3\%$ and $>10\%$ of the total SFRD at $z \sim 5$ and $z \sim 6$ based on our sample of companions galaxies near SFGs and QSOs, respectively. Our results suggest that dust obscuration is not negligible at $z\gtrsim 5$, further underlining the importance of far-IR observations of the $z\gtrsim 5$ Universe., Comment: Accepted for publication in MNRAS. 24 pages, 14 figures, 8 tables (including appendices)

Using a combination of HST, JWST, and ALMA data, we perform spatially resolved spectral energy distributions (SED) fitting of fourteen 4

We present a spatially resolved analysis of four star-forming galaxies at $z = 4.44-5.64$ using data from the JWST PRIMER and ALMA-CRISTAL surveys to probe the stellar and inter-stellar medium properties on the sub-kpc scale. In the $1-5\,\mu{\rm m}$ JWST NIRCam imaging we find that the galaxies are composed of multiple clumps (between $2$ and $\sim 8$) separated by $\simeq 5\,{\rm kpc}$, with comparable morphologies and sizes in the rest-frame UV and optical. Using BAGPIPES to perform pixel-by-pixel SED fitting to the JWST data we show that the SFR ($\simeq 25\,{\rm M}_{\odot}/{\rm yr}$) and stellar mass (${\rm log}_{10}(M_{\star}/{\rm M}_{\odot}) \simeq 9.5$) derived from the resolved analysis are in close ($ \lesssim 0.3\,{\rm dex}$) agreement with those obtained by fitting the integrated photometry. In contrast to studies of lower-mass sources, we thus find a reduced impact of outshining of the older (more massive) stellar populations in these normal $z \simeq 5$ galaxies. Our JWST analysis recovers bluer rest-frame UV slopes ($\beta \simeq -2.1$) and younger ages ($\simeq 100\,{\rm Myr}$) than archival values. We find that the dust continuum from ALMA-CRISTAL seen in two of these galaxies correlates, as expected, with regions of redder rest-frame UV slopes and the SED-derived $A_{\rm V}$, as well as the peak in the stellar mass map. We compute the resolved IRX-$\beta$ relation, showing that the IRX is consistent with the local starburst attenuation curve and further demonstrating the presence of an inhomogeneous dust distribution within the galaxies. A comparison of the CRISTAL sources to those from the FirstLight zoom-in simulation of galaxies with the same $M_{\star}$ and SFR reveals similar age and colour gradients, suggesting that major mergers may be important in the formation of clumpy galaxies at this epoch., Comment: 16 pages, 8 figures, 3 tables, plus 4 page appendix. Submitted to MNRAS

Memristive systems exhibit dynamics that depend on their past states, making them useful as memory units. Due to their functional resemblance to real synapses, memristors can also constitute the building blocks of neural networks for a variety of tasks. Recently, quantum memristor models have been proposed and notably, a photonic quantum memristor (PQM) has been experimentally proven. In this work, we explore and characterize various quantum properties that emerge from this specific model of PQM. Firstly, we find that a single PQM displays memristive dynamics on its quantum coherence. Secondly, we show that a network made of two independent PQMs can manifest memory effects on the dynamics of both entanglement and coherence of correlated photons traveling through the network, regardless of their distance. Additionally, we build and run a circuit-model of the PQM on a real qubit-based quantum computer (IBM-Q), showing that: (i) this system can effectively be used for non-linear quantum computing under specific conditions, and (ii) digital quantum simulations can reproduce the dynamics of a memristive quantum system in a non-Markovian regime., Comment: 9 pages, 7 figures

Generative adversarial network (GAN) is a strong deep learning model that has shown its value in practical applications such as image processing and data enhancement. Here, we propose a quantum topological code decoder based on GAN and we apply it to optimize the fault-tolerant quantum teleportation system. We construct the generator and discriminator networks of GAN, train the network using the eigenvalue dataset of the topological code, and obtain an optimized decoder with high decoding threshold. The decoding experiments at code distances $d=3$ and $d=5$ show that the error correction success rate of this model reaches 99.895\%. In the experiment, the fidelity threshold of this GAN decoder is about $P=0.2108$, which is significantly improved compared with the threshold $P=0.1099$ of the classical decoding model. In addition, the quantum teleportation system, optimized for noise resistance under $d=3$ topological code, shows a noticeable fidelity improvement within the non-polarized noise threshold range of $P<0.06503$, while under $d=5$ topological code optimization, there is a significant fidelity improvement within the non-polarized noise threshold range of $P<0.07512$. The proposed GAN model supplies a novel approach for topological code decoders and its principles can be applied to different kinds of noise processing., Comment: 12 pages, 11 figures

We present a complete analysis of Fermi Large Area Telescope (LAT) data of GRB 221009A, the brightest Gamma-Ray Burst (GRB) ever detected. The burst emission above 30 MeV detected by the LAT preceded by 1 s the low-energy (< 10 MeV) pulse that triggered the Fermi Gamma-Ray Burst Monitor (GBM), as has been observed in other GRBs. The prompt phase of GRB 221009A lasted a few hundred seconds. It was so bright that we identify a Bad Time Interval (BTI) of 64 seconds caused by the extremely high flux of hard X-rays and soft gamma rays, during which the event reconstruction efficiency was poor and the dead time fraction quite high. The late-time emission decayed as a power law, but the extrapolation of the late-time emission during the first 450 seconds suggests that the afterglow started during the prompt emission. We also found that high-energy events observed by the LAT are incompatible with synchrotron origin, and, during the prompt emission, are more likely related to an extra component identified as synchrotron self-Compton (SSC). A remarkable 400 GeV photon, detected by the LAT 33 ks after the GBM trigger and directionally consistent with the location of GRB 221009A, is hard to explain as a product of SSC or TeV electromagnetic cascades, and the process responsible for its origin is uncertain. Because of its proximity and energetic nature, GRB 221009A is an extremely rare event., Comment: 60 pages, 38 figures, 9 tables

