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Accurate and flexible world models are crucial for autonomous systems to understand their environment and predict future events. Object-centric models, with structured latent spaces, have shown promise in modeling object dynamics and interactions, but often face challenges in scaling to complex datasets and incorporating external guidance, limiting their applicability in robotics. To address these limitations, we propose TextOCVP, an object-centric model for image-to-video generation guided by textual descriptions. TextOCVP parses an observed scene into object representations, called slots, and utilizes a text-conditioned transformer predictor to forecast future object states and video frames. Our approach jointly models object dynamics and interactions while incorporating textual guidance, thus leading to accurate and controllable predictions. Our method's structured latent space offers enhanced control over the prediction process, outperforming several image-to-video generative baselines. Additionally, we demonstrate that structured object-centric representations provide superior controllability and interpretability, facilitating the modeling of object dynamics and enabling more precise and understandable predictions. Videos and code are available at https://play-slot.github.io/TextOCVP/.

Predicting future scene representations is a crucial task for enabling robots to understand and interact with the environment. However, most existing methods rely on video sequences and simulations with precise action annotations, limiting their ability to leverage the large amount of available unlabeled video data. To address this challenge, we propose PlaySlot, an object-centric video prediction model that infers object representations and latent actions from unlabeled video sequences. It then uses these representations to forecast future object states and video frames. PlaySlot allows to generate multiple possible futures conditioned on latent actions, which can be inferred from video dynamics, provided by a user, or generated by a learned action policy, thus enabling versatile and interpretable world modeling. Our results show that PlaySlot outperforms both stochastic and object-centric baselines for video prediction across different environments. Furthermore, we show that our inferred latent actions can be used to learn robot behaviors sample-efficiently from unlabeled video demonstrations. Videos and code are available at https://play-slot.github.io/PlaySlot/.

This work revisits optimal response-adaptive designs from a type-I error rate perspective, highlighting when and how much these allocations exacerbate type-I error rate inflation - an issue previously undocumented. We explore a range of approaches from the literature that can be applied to reduce type-I error rate inflation. However, we found that all of these approaches fail to give a robust solution to the problem. To address this, we derive two optimal proportions, incorporating the more robust score test (instead of the Wald test) with finite sample estimators (instead of the unknown true values) in the formulation of the optimization problem. One proportion optimizes statistical power and the other minimizes the total number failures in a trail while maintaining a predefined power level. Through simulations based on an early-phase and a confirmatory trial we provide crucial practical insight into how these new optimal proportion designs can offer substantial patient outcomes advantages while controlling type-I error rate. While we focused on binary outcomes, the framework offers valuable insights that naturally extend to other outcome types, multi-armed trials and alternative measures of interest., Comment: 14 pages, 3 figures, 5 tables

We report ALMA and ACA observations of atomic carbon ([\ion{C}{1}]$(1-0)$) and dust continuum in 10 Enormous Ly$\alpha$ Nebulae hosting ultra-luminous Type-I QSOs at $z=2.2-2.5$, as part of the SUrvey of Protocluster ELANe Revealing CO/CI in the Ly$\alpha$ Detected CGM (SUPERCOLD-CGM). We detect [\ion{C}{1}]$(1-0)$ and dust in all ten QSOs and five companion galaxies. We find that the QSOs and companions have higher gas densities and more intense radiation fields than Luminous Infrared galaxies and high-$z$ main sequence galaxies, with the highest values found in the QSOs. By comparing molecular gas masses derived from [\ion{C}{1}]$(1-0)$, CO(4$-$3) and dust continuum, we find that the QSOs and companions display a similar low CO conversion factor of $\alpha_{\rm CO}$\,$\sim$\,0.8 $\rm M_{\sun}$${[\rm K\,km/s\,pc^2]}^{-1}$. After tapering our data to low resolution, the [\ion{C}{1}]$(1-0)$ flux increases for nine QSOs, hinting at the possibility of [\ion{C}{1}]$(1-0)$ in the circum-galactic medium (CGM) on a scale of 16$-$40 kpc. However, the [\ion{C}{1}]$(1-0)$ sensitivity is too low to confirm this for individual targets, except for a tentative (2.7$\sigma$) CGM detection in Q0050+0051{} with M$_{\rm H_2}$\,=\, ($1.0 - 2.8$)$\times 10^{10}$ $\rm M_{\sun}$. The 3$\sigma$ mass limits of molecular CGM for the remaining QSO fields are ($0.2-1.4$)\,$\times$\,10$^{10}$ $\rm M_{\sun}$. This translates into a baryon fraction of $<$0.4-3$\% $ in the molecular CGM relative to the total baryonic halo mass. Our sample also includes a radio-detected AGN, Q1416+2649{}, which shows [\ion{C}{1}]$(1-0)$ and CO(4$-$3) luminosities an order of magnitude fainter for its far-infrared luminosity than other QSOs in our sample, possibly due to a lower molecular gas mass., Comment: 27 pages, 9 figures

