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We utilize serendipitous observations from the Asteroid Terrestrial-impact Last Alert System (ATLAS) and the Zwicky Transient Facility (ZTF) in addition to targeted follow-up observations from the Las Cumbres Observatory (LCO) and Liverpool Telescope to analyze the first observed instance of cometary activity by the newly-discovered Jupiter-family comet C/2023 RN3 (ATLAS), whose orbital dynamics place it close to residing on a Centaur-like orbit. Across our 7-month baseline, we observe an epoch of cometary activity commencing in August 2023 with an increase in brightness of >5.4 mag. The lightcurve of 2023 RN3 indicates the presence of continuous cometary activity across our observations, suggesting the onset of a new period of sustained activity. We find no evidence of any outbursts on top of the observed brightening, nor do we find any significant color evolution across our observations. 2023 RN3 is visibly extended in LCO and Liverpool Telescope observations, indicating the presence of a spatially-extended coma. Numerical integration of 2023 RN3's orbit reveals the comet to have recently undergone a slight increase in semimajor axis due to a planetary encounter with Jupiter, however whether this orbital change could trigger 2023 RN3's cometary activity is unclear. Our estimate for the maximum dust production metric of Afrho ~400 cm is consistent with previous measurements for the Jupiter-family comet and Centaur populations., Comment: 20 pages, 9 figures

Diffusion models have emerged as highly effective techniques for inpainting, however, they remain constrained by slow sampling rates. While recent advances have enhanced generation quality, they have also increased sampling time, thereby limiting scalability in real-world applications. We investigate the generative sampling process of diffusion-based inpainting models and observe that these models make minimal use of the input condition during the initial sampling steps. As a result, the sampling trajectory deviates from the data manifold, requiring complex synchronization mechanisms to realign the generation process. To address this, we propose Time-aware Diffusion Paint (TD-Paint), a novel approach that adapts the diffusion process by modeling variable noise levels at the pixel level. This technique allows the model to efficiently use known pixel values from the start, guiding the generation process toward the target manifold. By embedding this information early in the diffusion process, TD-Paint significantly accelerates sampling without compromising image quality. Unlike conventional diffusion-based inpainting models, which require a dedicated architecture or an expensive generation loop, TD-Paint achieves faster sampling times without architectural modifications. Experimental results across three datasets show that TD-Paint outperforms state-of-the-art diffusion models while maintaining lower complexity.

The phase diagram of a novel two-dimensional frustrated Ising model with both anti-ferromagnetic and ferromagnetic couplings is studied using Tensor-Network Renormalization-Group techniques. This model can be seen as two anti-ferromagnetic Ising replicas coupled by non-local spin-spin interactions, designed in such a way that the continuum limit matches that of the still debated J1 -J2 model and induces a marginal critical behavior. Our model has the advantage of having more symmetries than the J1 -J2 model and of allowing a more straightforward implementation of Tensor-Network Renormalization-Group algorithms We demonstrate the existence of two transition lines, featuring both first and second-order regimes. In the latter, the central charge and the critical exponents are shown to be compatible with the Ashkin-Teller universality class. This picture is consistent with that given by Monte Carlo simulations of the J1 -J2 model but not with recent studies with Tensor-Network techniques.

Centaurs are small Solar System objects on chaotic orbits in the giant planet region, forming an evolutionary continuum with the Kuiper belt objects and Jupiter-family comets. Some Centaurs are known to exhibit cometary activity, though unlike comets this activity tends not to correlate with heliocentric distance and the mechanism behind it is currently poorly understood. We utilize serendipitous observations from the Asteroid Terrestrial-impact Last Alert System (ATLAS), Zwicky Transient Facility (ZTF), Panoramic Survey Telescope and Rapid Response System (Pan-STARRS), Dark Energy Survey (DES), and Gaia in addition to targeted follow-up observations from the Las Cumbres Observatory, TRAnsiting Planets and PlanetesImals Small Telescope South (TRAPPIST-South), and Gemini North telescope to analyze an unexpected brightening exhibited by the known active Centaur (2060) Chiron in 2021. This is highly indicative of a cometary outburst. As of 2023 February, Chiron has still not returned to its pre-brightening magnitude. We find Chiron's rotational lightcurve, phase curve effects, and possible high-albedo surface features to be unlikely causes of this observed brightening. We consider the most likely cause to be an epoch of either new or increased cometary activity, though we cannot rule out a possible contribution from Chiron's reported ring system, such as a collision of as-yet unseen satellites shepherding the rings. We find no evidence for coma in our Gemini or TRAPPIST-South observations, though this does not preclude the possibility that Chiron is exhibiting a coma that is too faint for observation or constrained to the immediate vicinity of the nucleus., Comment: 39 pages, 14 figures, 14 tables. Has been accepted for publication in PSJ