Quasi-periodic Eruptions (QPEs) are luminous bursts of soft X-rays from the nuclei of galaxies, repeating on timescales of hours to weeks. The mechanism behind these rare systems is uncertain, but most theories involve accretion disks around supermassive black holes (SMBHs), undergoing instabilities or interacting with a stellar object in a close orbit. It has been suggested that this disk could be created when the SMBH disrupts a passing star, implying that many QPEs should be preceded by observable tidal disruption events (TDEs). Two known QPE sources show long-term decays in quiescent luminosity consistent with TDEs, and two observed TDEs have exhibited X-ray flares consistent with individual eruptions. TDEs and QPEs also occur preferentially in similar galaxies. However, no confirmed repeating QPEs have been associated with a spectroscopically confirmed TDE or an optical TDE observed at peak brightness. Here we report the detection of nine X-ray QPEs with a mean recurrence time of approximately 48 hours from AT2019qiz, a nearby and extensively studied optically-selected TDE. We detect and model the X-ray, ultraviolet and optical emission from the accretion disk, and show that an orbiting body colliding with this disk provides a plausible explanation for the QPEs.

Machine learning's widespread adoption in decision-making processes raises concerns about fairness, particularly regarding the treatment of sensitive features and potential discrimination against minorities. The software engineering community has responded by developing fairness-oriented metrics, empirical studies, and approaches. However, there remains a gap in understanding and categorizing practices for engineering fairness throughout the machine learning lifecycle. This paper presents a novel catalog of practices for addressing fairness in machine learning derived from a systematic mapping study. The study identifies and categorizes 28 practices from existing literature, mapping them onto different stages of the machine learning lifecycle. From this catalog, the authors extract actionable items and implications for both researchers and practitioners in software engineering. This work aims to provide a comprehensive resource for integrating fairness considerations into the development and deployment of machine learning systems, enhancing their reliability, accountability, and credibility.

Pairwise comparisons based on human judgements are an effective method for determining rankings of items or individuals. However, as human biases perpetuate from pairwise comparisons to recovered rankings, they affect algorithmic decision making. In this paper, we introduce the problem of fairness-aware ranking recovery from pairwise comparisons. We propose a group-conditioned accuracy measure which quantifies fairness of rankings recovered from pairwise comparisons. We evaluate the impact of state-of-the-art ranking recovery algorithms and sampling approaches on accuracy and fairness of the recovered rankings, using synthetic and empirical data. Our results show that Fairness-Aware PageRank and GNNRank with FA*IR post-processing effectively mitigate existing biases in pairwise comparisons and improve the overall accuracy of recovered rankings. We highlight limitations and strengths of different approaches, and provide a Python package to facilitate replication and future work on fair ranking recovery from pairwise comparisons., Comment: Appendix and source code are available at https://github.com/wanLo/fairpair

The NANOGrav 15-year data provides compelling evidence for a stochastic gravitational-wave (GW) background at nanohertz frequencies. The simplest model-independent approach to characterizing the frequency spectrum of this signal consists in a simple power-law fit involving two parameters: an amplitude A and a spectral index \gamma. In this paper, we consider the next logical step beyond this minimal spectral model, allowing for a running (i.e., logarithmic frequency dependence) of the spectral index, \gamma_run(f) = \gamma + \beta \ln(f/f_ref). We fit this running-power-law (RPL) model to the NANOGrav 15-year data and perform a Bayesian model comparison with the minimal constant-power-law (CPL) model, which results in a 95% credible interval for the parameter \beta consistent with no running, \beta \in [-0.80,2.96], and an inconclusive Bayes factor, B(RPL vs. CPL) = 0.69 +- 0.01. We thus conclude that, at present, the minimal CPL model still suffices to adequately describe the NANOGrav signal; however, future data sets may well lead to a measurement of nonzero \beta. Finally, we interpret the RPL model as a description of primordial GWs generated during cosmic inflation, which allows us to combine our results with upper limits from big-bang nucleosynthesis, the cosmic microwave background, and LIGO-Virgo-KAGRA., Comment: 17 pages, 4 figures, 2 tables

KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on an initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photo-sensors. A high-purity neutrino sample was extracted, corresponding to an exposure of 433 kton-years. The sample of 5828 neutrino candidates is analysed following a binned log-likelihood method in the reconstructed energy and cosine of the zenith angle. The atmospheric oscillation parameters are measured to be $\sin^2\theta_{23}= 0.51^{+0.04}_{-0.05}$, and $ \Delta m^2_{31} = 2.18^{+0.25}_{-0.35}\times 10^{-3}~\mathrm{eV^2} \cup \{-2.25,-1.76\}\times 10^{-3}~\mathrm{eV^2}$ at 68\% CL. The inverted neutrino mass ordering hypothesis is disfavoured with a p-value of 0.25., Comment: 29 pages, 12 figures