Astrophysical observations of the cosmos allow us to probe extreme physics and answer foundational questions on our universe. Modern astronomy is increasingly operating under a holistic approach, probing the same question with multiple diagnostics including how sources vary over time, how they appear across the electromagnetic spectrum, and through their other signatures, including gravitational waves, neutrinos, cosmic rays, and dust on Earth. Astrophysical observations are now reaching the point where approximate physics models are insufficient. Key sources of interest are explosive transients, whose understanding requires multidisciplinary studies at the intersection of astrophysics, gravity, nuclear science, plasma physics, fluid dynamics and turbulence, computation, particle physics, atomic, molecular, and optical science, condensed matter and materials science, radiation transport, and high energy density physics. This white paper provides an overview of the major scientific advances that lay at the intersection of physics and astronomy and are best probed through time-domain and multimessenger astrophysics, an exploration of how multidisciplinary science can be fostered, and introductory descriptions of the relevant scientific disciplines and key astrophysical sources of interest., Comment: This white paper is the product of the Third Time-Domain And Multimessenger Astrophysics workshop: Multidisciplinary Science in the Multimessenger Era, hosted by Louisiana State University with additional support from DOE, NSF, and NASA

X-ray observing facilities, such as the Chandra X-ray Observatory and the eROSITA, have detected millions of astronomical sources associated with high-energy phenomena. The arrival of photons as a function of time follows a Poisson process and can vary by orders-of-magnitude, presenting obstacles for common tasks such as source classification, physical property derivation, and anomaly detection. Previous work has either failed to directly capture the Poisson nature of the data or only focuses on Poisson rate function reconstruction. In this work, we present Poisson Process AutoDecoder (PPAD). PPAD is a neural field decoder that maps fixed-length latent features to continuous Poisson rate functions across energy band and time via unsupervised learning. PPAD reconstructs the rate function and yields a representation at the same time. We demonstrate the efficacy of PPAD via reconstruction, regression, classification and anomaly detection experiments using the Chandra Source Catalog., Comment: 13 pages, 5 figures

Photosensitivity refers to a neurophysiological condition in which the brain generates epileptic discharges known as Photoparoxysmal Responses (PPR) in response to light flashes.In severe cases, these PPR can lead to epileptic seizures. The standardized diagnostic procedure for this condition is called Intermittent Photic Stimulation. During this procedure, the patient is exposed to a flashing light, aiming to trigger these epileptic reactions while preventing their full development. Meanwhile, brain activity is monitored using Electroencephalography, which is visually analyzed by clinical staff to identify these responses. Hence, the automatic detection of PPR becomes a highly unbalanced problem that has been barely studied in the literature due to photosensitivity's low prevalence. This research tackles this problem and proposes using Inception-based Deep Learning (DL) neural networks that, together with transfer learning, are trained in epilepsy seizure detection and tuned in the PPR automatic detection task. A Data Augmentation (DA) technique is also applied to balance the available data set, evaluating its effects on the DL models. The proposal outperformed state-of-the-art solutions in the literature, achieving higher ratios on standard performance metrics, and with DA significantly improving the Sensitivity without affecting Accuracy and Specificity. This project is currently being developed with patients from Burgos University Hospital, Spain

Being immersed in the NISQ-era, current quantum annealers present limitations for solving optimization problems efficiently. To mitigate these limitations, D-Wave Systems developed a mechanism called Reverse Annealing, a specific type of quantum annealing designed to perform local refinement of good states found elsewhere. Despite the research activity around Reverse Annealing, none has theorized about the possible benefits related to the transfer of knowledge under this paradigm. This work moves in that direction and is driven by experimentation focused on answering two key research questions: i) is reverse annealing a paradigm that can benefit from knowledge transfer between similar problems? and ii) can we infer the characteristics that an input solution should meet to help increase the probability of success? To properly guide the tests in this paper, the well-known Knapsack Problem has been chosen for benchmarking purposes, using a total of 34 instances composed of 14 and 16 items., Comment: 13 pages, 2 figures and 2 tables. Paper submitted to Frontiers in Physics journal

The field of quantum computing is generating significant anticipation within the scientific and industrial communities due to its potential to revolutionize computing paradigms. Recognizing this potential, this paper explores the integration of transfer of knowledge techniques, traditionally used in classical artificial intelligence, into quantum computing. We present a comprehensive classification of the transfer models, focusing on Transfer Learning and Transfer Optimization. Additionally, we analyze relevant schemes in quantum computing that can benefit from knowledge sharing, and we delve into the potential synergies, supported by theoretical insights and initial experimental results. Our findings suggest that leveraging the transfer of knowledge can enhance the efficiency and effectiveness of quantum algorithms, particularly in the context of hybrid solvers. This approach not only accelerates the optimization process but also reduces the computational burden on quantum processors, making it a valuable tool for advancing quantum computing technologies., Comment: 12 pages, 8 figures, 4 tables. Paper submitted for its review in Expert Systems journal