Dynamic link prediction is a critical task in the analysis of evolving networks, with applications ranging from recommender systems to economic exchanges. However, the concept of the temporal receptive field, which refers to the temporal context that models use for making predictions, has been largely overlooked and insufficiently analyzed in existing research. In this study, we present a comprehensive analysis of the temporal receptive field in dynamic graph learning. By examining multiple datasets and models, we formalize the role of temporal receptive field and highlight their crucial influence on predictive accuracy. Our results demonstrate that appropriately chosen temporal receptive field can significantly enhance model performance, while for some models, overly large windows may introduce noise and reduce accuracy. We conduct extensive benchmarking to validate our findings, ensuring that all experiments are fully reproducible. Code is available at https://github.com/ykrmm/BenchmarkTW .

Magnetic Resonance Image (MRI) pre-processing is a critical step for neuroimaging analysis. However, the computational cost of MRI pre-processing pipelines is a major bottleneck for large cohort studies and some clinical applications. While High-Performance Computing (HPC) and, more recently, Deep Learning have been adopted to accelerate the computations, these techniques require costly hardware and are not accessible to all researchers. Therefore, it is important to understand the performance bottlenecks of MRI pre-processing pipelines to improve their performance. Using Intel VTune profiler, we characterized the bottlenecks of several commonly used MRI-preprocessing pipelines from the ANTs, FSL, and FreeSurfer toolboxes. We found that few functions contributed to most of the CPU time, and that linear interpolation was the largest contributor. Data access was also a substantial bottleneck. We identified a bug in the ITK library that impacts the performance of ANTs pipeline in single-precision and a potential issue with the OpenMP scaling in FreeSurfer recon-all. Our results provide a reference for future efforts to optimize MRI pre-processing pipelines., Comment: 8 pages, 7 figures, 3 tables, 1 listing

This paper reviews Trustworthy Artificial Intelligence (TAI) and its various definitions. Considering the principles respected in any society, TAI is often characterized by a few attributes, some of which have led to confusion in regulatory or engineering contexts. We argue against using terms such as Responsible or Ethical AI as substitutes for TAI. And to help clarify any confusion, we suggest leaving them behind. Given the subjectivity and complexity inherent in TAI, developing a universal framework is deemed infeasible. Instead, we advocate for approaches centered on addressing key attributes and properties such as fairness, bias, risk, security, explainability, and reliability. We examine the ongoing regulatory landscape, with a focus on initiatives in the EU, China, and the USA. We recognize that differences in AI regulations based on geopolitical and geographical reasons pose an additional challenge for multinational companies. We identify risk as a core factor in AI regulation and TAI. For example, as outlined in the EU-AI Act, organizations must gauge the risk level of their AI products to act accordingly (or risk hefty fines). We compare modalities of TAI implementation and how multiple cross-functional teams are engaged in the overall process. Thus, a brute force approach for enacting TAI renders its efficiency and agility, moot. To address this, we introduce our framework Set-Formalize-Measure-Act (SFMA). Our solution highlights the importance of transforming TAI-aware metrics, drivers of TAI, stakeholders, and business/legal requirements into actual benchmarks or tests. Finally, over-regulation driven by panic of powerful AI models can, in fact, harm TAI too. Based on GitHub user-activity data, in 2023, AI open-source projects rose to top projects by contributor account. Enabling innovation in TAI hinges on the independent contributions of the open-source community., Comment: Updates: Fixed typos. Fixed NIST checkmarks in table 1. Added new subsections: copyright (4.6) and risks on webcrawled datasets (5.2.1). Updated figure 3 to show EU-AI Act passing

