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Research on school improvement has accumulated an extensive list of factors that facilitate turnarounds at underperforming schools. Given that contextual or resource constraints may limit the possibilities of putting all of these factors in place, an important question is what is necessary and sufficient to turn a school around. We use a qualitative comparative analysis (QCA) of 77 Swedish schools studied over 12 years to answer this question. Our core finding is that there is no "silver bullet" solution. There are, instead, several distinct combinations of factors that can enable a turnaround. The local school context is essential for which combinations of factors are necessary and sufficient for school turnaround. We discuss implications for research on school improvement and education policy.

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

Coordinating the motion of robots with high degrees of freedom (DoF) to grasp objects gives rise to many challenges. In this paper, we propose a novel imitation learning approach to learn a policy that directly predicts 23 DoF grasp trajectories from a partial point cloud provided by a single, fixed camera. At the core of the approach is a second-order geometric-based model of behavioral dynamics. This Neural Geometric Fabric (NGF) policy predicts accelerations directly in joint space. We show that our policy is capable of generalizing to novel objects, and combine our policy with a geometric fabric motion planner in a loop to generate stable grasping trajectories. We evaluate our approach on a set of three different objects, compare different policy structures, and run ablation studies to understand the importance of different object encodings for policy learning., Comment: IEEE International Conference on Robotics and Automation (ICRA) Workshop on Geometric Representations 2023

Detailed studies of two-neutrino double electron capture (2$\nu$DEC) is a crucial step towards searching for the neutrino-less mode to explore the Majorana nature of neutrinos. We have measured precisely the half-life of the 2$\nu$DEC process in $^{124}$Xe, utilizing a total exposure of 1.73 tonne$\cdot$year from the commissioning run and the first science run of the PandaX-4T experiment. A time-dependent background model in the $\mathcal{O}$(10 keV) energy is constructed for the first time in PandaX-4T data. With an unbinned maximum likelihood fit, we determine the half-life of the 2$\nu$DEC process to be $(1.03\pm0.15_{\rm stat}\pm0.06_{\rm sys})\times 10^{22}$$\,$yr. Furthermore, we have evaluated the branching ratio for both electrons captured from the $K$ shell ($KK$) to be $(65\pm5)\%$, which aligns with the $^{124}$Xe nuclear model calculations within 1.5$\,$$\sigma$., Comment: 18 pages, 5 figures, 3 tables

For reasons of computational constraint, most global ocean circulation models used for Earth System Modeling still rely on parameterizations of sub-grid processes, and limitations in these parameterizations affect the modeled ocean circulation and impact on predictive skill. An increasingly popular approach is to leverage machine learning approaches for parameterizations, regressing for a map between the resolved state and missing feedbacks in a fluid system as a supervised learning task. However, the learning is often performed in an `offline' fashion, without involving the underlying fluid dynamical model during the training stage. Here, we explore the `online' approach that involves the fluid dynamical model during the training stage for the learning of baroclinic turbulence and its parameterization, with reference to ocean eddy parameterization. Two online approaches are considered: a full adjoint-based online approach, related to traditional adjoint optimization approaches that require a `differentiable' dynamical model, and an approximately online approach that approximates the adjoint calculation and does not require a differentiable dynamical model. The online approaches are found to be generally more skillful and numerically stable than offline approaches. Others details relating to online training, such as window size, machine learning model set up and designs of the loss functions are detailed to aid in further explorations of the online training methodology for Earth System Modeling., Comment: 25 pages, 1 table, 8 figures

This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal-dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes sublinear network regret and cumulative constraint violation bounds. When Slater condition holds, the network cumulative constraint violation bound is reduced. In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced. For the case where strongly convex parameters are known, the network regret and cumulative constraint violation bounds are further reduced. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater condition. Finally, a numerical example is provided to verify the theoretical results., Comment: arXiv admin note: text overlap with arXiv:2406.14060, arXiv:2306.00149