Gerrymandering is a pervasive problem within the US political system. In the past decade, methods based on Markov Chain Monte Carlo (MCMC) sampling and statistical outlier tests have been proposed to quantify gerrymandering and were used as evidence in several high-profile legal cases. We perform an empirical power analysis of one such hypothesis test from Chikina et al (2020). We generate a family of biased North Carolina congressional district maps using the 2012 and 2016 presidential elections and assess under which conditions the outlier test fails to flag them at the specified Type I error level. The power of the outlier test is found to be relatively stable across political parties, election years, lengths of the MCMC chain and effect sizes. The main effect on the power of the test is shown to be the choice of the bias metric. This is the first work that computationally verifies the power of statistical tests used in gerrymandering cases.

Spatially localised stationary patterns of arbitrary wide spatial extent emerge from subcritical Turing bifurcations in one-dimensional reaction-diffusion systems. They lie on characteristic bifurcation curves that oscillate around a Maxwell point in a homoclinic snaking phenomenon. Here, a generalisation of the exponential asymptotics method by Chapman \& Kozyreff is developed to provide leading-order expressions for the width of the snaking region close to a Turing bifurcation's super/sub-critical transition in arbitrary $n$-component reaction-diffusion systems. First, general expressions are provided for the regular asymptotic approximation of the Maxwell point, which depends algebraically on the parametric distance from the codimension-two super/sub-critical Turing bifurcation. Then, expressions are derived for the width of the snaking, which is exponentially small in the same distance. The general theory is developed using a vectorised form of regular and exponential asymptotic expansions for localised patterns, which are matched at a Stokes line. Detailed calculations for a particular example are algebraically cumbersome, depend sensitively on the form of the reaction kinetics, and rely on the summation of a weakly convergent series obtained via optimal truncation. Nevertheless, the process can be automated, and code is provided that carries out the calculations automatically, only requiring the user to input the model, and the values of parameters at a codimension-two bifurcation. The general theory also applies to higher-order equations, including those that can be recast as a reaction-diffusion system. The theory is illustrated by comparing numerical computations of localised patterns in two versions of the Swift-Hohenberg equation with different nonlinearities, and versions of activator-inhibitor reaction-diffusion systems., Comment: 102 pages, 13 figures

GRB 221009A is one of the brightest transients ever observed with the highest peak gamma-ray flux for a gamma-ray burst (GRB). A type Ic-BL supernova (SN), SN 2022xiw, was definitively detected in late-time JWST spectroscopy (t = 195 days, observer-frame). However, photometric studies have found SN 2022xiw to be less luminous (10-70%) than the canonical GRB-SN, SN 1998bw. We present late-time Hubble Space Telescope (HST)/WFC3 and JWST/NIRCam imaging of the afterglow and host galaxy of GRB 221009A at t ~ 185, 277, and 345 days post-trigger. Our joint archival ground, HST, and JWST light curve fits show strong support for a break in the light curve decay slope at t = 50 +/- 10 days (observer-frame) and a supernova at $1.4^{+0.37}_{-0.40} \times$ the optical/NIR flux of SN 1998bw. This break is consistent with an interpretation as a jet break when requiring slow-cooling electrons in a wind medium with the electron energy spectral index, p > 2, and $\nu_m < \nu_c$. Our light curve and joint HST/JWST spectral energy distribution (SED) also show evidence for the late-time emergence of a bluer component in addition to the fading afterglow and supernova. We find consistency with the interpretations that this source is either a young, massive, low-metallicity star cluster or a scattered light echo of the afterglow with a SED shape of $f_{\nu} \propto \nu^{2.0\pm1.0}$., Comment: 18 pages, 7 figures. Submitted to ApJ

To implement a Bayesian response-adaptive trial it is necessary to evaluate a sequence of posterior probabilities. This sequence is often approximated by simulation due to the unavailability of closed-form formulae to compute it exactly. Approximating these probabilities by simulation can be computationally expensive and impact the accuracy or the range of scenarios that may be explored. An alternative approximation method based on Gaussian distributions can be faster but its accuracy is not guaranteed. The literature lacks practical recommendations for selecting approximation methods and comparing their properties, particularly considering trade-offs between computational speed and accuracy. In this paper, we focus on the case where the trial has a binary endpoint with Beta priors. We first outline an efficient way to compute the posterior probabilities exactly for any number of treatment arms. Then, using exact probability computations, we show how to benchmark calculation methods based on considerations of computational speed, patient benefit, and inferential accuracy. This is done through a range of simulations in the two-armed case, as well as an analysis of the three-armed Established Status Epilepticus Treatment Trial. Finally, we provide practical guidance for which calculation method is most appropriate in different settings, and how to choose the number of simulations if the simulation-based approximation method is used., Comment: 16 pages, 3 figures, 3 tables