The performance of spectral clustering relies on the fluctuations of the entries of the eigenvectors of a similarity matrix, which has been left uncharacterized until now. In this letter, it is shown that the signal $+$ noise structure of a general spike random matrix model is transferred to the eigenvectors of the corresponding Gram kernel matrix and the fluctuations of their entries are Gaussian in the large-dimensional regime. This CLT-like result was the last missing piece to precisely predict the classification performance of spectral clustering. The proposed proof is very general and relies solely on the rotational invariance of the noise. Numerical experiments on synthetic and real data illustrate the universality of this phenomenon.

This work presents a comprehensive understanding of the estimation of a planted low-rank signal from a general spiked tensor model near the computational threshold. Relying on standard tools from the theory of large random matrices, we characterize the large-dimensional spectral behavior of the unfoldings of the data tensor and exhibit relevant signal-to-noise ratios governing the detectability of the principal directions of the signal. These results allow to accurately predict the reconstruction performance of truncated multilinear SVD (MLSVD) in the non-trivial regime. This is particularly important since it serves as an initialization of the higher-order orthogonal iteration (HOOI) scheme, whose convergence to the best low-multilinear-rank approximation depends entirely on its initialization. We give a sufficient condition for the convergence of HOOI and show that the number of iterations before convergence tends to $1$ in the large-dimensional limit.

The critical behavior of a dimer model with an interaction favoring parallel dimers in each plaquette of the square lattice is studied numerically by means of the Corner Transfer Matrix Renormalization Group algorithm. The critical exponents are known to depend on the chemical potential of vacancies, or monomers. At large average density of the latter, the phase transition becomes of first-order. We compute the scaling dimensions of both order parameter and temperature in the second order regime and compare them with the conjecture that the critical behavior is the same as the Ashkin-Teller model on its self-dual critical line.

On 26 September 2022, the Double Asteroid Redirection Test (DART) spacecraft impacted Dimorphos, the satellite of binary near-Earth asteroid (65803) Didymos. This demonstrated the efficacy of a kinetic impactor for planetary defense by changing the orbital period of Dimorphos by 33 minutes (Thomas et al. 2023). Measuring the period change relied heavily on a coordinated campaign of lightcurve photometry designed to detect mutual events (occultations and eclipses) as a direct probe of the satellite's orbital period. A total of 28 telescopes contributed 224 individual lightcurves during the impact apparition from July 2022 to February 2023. We focus here on decomposable lightcurves, i.e. those from which mutual events could be extracted. We describe our process of lightcurve decomposition and use that to release the full data set for future analysis. We leverage these data to place constraints on the post-impact evolution of ejecta. The measured depths of mutual events relative to models showed that the ejecta became optically thin within the first ~1 day after impact, and then faded with a decay time of about 25 days. The bulk magnitude of the system showed that ejecta no longer contributed measurable brightness enhancement after about 20 days post-impact. This bulk photometric behavior was not well represented by an HG photometric model. An HG1G2 model did fit the data well across a wide range of phase angles. Lastly, we note the presence of an ejecta tail through at least March 2023. Its persistence implied ongoing escape of ejecta from the system many months after DART impact., Comment: 52 pages, 5 tables, 9 figures, accepted to PSJ

The impact of the DART spacecraft into Dimorphos, moon of the asteroid Didymos, changed Dimorphos' orbit substantially, largely from the ejection of material. We present results from twelve Earth-based facilities involved in a world-wide campaign to monitor the brightness and morphology of the ejecta in the first 35 days after impact. After an initial brightening of ~1.4 magnitudes, we find consistent dimming rates of 0.11-0.12 magnitudes/day in the first week, and 0.08-0.09 magnitudes/day over the entire study period. The system returned to its pre-impact brightness 24.3-25.3 days after impact through the primary ejecta tail remained. The dimming paused briefly eight days after impact, near in time to the appearance of the second tail. This was likely due to a secondary release of material after re-impact of a boulder released in the initial impact, through movement of the primary ejecta through the aperture likely played a role., Comment: 16 pages, 5 Figures, accepted in the Astrophysical Journal Letters (ApJL) on October 16, 2023