We investigate the spatial extent of the [CII] line emission in a sample of 34 galaxies at $z=4-6$ from the ALMA-CRISTAL Survey. By modeling the [CII] line emission in the visibility data directly, we derive the effective radius of [CII] line emission assuming exponential distribution. These measurements comprise not only isolated galaxies but also interacting systems, identified thanks to the high spatial resolution of the data. The [CII] line radius ranges from 0.5 to 3.5 kpc with an average value of 1.9 kpc. We compare the [CII] sizes with the sizes of UV and FIR continua, which were measured from the HST F160W and ALMA Band-7 continuum images, respectively. We confirm that the [CII] line emission is more spatially extended than the continuum emission, with average size ratios of $R_{e,[CII]}/R_{e,UV}=2.90$ and $R_{e,[CII]}/R_{e,FIR}=1.54$, although about half of the FIR-detected sample show comparable spatial extent between [CII] line and FIR continuum emission ($R_{e,[CII]}\approx R_{e, FIR}$). The residual visibility data of the best-fit model do not show evidence of flux excesses either individually or in stacking analysis. This indicates that the [CII] line emission in star-forming galaxies can be characterized by an extended exponential disk profile. Overall, our results suggest that the spatial extent of [CII] line emission can primarily be explained by photodissociation regions associated with star formation activity, while the contribution from diffuse neutral medium (atomic gas) and the effects of mergers may further expand the [CII] line distributions, causing their variations among our sample. We report the correlations between the [CII] line, dust, and Lya line properties, which may be in line with our scenario. Future 3D-analysis of Lya and Ha lines will shed light on the association of the extended [CII] line emission with atomic gas and outflows., Comment: Submitted to A&A, 21 pages, 14 figures, 4 tables

We present new multi-frequency ALMA continuum observations of the massive [$\log_{10}(M_\star/M_\odot) = 10.3_{-0.2}^{+0.1}$], UV-luminous [$M_\mathrm{UV} = -21.7 \pm 0.2$] $z=7.31$ galaxy REBELS-25 in Bands 3, 4, 5, and 9. Combining the new observations with previously-taken data in Bands 6 and 8, we cover the dust continuum emission of the galaxy in six distinct bands -- spanning rest-frame $50-350\,\mu$m -- enabling simultaneous constraints on its dust mass ($M_\mathrm{dust}$), temperature ($T_\mathrm{dust}$) and emissivity index ($\beta_\mathrm{IR}$) via modified blackbody fitting. Given a fiducial model of optically thin emission, we infer a cold dust temperature of $T_\mathrm{dust} = 32_{-6}^{+9}\,$K and a high dust mass of $\log_{10}(M_\mathrm{dust}/M_\odot) = 8.2_{-0.4}^{+0.6}$, and moderately optically thick dust does not significantly alter these estimates. If we assume dust production is solely through supernovae (SNe), the inferred dust yield would be high, $y = 0.7_{-0.4}^{+2.3}\,M_\odot$ per SN. Consequently, we argue grain growth in the interstellar medium of REBELS-25 also contributes to its dust build-up. This is supported by the steep dust emissivity index $\beta_\mathrm{IR} = 2.5 \pm 0.4$ we measure for REBELS-25, as well as by its high stellar mass, dense interstellar medium, and metal-rich nature. Our results suggest that constraining the dust emissivity indices of high-redshift galaxies is important not only to mitigate systematic uncertainties in their dust masses and obscured star formation rates, but also to assess if dust properties evolve across cosmic time. We present an efficient observing setup to do so with ALMA, combining observations of the peak and Rayleigh-Jeans tail of the dust emission., Comment: 14 pages main text, 6 figures + appendices; re-submitted to MNRAS after a positive referee report

Galaxy evolution emerges from the balance between cosmic gas accretion, fueling star formation, and supernova (SN) feedback, regulating the metal enrichment. Hence, the stellar mass ($M_*$) - gas metallicity relation (MZR) is key to understand the physics of galaxies. High-quality JWST data enable accurate measurements of the MZR up to redshift z=10. Our aims are to understand the observed MZR, its connection with the star formation rate (SFR), the role played by SFR stochasticity, and how it is regulated by SN feedback. We compare the MZR from the JADES, CEERS, and UNCOVER surveys, which comprise about 180 galaxies at $z=3-10$ with $10^6

Pulsar-timing-array experiments have reported evidence for a stochastic background of nanohertz gravitational waves consistent with the signal expected from a population of supermassive--black-hole binaries. Those analyses assume power-law spectra for intrinsic pulsar noise and for the background, as well as a Hellings--Downs cross-correlation pattern among the gravitational-wave--induced residuals across pulsars. These assumptions are idealizations that may not be realized in actuality. We test them in the NANOGrav 15 yr data set using Bayesian posterior predictive checks: after fitting our fiducial model to real data, we generate a population of simulated data-set replications, and use them to assess whether the optimal-statistic significance, inter-pulsar correlations, and spectral coefficients assume extreme values for the real data when compared to the replications. We confirm that the NANOGrav 15 yr data set is consistent with power-law and Hellings--Downs assumptions. We also evaluate the evidence for the stochastic background using posterior-predictive versions of the frequentist optimal statistic and of Bayesian model comparison, and find comparable significance (3.2\ $\sigma$ and 3\ $\sigma$ respectively) to what was previously reported for the standard statistics. We conclude with novel visualizations of the reconstructed gravitational waveforms that enter the residuals for each pulsar. Our analysis strengthens confidence in the identification and characterization of the gravitational-wave background as reported by NANOGrav., Comment: 20 pages, 14 Figures