The detailed feeding and feedback mechanisms of Active Galactic Nuclei (AGN) are not yet well known. For low-luminosity and obscured AGN, as well as late-type galaxies, determining the central black hole (BH) masses is challenging. Our goal with the GATOS sample is to study circum-nuclear regions and better estimate BH masses with more precision than scaling relations offer. Using ALMA's high spatial resolution, we resolve CO(3-2) emissions within ~100 pc around the supermassive black hole (SMBH) in seven GATOS galaxies to estimate their BH masses when sufficient gas is present. We study seven bright ($L_{AGN}(14-150\mathrm{keV}) \geq 10^{42}\mathrm{erg/s}$), nearby (<28 Mpc) galaxies from the GATOS core sample. For comparison, we searched the literature for previous BH mass estimates and made additional calculations using the \mbh~ - $\sigma$ relation and the fundamental plane of BH activity. We developed a supervised machine learning method to estimate BH masses from position-velocity diagrams or first-moment maps using ALMA CO(3-2) observations. Numerical simulations with a wide range of parameters created the training, validation, and test sets. Seven galaxies provided enough gas for BH mass estimations: NGC4388, NGC5506, NGC5643, NGC6300, NGC7314, NGC7465, and NGC~7582. Our BH masses, ranging from 6.39 to 7.18 log$(M_{BH}/M_\odot)$, align with previous estimates. Additionally, our machine learning method provides robust error estimations with confidence intervals and offers greater potential than scaling relations. This work is a first step toward an automated \mbh estimation method using machine learning.

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

Graph neural networks (GNNs) provide state-of-the-art results in a wide variety of tasks which typically involve predicting features at the vertices of a graph. They are built from layers of graph convolutions which serve as a powerful inductive bias for describing the flow of information among the vertices. Often, more than one data modality is available. This work considers a setting in which several graphs have the same vertex set and a common vertex-level learning task. This generalizes standard GNN models to GNNs with several graph operators that do not commute. We may call this model graph-tuple neural networks (GtNN). In this work, we develop the mathematical theory to address the stability and transferability of GtNNs using properties of non-commuting non-expansive operators. We develop a limit theory of graphon-tuple neural networks and use it to prove a universal transferability theorem that guarantees that all graph-tuple neural networks are transferable on convergent graph-tuple sequences. In particular, there is no non-transferable energy under the convergence we consider here. Our theoretical results extend well-known transferability theorems for GNNs to the case of several simultaneous graphs (GtNNs) and provide a strict improvement on what is currently known even in the GNN case. We illustrate our theoretical results with simple experiments on synthetic and real-world data. To this end, we derive a training procedure that provably enforces the stability of the resulting model., Comment: NeurIPS 2024

In the asymptotic limit of a large diffusivity ratio, certain two-component reaction-diffusion (RD) systems can admit localized spike solutions on a 1-D finite domain in a far-from-equilibrium nonlinear regime. It is known that two distinct bifurcation mechanisms can occur which generate spike patterns of increased spatial complexity as the domain half-length L slowly increases; so-called spike nucleation and spike self-replication. Self-replication is found to occur via the passage beyond a saddle-node bifurcation point that can be predicted through linearization around the inner spike profile. In contrast, spike nucleation occurs through slow passage beyond the saddle-node of a nonlinear boundary-value problem defined in the outer region away from the core of a spike. Here, by treating L as a static parameter, precise conditions are established within the semi-strong interaction asymptotic regime to determine which occurs, conditions that are confirmed by numerical simulation and continuation. For the Schnakenberg and Brusselator RD models, phase diagrams in parameter space are derived that predict whether spike self-replication or spike nucleation will occur first as L is increased, or whether no such instability will occur. For the Gierer-Meinhardt model with a non-trivial activator background, spike nucleation is shown to be the only possible spike-generating mechanism. From time-dependent PDE numerical results on an exponentially slowly growing domain, it is shown that the analytical thresholds derived from the asymptotic theory accurately predict critical values of L where either spike self-replication or spike-nucleation will occur. The global bifurcation mechanism for transitions to patterns of increased spatial complexity is further elucidated by superimposing time-dependent PDE simulation results on the numerically computed solution branches of spike equilibria., Comment: 45 pages

Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing to fit simulated LSST 10-year quasar light curves. The driving variability is reconstructed using a latent stochastic differential equation, a physically motivated, generative deep learning method that can model continuous-time stochastic dynamics. By embedding these physical processes into our network, we achieve a model that is more robust and interpretable. We also use transformers to scale our model to tens of millions of parameters. We demonstrate how our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wavebands, even for out-of-distribution driving signals. Our approach provides a powerful and scalable framework that can be adapted to solve other inverse problems in multivariate time series with irregular sampling., Comment: 31 pages, 19 figures