We present upgraded infrastructure for Searches after Gravitational Waves Using ARizona Observatories (SAGUARO) during LIGO, Virgo, and KAGRA's fourth gravitational-wave (GW) observing run (O4). These upgrades implement many of the lessons we learned after a comprehensive analysis of potential electromagnetic counterparts to the GWs discovered during the previous observing run. We have developed a new web-based target and observation manager (TOM) that allows us to coordinate sky surveys, vet potential counterparts, and trigger follow-up observations from one centralized portal. The TOM includes software that aggregates all publicly available information on the light curves and possible host galaxies of targets, allowing us to rule out potential contaminants like active galactic nuclei, variable stars, solar-system objects, and preexisting supernovae, as well as to assess the viability of any plausible counterparts. We have also upgraded our image-subtraction pipeline by assembling deeper reference images and training a new neural network-based real-bogus classifier. These infrastructure upgrades will aid coordination by enabling the prompt reporting of observations, discoveries, and analysis to the GW follow-up community, and put SAGUARO in an advantageous position to discover kilonovae in the remainder of O4 and beyond. Many elements of our open-source software stack have broad utility beyond multimessenger astronomy, and will be particularly relevant in the "big data" era of transient discoveries by the Vera C. Rubin Observatory., Comment: updated to match accepted version

Domain-to-domain translation involves generating a target domain sample given a condition in the source domain. Most existing methods focus on fixed input and output domains, i.e. they only work for specific configurations (i.e. for two domains, either $D_1\rightarrow{}D_2$ or $D_2\rightarrow{}D_1$). This paper proposes Multi-Domain Diffusion (MDD), a conditional diffusion framework for multi-domain translation in a semi-supervised context. Unlike previous methods, MDD does not require defining input and output domains, allowing translation between any partition of domains within a set (such as $(D_1, D_2)\rightarrow{}D_3$, $D_2\rightarrow{}(D_1, D_3)$, $D_3\rightarrow{}D_1$, etc. for 3 domains), without the need to train separate models for each domain configuration. The key idea behind MDD is to leverage the noise formulation of diffusion models by incorporating one noise level per domain, which allows missing domains to be modeled with noise in a natural way. This transforms the training task from a simple reconstruction task to a domain translation task, where the model relies on less noisy domains to reconstruct more noisy domains. We present results on a multi-domain (with more than two domains) synthetic image translation dataset with challenging semantic domain inversion.

This paper investigates the numerical uncertainty of Convolutional Neural Networks (CNNs) inference for structural brain MRI analysis. It applies Random Rounding -- a stochastic arithmetic technique -- to CNN models employed in non-linear registration (SynthMorph) and whole-brain segmentation (FastSurfer), and compares the resulting numerical uncertainty to the one measured in a reference image-processing pipeline (FreeSurfer recon-all). Results obtained on 32 representative subjects show that CNN predictions are substantially more accurate numerically than traditional image-processing results (non-linear registration: 19 vs 13 significant bits on average; whole-brain segmentation: 0.99 vs 0.92 S{\o}rensen-Dice score on average), which suggests a better reproducibility of CNN results across execution environments.

Ensuring the long-term reproducibility of data analyses requires results stability tests to verify that analysis results remain within acceptable variation bounds despite inevitable software updates and hardware evolutions. This paper introduces a numerical variability approach for results stability tests, which determines acceptable variation bounds using random rounding of floating-point calculations. By applying the resulting stability test to \fmriprep, a widely-used neuroimaging tool, we show that the test is sensitive enough to detect subtle updates in image processing methods while remaining specific enough to accept numerical variations within a reference version of the application. This result contributes to enhancing the reliability and reproducibility of data analyses by providing a robust and flexible method for stability testing.