Biomarkers play a central role in medicine's gradual progress towards proactive, personalized precision diagnostics and interventions. However, finding biomarkers that provide very early indicators of a change in health status, particularly for multi-factorial diseases, has been challenging. Discovery of such biomarkers stands to benefit significantly from advanced information processing and means to detect complex correlations, which quantum computing offers. In this perspective paper, quantum algorithms, particularly in machine learning, are mapped to key applications in biomarker discovery. The opportunities and challenges associated with the algorithms and applications are discussed. The analysis is structured according to different data types - multi-dimensional, time series, and erroneous data - and covers key data modalities in healthcare - electronic health records (EHRs), omics, and medical images. An outlook is provided concerning open research challenges.

In this paper, we provide a tutorial overview and an extension of a recently developed framework for data-driven control of unknown nonlinear systems with rigorous closed-loop guarantees. The proposed approach relies on the Koopman operator representation of the nonlinear system, for which a bilinear surrogate model is estimated based on data. In contrast to existing Koopman-based estimation procedures, we state guaranteed bounds on the approximation error using the stability- and certificate-oriented extended dynamic mode decomposition (SafEDMD) framework. The resulting surrogate model and the uncertainty bounds allow us to design controllers via robust control theory and sum-of-squares optimization, guaranteeing desirable properties for the closed-loop system. We present results on stabilization both in discrete and continuous time, and we derive a method for controller design with performance objectives. The benefits of the presented framework over established approaches are demonstrated with a numerical example.

In holography, two manifestations of the black hole information paradox are given by the non-isometric nature of the bulk-boundary map and by the factorisation puzzle. By considering time-shifted microstates of the eternal black hole, we demonstrate that both these puzzles may be simultaneously resolved by taking into account non-local quantum corrections that correspond to wormholes arising from state averaging. This is achieved by showing, using a resolvent technique, that the resulting Hilbert space for an eternal black hole in Anti-de Sitter space is finite-dimensional with a discrete energy spectrum. The latter gives rise to a transition to a type I von Neumann algebra., Comment: 12 pages, 6 figures

We aim to apply a quantum computing technique to compose artworks. The main idea is to revisit three paintings of different styles and historical periods: ''Narciso'', painted circa 1597-1599 by Michelangelo Merisi (Caravaggio), ''Les fils de l'homme'', painted in 1964 by Rene Magritte and ''192 Farben'', painted in 1966 by Gerard Richter. We utilize the output of a quantum computation to change the composition in the paintings, leading to a paintings series titled ''Quantum Transformation I, II, III''. In particular, the figures are discretized into square lattices and the order of the pieces is changed according to the result of the quantum simulation. We consider an Ising Hamiltonian as the observable in the quantum computation and its time evolution as the final outcome. From a classical subject to abstract forms, we seek to combine classical and quantum aesthetics through these three art pieces. Besides experimenting with hardware runs and circuit noise, our goal is to reproduce these works as physical oil paintings on wooden panels. With this process, we complete a full circle between classical and quantum techniques and contribute to rethinking Art practice in the era of quantum computing technologies., Comment: 10 pages, 8 figures

This paper presents Low-cost Marine Autonomous Robotic Vehicle Explorer (Lo-MARVE), a novel autonomous underwater vehicle (AUV) designed to provide a low cost solution for underwater exploration and environmental monitoring in shallow water environments. Lo-MARVE offers a cost-effective alternative to existing AUVs, featuring a modular design, low-cost sensors, and wireless communication capabilities. The total cost of Lo-MARVE is approximately EUR 500. Lo-MARVE is developed using the Raspberry Pi 4B microprocessor, with control software written in Python. The proposed AUV was validated through field testing outside of a laboratory setting, in the freshwater environment of the River Corrib in Galway, Ireland. This demonstrates its ability to navigate autonomously, collect data, and communicate effectively outside of a controlled laboratory setting. The successful deployment of Lo-MARVE in a real-world environment validates its proof of concept., Comment: This paper was presented at the 12th International Conference on Control, Mechatronics and Automation (ICCMA 2024), held in London, UK, from November 11-13, 2024