We present new JWST/NIRSpec IFU observations of the J1000+0234 system at $z=4.54$, the dense core of a galaxy protocluster hosting a massive, dusty star forming galaxy (DSFG) with a low luminosity radio counterpart. The new data reveals two extended, high equivalent width (EW$_0 > 1000$ {\AA}) nebulae at each side of the DSFG disk along its minor axis (namely O3-N and O3-S). On one hand, O3-N's spectrum shows a prominent FWHM $\sim1300$ km s$^{-1}$ broad and blueshifted component, suggesting an outflow origin. On the other hand, O3-S stretches over parsec and has a velocity gradient that spans $800$ km s$^{-1}$ but no evidence of a broad component. Both sources, however, seem to be powered at least partially by an active galactic nucleus (AGN), so we classify them as extended emission-line regions (EELRs). The strongest evidence comes from the detection of the high-ionization [Ne V] $\lambda3427$ line toward O3-N, which paired with the non-detection of hard X-rays implies an obscuring column density above the Compton-thick regime. In O3-S, the [Ne V] line is not detected, but we measure a He II well above the expectation for star formation. We interpret this as O3-S being externally irradiated by the AGN, akin to the famous Hanny's Voorwerp object in the local Universe. In addition, more classical line ratio diagnostics (e.g. [O III]/H$\beta$ vs [N II]/H$\alpha$) put the DSFG itself in the AGN region of the diagrams, and hence the most probable host of the AGN. These results showcase the ability of JWST of unveiling highly obscured AGN at high redshifts., Comment: 5 pages, 4 figures plus 5 appendices (incl. 3 extra figures and one table). Submitted to A&A on July 17th 2024

We present new $\lambda_{\rm rest}=77$ $\mu$m dust continuum observations from the ALMA of HZ10 (CRISTAL-22), a dusty main-sequence galaxy at $z$=5.66 as part of the [CII] Resolved Ism in STar-forming Alma Large program, CRISTAL. The high angular resolution of the ALMA Band 7 and new Band 9 data($\sim{0}''.4$) reveals the complex structure of HZ10, which comprises two main components (HZ10-C and HZ10-W) and a bridge-like dusty emission between them (the Bridge). We model the dust spectral energy distribution (SED) to constrain the physical conditions of the interstellar medium (ISM) and its variations among the different components identified in HZ10. We find that HZ10-W (the more UV-obscured component) has an SED dust temperature of $T_{\rm SED}$$\sim$51.2$\pm13.1$ K; this is $\sim$5 K higher (although still consistent) than that of the central component and previous global estimations for HZ10. Our new ALMA data allow us to reduce by a factor of $\sim$2.3 the uncertainties of global $T_{\rm SED}$ measurements compared to previous studies. Interestingly, HZ10-W shows a lower [CII]/FIR ratio compared to the other two components (although still within the uncertainties), suggesting a harder radiation field destroying polycyclic aromatic hydrocarbon associated with [CII] emission (e.g., active galactic nuclei or young stellar populations). While HZ10-C appears to follow the tight IRX-$\beta_{\rm UV}$ relation seen in local UV-selected starburst galaxies and high-$z$ star-forming galaxies, we find that both HZ10-W and the Bridge depart from this relation and are well described by dust-screen models with holes in front of a hard UV radiation field. This suggests that the UV emission (likely from young stellar populations) is strongly attenuated in the more dusty components of the HZ10 system.

The proliferation of wearable technology enables the generation of vast amounts of sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, the complexity and volume of this data present substantial challenges in data modeling and analysis, which have been tamed with approaches spanning time series modeling to deep learning techniques. The latest frontier in this domain is the adoption of Large Language Models (LLMs), such as GPT-4 and Llama, for data analysis, modeling, understanding, and generation of human behavior through the lens of wearable sensor data. This survey explores current trends and challenges in applying LLMs for sensor-based human activity recognition and behavior modeling. We discuss the nature of wearable sensors data, the capabilities and limitations of LLMs to model them and their integration with traditional machine learning techniques. We also identify key challenges, including data quality, computational requirements, interpretability, and privacy concerns. By examining case studies and successful applications, we highlight the potential of LLMs in enhancing the analysis and interpretation of wearable sensors data. Finally, we propose future directions for research, emphasizing the need for improved preprocessing techniques, more efficient and scalable models, and interdisciplinary collaboration. This survey aims to provide a comprehensive overview of the intersection between wearable sensors data and LLMs, offering insights into the current state and future prospects of this emerging field.

Based on the rate of change of its orbital period, PSR J2043+1711 has a substantial peculiar acceleration of 3.5 $\pm$ 0.8 mm/s/yr, which deviates from the acceleration predicted by equilibrium Milky Way models at a $4\sigma$ level. The magnitude of the peculiar acceleration is too large to be explained by disequilibrium effects of the Milky Way interacting with orbiting dwarf galaxies ($\sim$1 mm/s/yr), and too small to be caused by period variations due to the pulsar being a redback. We identify and examine two plausible causes for the anomalous acceleration: a stellar flyby, and a long-period orbital companion. We identify a main-sequence star in \textit{Gaia} DR3 and Pan-STARRS DR2 with the correct mass, distance, and on-sky position to potentially explain the observed peculiar acceleration. However, the star and the pulsar system have substantially different proper motions, indicating that they are not gravitationally bound. However, it is possible that this is an unrelated star that just happens to be located near J2043+1711 along our line of sight (chance probability of 1.6\%). Therefore, we also constrain possible orbital parameters for a circumbinary companion in a hierarchical triple system with J2043+1711; the changes in the spindown rate of the pulsar are consistent with an outer object that has an orbital period of 80 kyr, a companion mass of 0.3 $M_\odot$ (indicative of a white dwarf or low-mass star), and a semi-major axis of 2000 AU. Continued timing and/or future faint optical observations of J2043+1711 may eventually allow us to differentiate between these scenarios.