Long-period comet C/2018 F4 (PANSTARRS) was observed to show duplicity of its inner region in 2020 September, suggestive of a splitting event. We here present analyses of our observations of the comet taken from the LOOK project and the University of Hawaii 2.2 m telescope after the discovery of the splitting. The two fragments Components A and B, estimated to be $\sim\!60$ m to 4 km in radius, remained highly similar to each other in terms of brightness, colour, and dust morphology throughout our observing campaign from 2020 September to 2021 December. Our fragmentation model yielded that the two components split at a relative speed of $3.00 \pm 0.18$ m s$^{-1}$ in 2020 late April, implying a specific energy change of $\left(5.3 \pm 2.8 \right) \times 10^3$ J kg$^{-1}$, and that Component B was subjected to a stronger nongravitational acceleration than Component A in both the radial and normal directions of the orbit. The obtained splitting time is broadly consistent with the result from the dust morphology analysis, which further suggested that the dominant dust grains were millimeter-sized and ejected at speed $\sim\!2$ m s$^{-1}$. We postulate that the pre-split nucleus of the comet consisted of two lobes resembling the one of 67P, or that the comet used to be a binary system like main-belt comet 288P. Regardless, we highlight the possibility of using observations of split comets as a feasible manner to study the bilobate shape or binarity fraction of cometary nuclei., Comment: Accepted to AJ for publication

In recent years, Dynamic Graph (DG) representations have been increasingly used for modeling dynamic systems due to their ability to integrate both topological and temporal information in a compact representation. Dynamic graphs allow to efficiently handle applications such as social network prediction, recommender systems, traffic forecasting or electroencephalography analysis, that can not be adressed using standard numeric representations. As a direct consequence of the emergence of dynamic graph representations, dynamic graph learning has emerged as a new machine learning problem, combining challenges from both sequential/temporal data processing and static graph learning. In this research area, Dynamic Graph Neural Network (DGNN) has became the state of the art approach and plethora of models have been proposed in the very recent years. This paper aims at providing a review of problems and models related to dynamic graph learning. The various dynamic graph supervised learning settings are analysed and discussed. We identify the similarities and differences between existing models with respect to the way time information is modeled. Finally, general guidelines for a DGNN designer when faced with a dynamic graph learning problem are provided.

The Double Asteroid Redirection Test (DART) spacecraft successfully performed the first test of a kinetic impactor for asteroid deflection by impacting Dimorphos, the secondary of near-Earth binary asteroid (65803) Didymos, and changing the orbital period of Dimorphos. A change in orbital period of approximately 7 minutes was expected if the incident momentum from the DART spacecraft was directly transferred to the asteroid target in a perfectly inelastic collision, but studies of the probable impact conditions and asteroid properties indicated that a considerable momentum enhancement ($\beta$) was possible. In the years prior to impact, we used lightcurve observations to accurately determine the pre-impact orbit parameters of Dimorphos with respect to Didymos. Here we report the change in the orbital period of Dimorphos as a result of the DART kinetic impact to be -33.0 +/- 1.0 (3$\sigma$) minutes. Using new Earth-based lightcurve and radar observations, two independent approaches determined identical values for the change in the orbital period. This large orbit period change suggests that ejecta contributed a significant amount of momentum to the asteroid beyond what the DART spacecraft carried., Comment: Accepted by Nature

Recent advances in handwritten text recognition enabled to recognize whole documents in an end-to-end way: the Document Attention Network (DAN) recognizes the characters one after the other through an attention-based prediction process until reaching the end of the document. However, this autoregressive process leads to inference that cannot benefit from any parallelization optimization. In this paper, we propose Faster DAN, a two-step strategy to speed up the recognition process at prediction time: the model predicts the first character of each text line in the document, and then completes all the text lines in parallel through multi-target queries and a specific document positional encoding scheme. Faster DAN reaches competitive results compared to standard DAN, while being at least 4 times faster on whole single-page and double-page images of the RIMES 2009, READ 2016 and MAURDOR datasets. Source code and trained model weights are available at https://github.com/FactoDeepLearning/FasterDAN.

The random quantum $q$-state clock and Potts models are studied in 2 and 3 dimensions. The existence of Griffiths phases is tested in the 2D case with $q=6$ by sampling the integrated probability distribution of local susceptibilities of the equivalent McCoy-Wu 3D classical modelswith Monte Carlo simulations. No Griffiths phase is found for the clock model. In contrast, numerical evidences of the existence of Griffiths phases in the random Potts model are given and the Finite Size effects are analyzed. The critical point of the random quantum clock model is then studied by Strong-Disorder Renormalization Group. Despite a chaotic behavior of the Renormalization-Group flow at weak disorder, evidences are given that this critical behavior is governed by the same Infinite-Disorder Fixed Point as the Potts model, independently from the number of states $q$.