We propose a quantum error mitigation scheme for single-qubit measurement errors, particularly suited for one-way quantum computation. Contrary to well established error mitigation methods for circuit-based quantum computation, that require to run the circuits several times, our method is capable of mitigating measurement errors in real-time, during the processing measurements of the one-way computation. For that, an ancillary qubit register is entangled with the to-be-measured qubit and additionally measured afterwards. By using a voting protocol on all measurement outcomes, occurring measurement errors can be mitigated in real-time while the one-way computation continues. We provide an analytical expression for the probability to detect a measurement error in dependency of the error rate and the number of ancilla qubits. From this, we derive an estimate of the ancilla register size for a given measurement error rate and a required success probability to detect a measurement error. Additionally, we also consider the CNOT gate error in our mitigation method and investigate how this influences the probability to detect a measurement error. Finally, we show in proof-of-principle simulations, also considering a hardware noise model, that our method is capable of reducing the measurement errors significantly in a one-way quantum computation with only a small number of ancilla qubits., Comment: 11 pages, 10 figures

Fainter standard stars are essential for the calibration of larger telescopes. This work adds to the CALSPEC (calibration spectra) database 19 faint white dwarfs (WDs) with all-sky coverage and V magnitudes between 16.5 and 18.7. Included for these stars is new UV (ultraviolet) HST (Hubble Space Telescope) STIS (Space Telescope Imaging Spectrometer) spectrophotometry between 1150 and 3000~\AA\ with a resolution of $\sim$500. Pure hydrogen WD models are fit to these UV spectra and to six-band HST/WFC3 (Wide Field Camera 3) photometry at 0.28 to 1.6~\micron\ to construct predicted model SEDs (spectral energy distributions) covering wavelengths from 900~\AA\ to the JWST (James Webb Space Telescope) limit of 30~\micron\ using well-established CALSPEC procedures for producing flux standards with the goal of 1\% accuracy.

Ever since they were first detected in the interstellar medium, the radio wavelength (3.3 GHz) hyperfine-structure splitting transitions in the rotational ground state of CH have been observed to show anomalous excitation. Astonishingly, this behaviour has been uniformly observed towards a variety of different sources probing a wide range of physical conditions. While the observed level inversion can be explained globally by a pumping scheme involving collisions, a description of the extent of 'over-excitation' observed in individual sources requires the inclusion of radiative processes, involving transitions at higher rotational levels. Therefore, a complete description of the excitation mechanism in the CH ground state, observed towards individual sources entails observational constraints from the rotationally excited levels of CH and in particular that of its first rotationally excited state. Given the limited detections of these lines, the objective of this work is to characterise the physical and excitation properties of the rotationally excited lines of CH near 700 MHz, and investigate their influence on the pumping mechanisms of the ground-state lines of CH. This work presents the first interferometric search for the rotationally excited lines of CH near 700 MHz carried out using the uGMRT array and jointly models the physical and excitation conditions traced by lines from both the ground and first rotationally excited states of CH., Comment: Accepted for publication in A&A 18 Pages + Appendix, 22 Figures, 4 Tables

Advances in machine learning and the growing trend towards effortless data generation in real-world systems has led to an increasing interest for data-inferred models and data-based control in robotics. It seems appealing to govern robots solely based on data, bypassing the traditional, more elaborate pipeline of system modeling through first-principles and subsequent controller design. One promising data-driven approach is the Extended Dynamic Mode Decomposition (EDMD) for control-affine systems, a system class which contains many vehicles and machines of immense practical importance including, e.g., typical wheeled mobile robots. EDMD can be highly data-efficient, computationally inexpensive, can deal with nonlinear dynamics as prevalent in robotics and mechanics, and has a sound theoretical foundation rooted in Koopman theory. On this background, this present paper examines how EDMD models can be integrated into predictive controllers for nonholonomic mobile robots. In addition to the conventional kinematic mobile robot, we also cover the complete data-driven control pipeline - from data acquisition to control design - when the robot is not treated in terms of first-order kinematics but in a second-order manner, allowing to account for actuator dynamics. Using only real-world measurement data, it is shown in both simulations and hardware experiments that the surrogate models enable high-precision predictive controllers in the studied cases. However, the findings raise significant concerns about purely data-centric approaches that overlook the underlying geometry of nonholonomic systems, showing that, for nonholonomic systems, some geometric insight seems necessary and cannot be easily compensated for with large amounts of data., Comment: 23 pages, 12 figures