Characterizing the host galaxies of astrophysical transients is important to many areas of astrophysics, including constraining the progenitor systems of core-collapse supernovae, correcting Type Ia supernova distances, and probabilistically classifying transients without photometric or spectroscopic data. Given the increasing transient discovery rate in the coming years, there is substantial utility in providing public, transparent, reproducible, and automatic characterization for large samples of transient host galaxies. Here we present Blast, a web application that ingests live streams of transient alerts, matches transients to their host galaxies, and performs photometry on coincident archival imaging data of the host galaxy. The photometry is then used to infer both global host-galaxy properties and galaxy properties within 2 kpc of the transient location by using the Prospector Bayesian inference framework, with an acceleration in evaluation speed achieved via simulation-based inference. Blast provides host-galaxy properties to users via a web browser or an application program interface. The software can be extended to support alternative photometric or SED-fitting algorithms, and can be scaled via an asynchronous worker queue across multiple compute nodes to handle the processing of large volumes of transient alerts for upcoming transient surveys. Blast has been ingesting newly discovered transients from the Transient Name Server since mid-2024, and has currently measured SED parameters for more than 6000 transients. The service is publicly available at https://blast.scimma.org/., Comment: submitted to PASP

Luminous interacting supernovae are a class of stellar explosions whose progenitors underwent vigorous mass loss in the years prior to core-collapse. While the mechanism by which this material is ejected is still debated, obtaining the full density profile of the circumstellar medium (CSM) could reveal more about this process. Here, we present an extensive multi-wavelength study of PS1-11aop, a luminous and slowly declining Type IIn SN discovered by the PanSTARRS Medium Deep Survey. PS1-11aop had a peak r-band magnitude of $-$20.5\,mag, a total radiated energy $>$ 8$\times$10$^{50}$\,erg, and it exploded near the center of a star-forming galaxy with super-solar metallicity. We obtained multiple detections at the location of PS1-11aop in the radio and X-ray bands between 4 and 10\,years post-explosion, and if due to the SN, it is one of the most luminous radio supernovae identified to date. Taken together, the multiwavelength properties of PS1-11aop are consistent with a CSM density profile with multiple zones. The early optical emission is consistent with the supernova blastwave interacting with a dense and confined CSM shell which contains multiple solar masses of material that was likely ejected in the final $<$10-100 years prior to the explosion,($\sim$0.05$-$1.0 M$_{\odot}$yr$^{-1}$ at radii of $\lesssim$10$^{16}$\,cm). The radio observations, on the other hand, are consistent with a sparser environment ($\lesssim$2$\times 10^{-3}$ M$_{\odot}$yr$^{-1}$ at radii of $\sim$0.5-1$\times$10$^{17}$\,cm) -- thus probing the history of the progenitor star prior to its final mass loss episode., Comment: 37 pages, 17 figures

For over 25 years, the origin of long-duration gamma-ray bursts (lGRBs) has been linked to the collapse of rotating massive stars. However, we have yet to pinpoint the stellar progenitor powering these transients. Moreover, the dominant engine powering the explosions remains open to debate. Observations of both lGRBs, supernovae associated with these GRBs, such as broad-line (BL) stripped-envelope (type Ic) supernovae (hereafter, Ic-BL) supernovae (SNe) and perhaps superluminous SNe, fast blue optical transients, and fast x-ray transients, may provide clues to both engines and progenitors. In this paper, we conduct a detailed study of the tight-binary formation scenario for lGRBs, comparing this scenario to other leading progenitor models. Combining this progenitor scenario with different lGRB engines, we can compare to existing data and make predictions for future observational tests. We find that the combination of the tight-binary progenitor scenario with the black hole accretion disk (BHAD) engine can explain lGRBs, low-luminosity GRBs, ultra-long GRBs, and Ic-BL. We discuss the various progenitor properties required for these different subclasses and note such systems would be future gravitational wave merger sources. We show that the current literature on other progenitor-engine scenarios cannot explain all of these transient classes with a single origin, motivating additional work. We find that the tight-binary progenitor with a magnetar engine is excluded by existing observations. The observations can be used to constrain the properties of stellar evolution, the nature of the GRB and the associated SN engines in lGRBs and Ic-BL. We discuss the future observations needed to constrain our understanding of these rare, but powerful, explosions., Comment: 20 pages, 10 figures, submitted to ApJ

Learning a latent dynamics model provides a task-agnostic representation of an agent's understanding of its environment. Leveraging this knowledge for model-based reinforcement learning (RL) holds the potential to improve sample efficiency over model-free methods by learning from imagined rollouts. Furthermore, because the latent space serves as input to behavior models, the informative representations learned by the world model facilitate efficient learning of desired skills. Most existing methods rely on holistic representations of the environment's state. In contrast, humans reason about objects and their interactions, predicting how actions will affect specific parts of their surroundings. Inspired by this, we propose Slot-Attention for Object-centric Latent Dynamics (SOLD), a novel model-based RL algorithm that learns object-centric dynamics models in an unsupervised manner from pixel inputs. We demonstrate that the structured latent space not only improves model interpretability but also provides a valuable input space for behavior models to reason over. Our results show that SOLD outperforms DreamerV3 and TD-MPC2 - state-of-the-art model-based RL algorithms - across a range of benchmark robotic environments that require relational reasoning and manipulation capabilities. Videos are available at https://slot-latent-dynamics.github.io/.