This paper documents our release of a large-scale data collection of TikTok posts related to the upcoming 2024 U.S. Presidential Election. Our current data comprises 1.8 million videos published between November 1, 2023, and May 26, 2024. Its exploratory analysis identifies the most common keywords, hashtags, and bigrams in both Spanish and English posts, focusing on the election and the two main Presidential candidates, President Joe Biden and Donald Trump. We utilized the TikTok Research API, incorporating various election-related keywords and hashtags, to capture the full scope of relevant content. To address the limitations of the TikTok Research API, we also employed third-party scrapers to expand our dataset. The dataset is publicly available at https://github.com/gabbypinto/US2024PresElectionTikToks, Comment: The 2024 Election Integrity Initiative

High-significance evidences of the existence of a high-energy diffuse flux of cosmic neutrinos have emerged in the last decade from several observations by the IceCube Collaboration. The ANTARES neutrino telescope took data for 15 years in the Mediterranean Sea, from 2007 to 2022, and collected a high-purity all-flavour neutrino sample. The search for a diffuse cosmic neutrino signal using this dataset is presented in this article. This final analysis did not provide a statistically significant observation of the cosmic diffuse flux. However, this is converted into limits on the properties of the cosmic neutrino spectrum. In particular, given the sensitivity of the ANTARES neutrino telescope between 1 and 50 TeV, constraints on single-power-law hypotheses are derived for the cosmic diffuse flux below 20 TeV, especially for power-law fits of the IceCube data with spectral index softer than 2.8.

We present Atacama Large Millimetre/submillimetre Array (ALMA) observations of the [O$_{\rm III}$] 88 $\mu$m emission of a sample of thirteen galaxies at $z$ = 6 to 7.6 selected as [C$_{\rm II}$]-emitting companion sources of quasars. To disentangle the origins of the luminous Oxygen line in the $z$ > 6 Universe, we looked at emission-line galaxies that are selected through an excellent star-formation tracer [C$_{\rm II}$] with star-formation rates between 9 and 162 M$_{\odot}$/yr. Direct observations reveal [O$_{\rm III}$] emission in just a single galaxy (L$_{\rm [O_{\rm III}]}$/L$_{\rm [C_{\rm II}]}$ = 2.3), and a stacked image shows no [O$_{\rm III}$] detection, providing deep upper limits on the L$_{\rm [O_{\rm III}]}$/L$_{\rm [C_{\rm II}]}$ ratios in the $z > 6$ Universe (L$_{\rm [O_{\rm III}]}$/L$_{\rm [C_{\rm II}]}$ < 1.2 at 3${\sigma}$). While the fidelity of this sample is high, no obvious optical/near-infrared counterpart is seen in the JWST imaging available for four galaxies. Additionally accounting for low-redshift CO emitters, line stacking shows that our sample-wide result remains robust: The enhanced L$_{\rm [O_{\rm III}]}$/L$_{\rm [C_{\rm II}]}$ reported in the first billion years of the Universe is likely due to the selection towards bright, blue Lyman-break galaxies with high surface star-formation rates or young stellar populations. The deep upper limit on the rest-frame 90 $\mu$m continuum emission (< 141 $\mu$Jy at 3${\sigma}$), implies a low average dust temperature (T$_{\rm dust}$ < 30K) and high dust mass (M$_{\rm dust}$ ~ 10$^8$ M$_{\odot}$). As more normal galaxies are explored in the early Universe, synergy between JWST and ALMA is fundamental to further investigate the ISM properties of the a broad range of samples of high-$z$ galaxies., Comment: 20 pages; 13 figures; accepted for publication in MNRAS

In this paper, we propose a method that, given a partial grid map of an indoor environment built by an autonomous mobile robot, estimates the amount of the explored area represented in the map, as well as whether the uncovered part is still worth being explored or not. Our method is based on a deep convolutional neural network trained on data from partially explored environments with annotations derived from the knowledge of the entire map (which is not available when the network is used for inference). We show how such a network can be used to define a stopping criterion to terminate the exploration process when it is no longer adding relevant details about the environment to the map, saving, on average, 40% of the total exploration time with respect to covering all the area of the environment., Comment: Under review at IEEE RAL

Pulsar timing array experiments have recently uncovered evidence for a nanohertz gravitational wave background by precisely timing an ensemble of millisecond pulsars. The next significant milestones for these experiments include characterizing the detected background with greater precision, identifying its source(s), and detecting continuous gravitational waves from individual supermassive black hole binaries. To achieve these objectives, generating accurate and precise times of arrival of pulses from pulsar observations is crucial. Incorrect polarization calibration of the observed pulsar profiles may introduce errors in the measured times of arrival. Further, previous studies (e.g., van Straten 2013; Manchester et al. 2013) have demonstrated that robust polarization calibration of pulsar profiles can reduce noise in the pulsar timing data and improve timing solutions. In this paper, we investigate and compare the impact of different polarization calibration methods on pulsar timing precision using three distinct calibration techniques: the Ideal Feed Assumption (IFA), Measurement Equation Modeling (MEM), and Measurement Equation Template Matching (METM). Three NANOGrav pulsars-PSRs J1643$-$1224, J1744$-$1134, and J1909$-$3744-observed with the 800 MHz and 1.5 GHz receivers at the Green Bank Telescope (GBT) are utilized for our analysis. Our findings reveal that all three calibration methods enhance timing precision compared to scenarios where no polarization calibration is performed. Additionally, among the three calibration methods, the IFA approach generally provides the best results for timing analysis of pulsars observed with the GBT receiver system. We attribute the comparatively poorer performance of the MEM and METM methods to potential instabilities in the reference noise diode coupled to the receiver and temporal variations in the profile of the reference pulsar, respectively., Comment: 21 pages, 6 figures, 1 table, submitted to Astrophysical Journal