Parametric instabilities in interacting systems can lead to the appearance of new structures or patterns. In quantum gases, two-body interactions are responsible for a variety of instabilities that depend on the characteristics of both trapping and interactions. We report on the Floquet engineering of such instabilities, on a Bose-Einstein condensate held in a time-modulated optical lattice. The modulation triggers a destabilization of the condensate into a state exhibiting a density modulation with a new spatial periodicity. This new crystal-like order directly depends on the modulation parameters: the interplay between the Floquet spectrum and interactions generates narrow and adjustable instability regions, leading to the growth, from quantum or thermal fluctuations, of modes with a density modulation non commensurate with the lattice spacing. This study demonstrates the production of metastable exotic states of matter through Floquet engineering, and paves the way for further studies of dissipation in the resulting phase, and of similar phenomena in other geometries., Comment: 12 pages, 9 figures

Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many problems. Originally applied to computer vision tasks, CNNs work well with any data with a spatial relationship, besides images, and have been applied to different fields. However, recent works have highlighted numerical stability challenges in DNNs, which also relates to their known sensitivity to noise injection. These challenges can jeopardise their performance and reliability. This paper investigates DeepGOPlus, a CNN that predicts protein function. DeepGOPlus has achieved state-of-the-art performance and can successfully take advantage and annotate the abounding protein sequences emerging in proteomics. We determine the numerical stability of the model's inference stage by quantifying the numerical uncertainty resulting from perturbations of the underlying floating-point data. In addition, we explore the opportunity to use reduced-precision floating point formats for DeepGOPlus inference, to reduce memory consumption and latency. This is achieved by instrumenting DeepGOPlus' execution using Monte Carlo Arithmetic, a technique that experimentally quantifies floating point operation errors and VPREC, a tool that emulates results with customizable floating point precision formats. Focus is placed on the inference stage as it is the primary deliverable of the DeepGOPlus model, widely applicable across different environments. All in all, our results show that although the DeepGOPlus CNN is very stable numerically, it can only be selectively implemented with lower-precision floating-point formats. We conclude that predictions obtained from the pre-trained DeepGOPlus model are very reliable numerically, and use existing floating-point formats efficiently., Comment: 17 pages, 5 figures, 4 tables with 3 figures, 2 tables in Appendix

In this paper, we investigate the problem of multi-domain translation: given an element $a$ of domain $A$, we would like to generate a corresponding $b$ sample in another domain $B$, and vice versa. Acquiring supervision in multiple domains can be a tedious task, also we propose to learn this translation from one domain to another when supervision is available as a pair $(a,b)\sim A\times B$ and leveraging possible unpaired data when only $a\sim A$ or only $b\sim B$ is available. We introduce a new unified framework called Latent Space Mapping (\model) that exploits the manifold assumption in order to learn, from each domain, a latent space. Unlike existing approaches, we propose to further regularize each latent space using available domains by learning each dependency between pairs of domains. We evaluate our approach in three tasks performing i) synthetic dataset with image translation, ii) real-world task of semantic segmentation for medical images, and iii) real-world task of facial landmark detection.

Massively parallel Fourier transforms are widely used in computational sciences, and specifically in computational fluid dynamics which involves unbounded Poisson problems. In practice the latter is usually the most time-consuming operation due to its inescapable all-to-all communication pattern. The original flups library tackles that issue with an implementation of the distributed Fourier transform tailor-made for successive resolutions of unbounded Poisson problems. However the proposed implementation lacks of flexibility as it only supports cell-centered data layout and features a plain communication strategy. This work extends the library along two directions. First, flups implementation is generalized to support a node-centered data layout. Second, three distinct approaches are provided to handle the communications: one all-to-all, and two non-blocking implementations relying on manual packing and MPI_Datatype to communicate over the network. The proposed software is validated against analytical solutions for unbounded, semi-unbounded, and periodic domains. The performance of the approaches is then compared against accFFT, another distributed FFT implementation, using a periodic case. Finally the performance metrics of each implementation are analyzed and detailed on various top-tier European facilities up to 49,152 cores. This work brings flups up to a fully production-ready and performant distributed FFT library, featuring all the possible types of FFTs and with flexibility in the data-layout. The code is available under a BSD-3 license at github.com/vortexlab-uclouvain/flups., Comment: includes updated results due to a typo in [1], B.10