Due to the difficulty of replicating the real conditions during training, supervised algorithms for spacecraft pose estimation experience a drop in performance when trained on synthetic data and applied to real operational data. To address this issue, we propose a test-time adaptation approach that leverages the temporal redundancy between images acquired during close proximity operations. Our approach involves extracting features from sequential spacecraft images, estimating their poses, and then using this information to synthesise a reconstructed view. We establish a self-supervised learning objective by comparing the synthesised view with the actual one. During training, we supervise both pose estimation and image synthesis, while at test-time, we optimise the self-supervised objective. Additionally, we introduce a regularisation loss to prevent solutions that are not consistent with the keypoint structure of the spacecraft. Our code is available at: https://github.com/JotaBravo/spacecraft-tta., Comment: Preprint

Although response-adaptive randomisation (RAR) has gained substantial attention in the literature, it still has limited use in clinical trials. Amongst other reasons, the implementation of RAR in the real world raises important practical questions, often neglected. Motivated by an innovative phase-II stratified RAR trial, this paper addresses two challenges: (1) How to ensure that RAR allocations are both desirable and faithful to target probabilities, even in small samples? and (2) What adaptations to trigger after interim analyses in the presence of missing data? We propose a Mapping strategy that discretises the randomisation probabilities into a vector of allocation ratios, resulting in improved frequentist errors. Under the implementation of Mapping, we analyse the impact of missing data on operating characteristics by examining selected scenarios. Finally, we discuss additional concerns including: pooling data across trial strata, analysing the level of blinding in the trial, and reporting safety results.

In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments.

Theories of localised pattern formation are important to understand a broad range of natural patterns, but are less well-understood than more established mechanisms of domain-filling pattern formation. Here, we extend recent work on pattern localisation via slow spatial heterogeneity in reaction-diffusion systems to the Swift-Hohenberg equation. We use a WKB asymptotic approach to show that, in the limit of a large domain and slowly varying heterogeneity, conditions for Turing-type linear instability localise in a simple way, with the spatial variable playing the role of a parameter. For nonlinearities locally corresponding to supercritical bifurcations in the spatially homogeneous system, this analysis asymptotically predicts regions where patterned states are confined, which we confirm numerically. We resolve the inner region of this asymptotic approach, finding excellent agreement with the tails of these confined pattern regions. In the locally subcritical case, however, this theory is insufficient to fully predict such confined regions, and so we propose an approach based on numerical continuation of a local homogeneous analog system. Pattern localisation in the heterogeneous system can then be determined based on the Maxwell point of this system, with the spatial variable parameterizing this point. We compare this theory of localisation via spatial heterogeneity to localised patterns arising from homoclinic snaking, and suggest a way to distinguish between different localisation mechanisms in natural systems based on how these structures decay to the background state (i.e. how their tails decay). We also explore cases where both of these local theories of pattern formation fail to capture the interaction between spatial heterogeneity and underlying pattern-forming mechanisms, suggesting that more work needs to be done to fully disentangle exogenous and intrinsic heterogeneity., Comment: 36 pages, 17 figures

Type IIn supernovae (SNeIIn) are a highly heterogeneous subclass of core-collapse supernovae, spectroscopically characterized by signatures of interaction with a dense circumstellar medium (CSM). Here we systematically model the light curves of 142 archival SNeIIn using MOSFiT (the Modular Open Source Fitter for Transients). We find that the observed and inferred properties of SNIIn are diverse, but there are some trends. The typical SN CSM is dense ($\sim$10$^{-12}$gcm$^{-3}$) with highly diverse CSM geometry, with a median CSM mass of $\sim$1M$_\odot$. The ejecta are typically massive ($\gtrsim10$M$_\odot$), suggesting massive progenitor systems. We find positive correlations between the CSM mass and the rise and fall times of SNeIIn. Furthermore there are positive correlations between the rise time and fall times and the $r$-band luminosity. We estimate the mass-loss rates of our sample (where spectroscopy is available) and find a high median mass-loss rate of $\sim$10$^{-2}$M$_\odot$yr$^{-1}$, with a range between 10$^{-4}$--1M$_\odot$yr$^{-1}$. These mass-loss rates are most similar to the mass loss from great eruptions of luminous blue variables, consistent with the direct progenitor detections in the literature. We also discuss the role that binary interactions may play, concluding that at least some of our SNeIIn may be from massive binary systems. Finally, we estimate a detection rate of 1.6$\times$10$^5$yr$^{-1}$ in the upcoming Legacy Survey of Space and Time at the Vera C. Rubin Observatory., Comment: 55 pages, 19 figures, 6 tables