In the shadow of the digital revolution, the insidious issue of human trafficking has found new breeding grounds within the realms of social media and online job boards. Previous research efforts have predominantly centered on identifying victims via the analysis of escort advertisements. However, our work shifts the focus towards enabling a proactive approach: pinpointing potential traffickers before they lure their preys through false job opportunities. In this study, we collect and analyze a vast dataset comprising over a quarter million job postings collected from eight relevant regions across the United States, spanning nearly two decades (2006-2024). The job boards we considered are specifically catered towards Chinese-speaking immigrants in the US. We classify the job posts into distinct groups based on the self-reported information of the posting user. Our investigation into the types of advertised opportunities, the modes of preferred contact, and the frequency of postings uncovers the patterns characterizing suspicious ads. Additionally, we highlight how external events such as health emergencies and conflicts appear to strongly correlate with increased volume of suspicious job posts: traffickers are more likely to prey upon vulnerable populations in times of crises. This research underscores the imperative for a deeper dive into how online job boards and communication platforms could be unwitting facilitators of human trafficking. More importantly, it calls for the urgent formulation of targeted strategies to dismantle these digital conduits of exploitation.

Understanding susceptibility to online influence is crucial for mitigating the spread of misinformation and protecting vulnerable audiences. This paper investigates susceptibility to influence within social networks, focusing on the differential effects of influence-driven versus spontaneous behaviors on user content adoption. Our analysis reveals that influence-driven adoption exhibits high homophily, indicating that individuals prone to influence often connect with similarly susceptible peers, thereby reinforcing peer influence dynamics, whereas spontaneous adoption shows significant but lower homophily. Additionally, we extend the Generalized Friendship Paradox to influence-driven behaviors, demonstrating that users' friends are generally more susceptible to influence than the users themselves, de facto establishing the notion of Susceptibility Paradox in online social influence. This pattern does not hold for spontaneous behaviors, where friends exhibit fewer spontaneous adoptions. We find that susceptibility to influence can be predicted using friends' susceptibility alone, while predicting spontaneous adoption requires additional features, such as user metadata. These findings highlight the complex interplay between user engagement and characteristics in spontaneous content adoption. Our results provide new insights into social influence mechanisms and offer implications for designing more effective moderation strategies to protect vulnerable audiences.

Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) pose significant risks, particularly in the realm of online election interference. This paper explores the nefarious applications of GenAI, highlighting their potential to disrupt democratic processes through deepfakes, botnets, targeted misinformation campaigns, and synthetic identities. By examining recent case studies and public incidents, we illustrate how malicious actors exploit these technologies to try influencing voter behavior, spread disinformation, and undermine public trust in electoral systems. The paper also discusses the societal implications of these threats, emphasizing the urgent need for robust mitigation strategies and international cooperation to safeguard democratic integrity., Comment: The 2024 Election Integrity Initiative: HUMANS Lab - Working Paper No. 2024.1

With stunning clarity, JWST has revealed the Universe's first billion years. The scientific community is analyzing a wealth of JWST imaging and spectroscopic data from that era, and is in the process of rewriting the astronomy textbooks. Here, 1.5 years into the JWST science mission, we provide a snapshot of the great progress made towards understanding the initial chapters of our cosmic history. We highlight discoveries and breakthroughs, topics and issues that are not yet understood, and questions that will be addressed in the coming years, as JWST continues its revolutionary observations of the Early Universe. While this compendium is written by a small number of authors, invited to ISSI Bern in March 2024 as part of the 2024 ISSI Breakthrough Workshop, we acknowledge the work of a large community that is advancing our collective understanding of the evolution of the Early Universe., Comment: review article written by the attendees of the 2024 ISSI breakthrough workshop "The first billion year of the Universe", submitted. Comments welcome

We developed a model for the star formation history (SFH) of super-early galaxies and applied it to GS-z14-0, the most distant galaxy known, located at $z=14.32$ (294 million years after the Big Bang). The SFH, starting at $z=26.7$, is complex. Initially ($z>18$), the galaxy experiences feedback-regulated phases that are bursty, relatively faint (reaching $M_{\rm UV}=-18.4$), and unattenuated. When dust shielding allows for a smooth star formation rate (SFR), the galaxy quickly becomes heavily obscured. During this obscured phase, which lasts for approximately 20% of the total star-forming time, 70% of the observed stars are formed. Super-early galaxies in this phase should be detectable by ALMA. Twenty-six million years before observation, as the galaxy becomes super-Eddington, a powerful radiation-driven outflow clears most of the dust and significantly reduces the SFR by a factor of seven, from $100 \to 15\ M_\odot \rm yr^{-1}$. The galaxy transitions into a "blue monster" dominating the bright end of the UV luminosity function. When the outflow ceases due to decreased dust opacity, the galaxy relaxes into a post-starburst phase, in which it is currently observed. Our model accurately reproduces all the observed and inferred properties of the galaxy. The analysis of this extreme system opens exciting opportunities for studying the beginnings of the luminous Universe., Comment: 10 pages, 2 figures. Submitted. Comments welcome