The majority-voter model is studied by Monte Carlo simulations on hypercubic lattices of dimension $d=2$ to 7 with periodic boundary conditions. The critical exponents associated to the Finite-Size Scaling of the magnetic susceptibility are shown to be compatible with those of the Ising model. At dimension $d=4$, the numerical data are compatible with the presence of multiplicative logarithmic corrections. For $d\ge 5$,the estimates of the exponents are close to the prediction $d/2$ when taking into account the dangerous irrelevant variable at theGaussian fixed point. Moreover, the universal values of the Binder cumulant are also compatible with those of the Ising model. This indicates that the upper critical dimension of the majority-voter model is not $d_c=6$ as claimed in the literature, but $d_c=4$ like the equilibrium Ising model., Comment: To be published in Condensed Matter Physics

When modeling multivariate phenomena, properly capturing the joint extremal behavior is often one of the many concerns. Archimax copulas appear as successful candidates in case of asymptotic dependence. In this paper, the class of Archimax copulas is extended via their stochastic representation to a clustered construction. These clustered Archimax copulas are characterized by a partition of the random variables into groups linked by a radial copula; each cluster is Archimax and therefore defined by its own Archimedean generator and stable tail dependence function. The proposed extension allows for both asymptotic dependence and independence between the clusters, a property which is sought, for example, in applications in environmental sciences and finance. The model also inherits from the ability of Archimax copulas to capture dependence between variables at pre-extreme levels. The asymptotic behavior of the model is established, leading to a rich class of stable tail dependence functions., Comment: 42 pages, 10 figures

The surfaces of airless bodies such as asteroids are exposed to many phenomena that can alter their physical properties. Bennu, the target of the OSIRIS-REx mission, has demonstrated how complex the surface of a small body can be. In 2019 November, the potentially hazardous asteroid 2015 JD1 experienced a close approach of 0.0331 au from the Earth. We present results of the physical characterization of 2015 JD1 based on ground-based radar, spectroscopy, and photometric observations acquired during 2019 November. Radar polarimetry measurements from the Arecibo Observatory indicate a morphologically complex surface. The delay-Doppler images reveal a contact-binary asteroid with an estimated visible extent of ~150 m. Our observations suggest that 2015 JD1 is an E-type asteroid with a surface composition similar to aubrites, a class of differentiated enstatite meteorites. The dynamical properties of 2015 JD1 suggest it came from the $\nu_6$ resonance with Jupiter, and spectral comparison with major E-type bodies suggest that it may have been derived from a parental body similar to the progenitor of the E-type (64) Angelina. Significantly, we find rotational spectral variation across the surface of 2015 JD1 from red to blue spectral slope. Our compositional analysis suggests that the spectral slope variation could be due to the lack of iron and sulfides in one area of the 2015 JD1 and/or differences in grain sizes., Comment: 20 pages, 14 figures, 3 tables; accepted for publication in Planetary Science Journal

Cometary activity may be driven by ices with very low sublimation temperatures, such as carbon monoxide ice, which can sublimate at distances well beyond 20 au. This point is emphasized by the discovery of Oort cloud comet C/2014 UN$_{271}$ (Bernardinelli-Bernstein), and its observed activity out to $\sim$26 au. Through observations of this comet's optical brightness and behavior, we can potentially discern the drivers of activity in the outer solar system. We present a study of the activity of comet Bernardinelli-Bernstein with broad-band optical photometry taken at 19-20 au from the Sun (2021 June to 2022 February) as part of the LCO Outbursting Objects Key (LOOK) Project. Our analysis shows that the comet's optical brightness during this period was initially dominated by cometary outbursts, stochastic events that ejected $\sim10^7$ to $\sim10^8$ kg of material on short (< 1 day) timescales. We present evidence for three such outbursts occurring in 2021 June and September. The nominal nuclear volumes excavated by these events are similar to the 10-100 m pit-shaped voids on the surfaces of short-period comet nuclei, as imaged by spacecraft. Two out of three Oort cloud comets observed at large pre-perihelion distances exhibit outburst behavior near 20 au, suggesting such events may be common in this population. In addition, quiescent CO-driven activity may account for the brightness of the comet in 2022 January to February, but that variations in the cometary active area (i.e., the amount of sublimating ice) with heliocentric distance are also possible., Comment: Accepted for publication in The Astrophysical Journal Letters 13 pages, 6 figures, 1 table