In the current NISQ-era, one of the major challenges faced by researchers and practitioners lies in figuring out how to combine quantum and classical computing in the most efficient and innovative way. In this paper, we present a mechanism coined as Quantum Initialization for Warehouse Optimization Problem that resorts to D-Wave's Quantum Annealer. The module has been specifically designed to be embedded into already existing classical software dedicated to the optimization of a real-world industrial problem. We preliminary tested the implemented mechanism through a two-phase experiment against the classical version of the software., Comment: 2 pages, 2 figures. Paper presented at the 5th IEEE International Conference on Quantum Computing and Engineering (IEEE QCE 2024)

General conditions are established under which reaction-cross-diffusion systems can undergo spatiotemporal pattern-forming instabilities. Recent work has focused on designing systems theoretically and experimentally to exhibit patterns with specific features, but the case of non-diagonal diffusion matrices has yet to be analysed. Here, a framework is presented for the design of general $n$-component reaction-cross-diffusion systems that exhibit Turing and wave instabilities of a given wavelength. For a fixed set of reaction kinetics, it is shown how to choose diffusion matrices that produce each instability; conversely, for a given diffusion tensor, how to choose linearised kinetics. The theory is applied to several examples including a hyperbolic reaction-diffusion system, two different 3-component models, and a spatio-temporal version of the Ross-Macdonald model for the spread of malaria., Comment: 49 pages, 14 figures

Weakly nonlinear amplitude equations are derived for the onset of spatially extended patterns on a general class of $n$-component bulk-surface reaction-diffusion systems in a ball, under the assumption of linear kinetics in the bulk. Linear analysis shows conditions under which various pattern modes can become unstable to either generalised pitchfork or transcritical bifurcations depending on the parity of the spatial wavenumber. Weakly nonlinear analysis is used to derive general expressions for the multi-component amplitude equations of different patterned states. These reduced-order systems are found to agree with prior normal forms for pattern formation bifurcations with $O(3)$ symmetry and provide information on the stability of bifurcating patterns of different symmetry types. The analysis is complemented with numerical results using a dedicated finite-element method. The theory is illustrated in two examples; a bulk-surface version of the Brusselator, and a four-component cell-polarity model.

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.

We analyze JWST MIRI/MRS observations of the infrared PAH bands in the nuclear and circumnuclear regions of local AGN from the GATOS Survey. In this work, we examine the PAH properties in the circumnuclear regions of AGN and AGN-outflows, and compare them to those in star-forming regions and the innermost regions of AGN. This study employs 4.9-28.1 micron sub-arcsecond angular resolution data to investigate the properties of PAH in three nearby sources (DL~30-40 Mpc). Our findings align with previous JWST studies, showing that the central regions of AGN show a larger fraction of neutral PAH molecules (i.e. elevated 11.3/6.2 and 11.3/7.7 PAH ratios) compared to star-forming galaxies. We find that the AGN might affect not only the PAH population in the innermost region but also in the extended regions up to ~kpc scales. By comparing our observations to PAH diagnostic diagrams, we find that, in general, regions located in the projected direction of the AGN-outflow occupy similar positions on the PAH diagnostic diagrams as those of the innermost regions of AGN. Star-forming regions that are not affected by the AGN in these galaxies share the same part of the diagram as Star-forming galaxies. We examine the potential of the PAH-H2 diagram to disentangle AGN versus star-forming activity. Our results suggest that in Sy-like AGN, illumination and feedback from the AGN might affect the PAH population at nuclear and kpc scales, in particular, the ionization state of the PAH grains. However, PAH sizes are rather similar. The carriers of the ionized PAH bands (6.2 and 7.7 micron) are less resilience than those of neutral PAH bands (11.3 micron), which might be particularly important for strongly AGN-host coupled systems. Therefore, caution must be applied when using PAH bands as star-formation rate indicators in these systems even at kpc scales, with the ionized ones being more affected by the AGN., Comment: Accepted for publication in A&A. 21 pages, 13 Figures