Pulsar timing arrays (PTAs) are designed to detect low-frequency gravitational waves (GWs). GWs induce achromatic signals in PTA data, meaning that the timing delays do not depend on radio-frequency. However, pulse arrival times are also affected by radio-frequency dependent "chromatic" noise from sources such as dispersion measure (DM) and scattering delay variations. Furthermore, the characterization of GW signals may be influenced by the choice of chromatic noise model for each pulsar. To better understand this effect, we assess if and how different chromatic noise models affect achromatic noise properties in each pulsar. The models we compare include existing DM models used by NANOGrav and noise models used for the European PTA Data Release 2 (EPTA DR2). We perform this comparison using a subsample of six pulsars from the NANOGrav 15 yr data set, selecting the same six pulsars as from the EPTA DR2 six-pulsar dataset. We find that the choice of chromatic noise model noticeably affects the achromatic noise properties of several pulsars. This is most dramatic for PSR J1713+0747, where the amplitude of its achromatic red noise lowers from $\log_{10}A_{\text{RN}} = -14.1^{+0.1}_{-0.1}$ to $-14.7^{+0.3}_{-0.5}$, and the spectral index broadens from $\gamma_{\text{RN}} = 2.6^{+0.5}_{-0.4}$ to $\gamma_{\text{RN}} = 3.5^{+1.2}_{-0.9}$. We also compare each pulsar's noise properties with those inferred from the EPTA DR2, using the same models. From the discrepancies, we identify potential areas where the noise models could be improved. These results highlight the potential for custom chromatic noise models to improve PTA sensitivity to GWs.

Position calibration in the deep sea is typically done by means of acoustic multilateration using three or more acoustic emitters installed at known positions. Rather than using hydrophones as receivers that are exposed to the ambient pressure, the sound signals can be coupled to piezo ceramics glued to the inside of existing containers for electronics or measuring instruments of a deep sea infrastructure. The ANTARES neutrino telescope operated from 2006 until 2022 in the Mediterranean Sea at a depth exceeding 2000m. It comprised nearly 900 glass spheres with 432mm diameter and 15mm thickness, equipped with photomultiplier tubes to detect Cherenkov light from tracks of charged elementary particles. In an experimental setup within ANTARES, piezo sensors have been glued to the inside of such - otherwise empty - glass spheres. These sensors recorded signals from acoustic emitters with frequencies from 46545 to 60235Hz. Two waves propagating through the glass sphere are found as a result of the excitation by the waves in the water. These can be qualitatively associated with symmetric and asymmetric Lamb-like waves of zeroth order: a fast (early) one with $v_e \approx 5$mm/$\mu$s and a slow (late) one with $v_\ell \approx 2$mm/$\mu$s. Taking these findings into account improves the accuracy of the position calibration. The results can be transferred to the KM3NeT neutrino telescope, currently under construction at multiple sites in the Mediterranean Sea, for which the concept of piezo sensors glued to the inside of glass spheres has been adapted for monitoring the positions of the photomultiplier tubes., Comment: submitted to "Experimental Astronomy"

We present high resolution ($\sim0.14$" = 710 pc) ALMA [CII] 158$\mu$m and dust continuum follow-up observations of REBELS-25, a [CII]-luminous ($L_{\mathrm{[CII]}}=(1.7\pm0.2)\times 10^9 \mathrm{L_{\odot}}$) galaxy at redshift $z=7.3065\pm0.0001$. These high resolution, high signal-to-noise observations allow us to study the sub-kpc morphology and kinematics of this massive ($M_* = 8^{+4}_{-2} \times 10^9 \mathrm{M_{\odot}}$) star-forming (SFR$_{\mathrm{UV+IR}} = 199^{+101}_{-63} \mathrm{M_{\odot}} \mathrm{yr}^{-1}$) galaxy in the Epoch of Reionisation. By modelling the kinematics with $^{\mathrm{3D}}$BAROLO, we find it has a low velocity dispersion ($\bar{\sigma} = 33 \pm 9$ km s$^{-1}$) and a high ratio of ordered-to-random motion ($V_{\mathrm{rot, ~max}}/\bar{\sigma} = 11 ^{+8}_{-4}$), indicating that REBELS-25 is a dynamically cold disc. Additionally, we find that the [CII] distribution is well fit by a near-exponential disc model, with a S\'ersic index, $n$, of $1.3 \pm 0.2$, and we see tentative evidence of more complex non-axisymmetric structures suggestive of a bar in the [CII] and dust continuum emission. By comparing to other high spatial resolution cold gas kinematic studies, we find that dynamically cold discs seem to be more common in the high redshift Universe than expected based on prevailing galaxy formation theories, which typically predict more turbulent and dispersion-dominated galaxies in the early Universe as an outcome of merger activity, gas accretion and more intense feedback. This higher degree of rotational support seems instead to be consistent with recent cosmological simulations that have highlighted the contrast between cold and warm ionised gas tracers, particularly for massive galaxies. We therefore show that dynamically settled disc galaxies can form as early as 700 Myr after the Big Bang., Comment: Submitted to MNRAS

Despite their effectiveness, current deep learning models face challenges with images coming from different domains with varying appearance and content. We introduce SegCLR, a versatile framework designed to segment volumetric images across different domains, employing supervised and contrastive learning simultaneously to effectively learn from both labeled and unlabeled data. We demonstrate the superior performance of SegCLR through a comprehensive evaluation involving three diverse clinical datasets of retinal fluid segmentation in 3D Optical Coherence Tomography (OCT), various network configurations, and verification across 10 different network initializations. In an unsupervised domain adaptation context, SegCLR achieves results on par with a supervised upper-bound model trained on the intended target domain. Notably, we discover that the segmentation performance of SegCLR framework is marginally impacted by the abundance of unlabeled data from the target domain, thereby we also propose an effective zero-shot domain adaptation extension of SegCLR, eliminating the need for any target domain information. This shows that our proposed addition of contrastive loss in standard supervised training for segmentation leads to superior models, inherently more generalizable to both in- and out-of-domain test data. We additionally propose a pragmatic solution for SegCLR deployment in realistic scenarios with multiple domains containing labeled data. Accordingly, our framework pushes the boundaries of deep-learning based segmentation in multi-domain applications, regardless of data availability - labeled, unlabeled, or nonexistent.