The LCO Outbursting Objects Key (LOOK) Project uses the telescopes of the Las Cumbres Observatory (LCO) Network to: (1) to systematically monitor a sample of Dynamically New Comets over the whole sky, and (2) use alerts from existing sky surveys to rapidly respond to and characterize detected outburst activity in all small bodies. The data gathered on outbursts helps to characterize each outburst's evolution with time, assess the frequency and magnitude distribution of outbursts in general, and contributes to the understanding of outburst processes and volatile distribution in the Solar System. The LOOK Project exploits the synergy between current and future wide-field surveys such as ZTF, PanSTARRS, and LSST as well as rapid-response telescope networks such as LCO, and serves as an excellent testbed for what will be needed the much larger number of objects coming from Rubin Observatory. We will describe the LOOK Project goals, the planning and target selection (including the use of NEOexchange as a Target and Observation Manager or "TOM"), and results from the first phase of observations, including the detection of activity and outbursts on the giant comet C/2014 UN271 (Bernardinelli-Bernstein) and the discovery and follow-up of outbursts on comets. Within these outburst discoveries, we present a high cadence of 7P/Pons-Winnecke with days, a large outburst on 57P/duToit-Neujmin-Delporte, and evidence that comet P/2020 X1 (ATLAS) was in outburst when discovered., Comment: 35 pages, 11 figures, accepted for publication in PSJ

Context. The close approach of the near-Earth asteroid (99942) Apophis to Earth in 2029 will provide a unique opportunity to examine how the physical properties of the asteroid could be changed due to the Earth's gravitational perturbation. As a result, the Republic of Korea is planning a rendezvous mission to Apophis. Aims. Our aim was to use photometric data from the apparitions in 2020-2021 to refine the shape model and spin state of Apophis. Methods. Using thirty-six 1 to 2-m class ground-based telescopes and the Transiting Exoplanet Survey Satellite, we performed a photometric observation campaign throughout the 2020-2021 apparition. The convex shape model and spin state were refined using the light-curve inversion method. Results. According to our best-fit model, Apophis is rotating in a short axis mode with rotation and precession periods of 264.178 hours and 27.38547 hours, respectively. The angular momentum vector orientation of Apophis was found as (275$^\circ$, -85$^\circ$) in the ecliptic coordinate system. The ratio of the dynamic moments of inertia of this asteroid was fitted to $I_a:I_b:I_c=0.64:0.97:1$, which corresponds to an elongated prolate ellipsoid. These findings regarding the spin state and shape model could be used to not only design the space mission scenario but also investigate the impact of the Earth's tidal force during close encounters., Comment: 14 pages, 5 figures; Accepted for publication on Astronomy & Astrophysics

Unconstrained handwritten text recognition is a challenging computer vision task. It is traditionally handled by a two-step approach, combining line segmentation followed by text line recognition. For the first time, we propose an end-to-end segmentation-free architecture for the task of handwritten document recognition: the Document Attention Network. In addition to text recognition, the model is trained to label text parts using begin and end tags in an XML-like fashion. This model is made up of an FCN encoder for feature extraction and a stack of transformer decoder layers for a recurrent token-by-token prediction process. It takes whole text documents as input and sequentially outputs characters, as well as logical layout tokens. Contrary to the existing segmentation-based approaches, the model is trained without using any segmentation label. We achieve competitive results on the READ 2016 dataset at page level, as well as double-page level with a CER of 3.43% and 3.70%, respectively. We also provide results for the RIMES 2009 dataset at page level, reaching 4.54% of CER. We provide all source code and pre-trained model weights at https://github.com/FactoDeepLearning/DAN.