We present multi-wavelength data of SN2020acct, a double-peaked stripped-envelope supernova (SN) in NGC2981 at ~150 Mpc. The two peaks are temporally distinct, with maxima separated by 58 rest-frame days, and a factor of 20 reduction in flux between. The first is luminous (M$_{r}$ = -18.00 $\pm$ 0.02 mag), blue (g - r = 0.27 $\pm$ 0.03 mag), and displays spectroscopic signatures of interaction with hydrogen-free circumstellar material. The second peak is fainter (M$_{r}$ = -17.29 $\pm$ 0.03 mag), and spectroscopically similar to an evolved stripped-envelope SNe, with strong blended forbidden [Ca II] and [O II] features. No other known double-peak SN exhibits a light curve similar to that of SN 2020acct. We find the likelihood of two individual SNe occurring in the same star-forming region within that time to be highly improbable, while an implausibly fine-tuned configuration would be required to produce two SNe from a single binary system. We find that the peculiar properties of SN2020acct match models of pulsational pair instability (PPI), in which the initial peak is produced by collisions of shells of ejected material, shortly followed by a terminal explosion. Pulsations from a star with a 72 M$_{\odot}$ helium core provide an excellent match to the double-peaked light curve. The local galactic environment has a metallicity of 0.4 Z$_{\odot}$, a level where massive single stars are not expected retain enough mass to encounter the PPI. However, late binary mergers or a low-metallicity pocket may allow the required core mass. We measure the rate of SN 2020acct-like events to be $<3.3\times10^{-8}$ Mpc$^{-3}$ yr$^{-1}$ at z = 0.07, or <0.1% of the total core-collapse SN rate., Comment: 31 pages, 14 figures. Submitted to ApJL, comments welcome

We present a comprehensive analysis of the photometric and spectroscopic evolution of SN~2021foa, unique among the class of transitional supernovae for repeatedly changing its spectroscopic appearance from hydrogen-to-helium-to-hydrogen-dominated (IIn-to-Ibn-to-IIn) within 50 days past peak brightness. The spectra exhibit multiple narrow ($\approx$ 300--600~km~s$^{-1}$) absorption lines of hydrogen, helium, calcium and iron together with broad helium emission lines with a full-width-at-half-maximum (FWHM) of $\sim 6000$~km~s$^{-1}$. For a steady, wind-mass loss regime, light curve modeling results in an ejecta mass of $\sim 8$ M$_{\odot}$ and CSM mass below 1 M$_{\odot}$, and an ejecta velocity consistent with the FWHM of the broad helium lines. We obtain a mass-loss rate of $\approx 2$ M$_{\odot} {\rm yr}^{-1}$. This mass-loss rate is three orders of magnitude larger than derived for normal Type II SNe. We estimate that the bulk of the CSM of SN~2021foa must have been expelled within half a year, about 15 years ago. Our analysis suggests that SN~2021foa had a helium rich ejecta which swept up a dense shell of hydrogen rich CSM shortly after explosion. At about 60 days past peak brightness, the photosphere recedes through the dense ejecta-CSM region, occulting much of the red-shifted emission of the hydrogen and helium lines, which results in observed blue-shift ($\sim -3000$~km~s$^{-1}$). Strong mass loss activity prior to explosion, such as those seen in SN~2009ip-like objects and SN~2021foa as precursor emission, are the likely origin of a complex, multiple-shell CSM close to the progenitor star., Comment: Revised. Accepted in ApJ

A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of this imbalance, with the number of supernovae observed photometrically outpacing the number observed spectroscopically by multiple orders of magnitude. At the same time, no data-driven models exist to understand these photometric and spectroscopic observables in a common context. Contrastive learning objectives, which have grown in popularity for aligning distinct data modalities in a shared embedding space, provide a potential solution to extract information from these modalities. We present Maven, the first foundation model for supernova science. To construct Maven, we first pre-train our model to align photometry and spectroscopy from 0.5M synthetic supernovae using a constrastive objective. We then fine-tune the model on 4,702 observed supernovae from the Zwicky Transient Facility. Maven reaches state-of-the-art performance on both classification and redshift estimation, despite the embeddings not being explicitly optimized for these tasks. Through ablation studies, we show that pre-training with synthetic data improves overall performance. In the upcoming era of the Vera C. Rubin Observatory, Maven serves as a Rosetta Stone for leveraging large, unlabeled and multimodal time-domain datasets., Comment: code: https://github.com/ThomasHelfer/multimodal-supernovae data: https://huggingface.co/datasets/thelfer/multimodal_supernovae

Conformal Autoencoders are a neural network architecture that imposes orthogonality conditions between the gradients of latent variables towards achieving disentangled representations of data. In this letter we show that orthogonality relations within the latent layer of the network can be leveraged to infer the intrinsic dimensionality of nonlinear manifold data sets (locally characterized by the dimension of their tangent space), while simultaneously computing encoding and decoding (embedding) maps. We outline the relevant theory relying on differential geometry, and describe the corresponding gradient-descent optimization algorithm. The method is applied to standard data sets and we highlight its applicability, advantages, and shortcomings. In addition, we demonstrate that the same computational technology can be used to build coordinate invariance to local group actions when defined only on a (reduced) submanifold of the embedding space.