Near- to mid-Infrared observations (from Spitzer and JWST) have revealed a hidden population of galaxies at redshift z=3-6, called optically-dark objects, which are believed to be massive and dusty star-formers. While optically-dark sources are widely recognized as a significant component of the stellar mass function, the history of their stellar mass assembly remains unexplored. However, they are thought to be the progenitors of the more massive early-type galaxies found in present-day groups and clusters. It is thus important to examine the possible connection between dark sources and merging events, in order to understand the environment in which they live. Here, we report our search for close companions in a sample of 19 optically-dark objects identified in the SMACS0723 JWST deep field. They were selected in the NIRCam F444W band and undetected below 2mu. We restrict our analysis to the reddest (i.e. F277W-F444W> 1.3) and brightest (F444W< 26 mag) objects. We have identified an optically-dark source showing a very close companion (<0.5"). The spatially resolved SED fitting procedure indicates that all components lying within 1.5" from the dark source are indeed at z~5.7. Tidal features (leading to a whale shaped morphology) corroborate the hypothesis that the dark source is the most massive (log(M/Msun)>10.3) and dusty (Av~3 at the core) system of an ongoing merger with a mass ratio of ~10. Similar merging systems are identified in the SERRA simulations, allowing us to reconstruct their stellar mass assembly history and predict their molecular gas properties The discovery of mergers within dark galaxies at the end of the Epoch of Reionization underscores the importance of conducting a statistical search for additional candidates in deep NIRCam fields. Such research will aid in understanding the role of merging processes during the obscured phase of stellar mass accumulation., Comment: Accepted for publication in Astronomy & Astrophysics

We present the discovery, with the Neutron Star Interior Composition Explorer (NICER), that SRGA J144459.2-604207 is a 447.9 Hz accreting millisecond X-ray pulsar (AMXP), which underwent a four-week long outburst starting on 2024 February 15. The AMXP resides in a 5.22 hr binary, orbiting a low-mass companion donor with $M_d>0.1M_\odot$. We report on the temporal and spectral properties from NICER observations during the early days of the outburst, from 2024 February 21 through 2024 February 23, during which NICER also detected a type-I X-ray burst that exhibited a plateau lasting ~6 s. The spectra of the persistent emission were well described by an absorbed thermal blackbody and power-law model, with blackbody temperature $kT\approx0.9{\rm\,keV}$ and power-law photon index $\Gamma\approx1.9$. Time-resolved burst spectroscopy confirmed the thermonuclear nature of the burst, where an additional blackbody component reached a maximum temperature of nearly $kT\approx3{\rm\,keV}$ at the peak of the burst. We discuss the nature of the companion as well as the type-I X-ray burst., Comment: 11 pages, 4 figures, 3 tables. Accepted by ApJL

In this work, we present two new methods for Variational Quantum Circuit (VQC) Process Tomography onto $n$ qubits systems: PT_VQC and U-VQSVD. Compared to the state of the art, PT_VQC halves in each run the required amount of qubits for process tomography and decreases the required state initializations from $4^{n}$ to just $2^{n}$, all while ensuring high-fidelity reconstruction of the targeted unitary channel $U$. It is worth noting that, for a fixed reconstruction accuracy, PT_VQC achieves faster convergence per iteration step compared to Quantum Deep Neural Network (QDNN) and tensor network schemes. The novel U-VQSVD algorithm utilizes variational singular value decomposition to extract eigenvectors (up to a global phase) and their associated eigenvalues from an unknown unitary representing a general channel. We assess the performance of U-VQSVD by executing an attack on a non-unitary channel Quantum Physical Unclonable Function (QPUF). U-VQSVD outperforms an uninformed impersonation attack (using randomly generated input states) by a factor of 2 to 5, depending on the qubit dimension. For the two presented methods, we propose a new approach to calculate the complexity of the displayed VQC, based on what we denote as optimal depth.

Social media platforms actively moderate content glorifying harmful behaviors like eating disorders, which include anorexia and bulimia. However, users have adapted to evade moderation by using coded hashtags. Our study investigates the prevalence of moderation evaders on the popular social media platform TikTok and contrasts their use and emotional valence with mainstream hashtags. We notice that moderation evaders and mainstream hashtags appear together, indicating that vulnerable users might inadvertently encounter harmful content even when searching for mainstream terms. Additionally, through an analysis of emotional expressions in video descriptions and comments, we find that mainstream hashtags generally promote positive engagement, while moderation evaders evoke a wider range of emotions, including heightened negativity. These findings provide valuable insights for content creators, platform moderation efforts, and interventions aimed at cultivating a supportive online environment for discussions on mental health and eating disorders., Comment: 10 pages, 5 figures, 2 tables

The Latin language has received attention from the computational linguistics research community, which has built, over the years, several valuable resources, ranging from detailed annotated corpora to sophisticated tools for linguistic analysis. With the recent advent of large language models, researchers have also started developing models capable of generating vector representations of Latin texts. The performances of such models remain behind the ones for modern languages, given the disparity in available data. In this paper, we present the LiMe dataset, a corpus of 325 documents extracted from a series of medieval manuscripts called Libri sententiarum potestatis Mediolani, and thoroughly annotated by experts, in order to be employed for masked language model, as well as supervised natural language processing tasks., Comment: to be published in: LT4HALA@LREC-COLING 2024