An intelligent tutoring system (ITS) aims to provide instructions and exercises tailored to the ability of a student. To do this, the ITS needs to estimate the ability based on student input. Rather than including frequent full-scale tests to update our ability estimate, we want to base estimates on the outcomes of practice exercises that are part of the learning process. A challenge with this approach is that the ability changes as the student learns, which makes traditional item response theory (IRT) models inappropriate. Most IRT models estimate an ability based on a test result, and assume that the ability is constant throughout a test. We review some existing methods for measuring abilities that change throughout the measurement period, and propose a new method which we call the Elo-informed growth model. This method assumes that the abilities for a group of respondents who are all in the same stage of the learning process follow a distribution that can be estimated. The method does not assume a particular shape of the growth curve. It performs better than the standard Elo algorithm when the measured outcomes are far apart in time, or when the ability change is rapid.

(Abridged) JWST continues to deliver incredibly detailed infrared (IR) images of star forming regions in the Milky Way and beyond. IR emission from star-forming regions is very spectrally rich due to emission from gas-phase atoms, ions, and polycyclic aromatic hydrocarbons (PAHs). Physically interpreting IR images of these regions relies on assumptions about the underlying spectral energy distribution in the imaging bandpasses. We aim to provide empirical prescriptions linking line, PAH, and continuum intensities from JWST images, to facilitate the interpretation of JWST images in a wide variety of contexts. We use JWST PDRs4All Near-Infrared Camera (NIRCam) and Mid-Infrared Instrument (MIRI) imaging and Near-Infrared Spectrograph (NIRSpec) integral field unit (IFU) and MIRI Medium Resolution Spectrograph (MRS) spectroscopic observations of the Orion Bar photodissociation region (PDR), to directly compare and cross-calibrate imaging and IFU data at ~100 AU resolution over a region where the radiation field and ISM environment evolves from the hot ionized gas to the cold molecular gas. We measure the relative contributions of line, PAH, and continuum emission to the NIRCam and MIRI filters as functions of local physical conditions. We provide empirical prescriptions based on NIRCam and MIRI images to derive intensities of emission lines and PAH features. Within the range of the environments probed in this study, these prescriptions accurately predict Pa-alpha, Br-alpha, PAH 3.3 um and 11.2 um intensities, while those for FeII 1.644 um, H_2 1--0 S(1) 2.12 um and 1--0 S(9) 4.96 um, and PAH 7.7 um show more complicated environmental dependencies. Linear combinations of JWST NIRCam and MIRI images provide effective tracers of ionized gas, H_2, and PAH emission in PDRs. We expect these recipes to be useful for both the Galactic and extragalactic communities., Comment: 31 pages, 24 figures, submitted to A&A

Compact lattice Quantum Electrodynamics is a complex quantum field theory with dynamical gauge and matter fields and it has similarities with Quantum Chromodynamics, in particular asymptotic freedom and confinement. We consider a (2+1)-dimensional lattice discretization of Quantum Electrodynamics with the inclusion of dynamical fermionic matter. We define a suitable quantum algorithm to measure the static potential as a function of the distance between two charges on the lattice and we use a variational quantum calculation to explore the Coulomb, confinement and string breaking regimes. A symmetry-preserving and resource-efficient variational quantum circuit is employed to prepare the ground state of the theory at various values of the coupling constant, corresponding to different physical distances, allowing the accurate extraction of the static potential from a quantum computer. We demonstrate that results from quantum experiments on the Quantinuum H1-1 trapped-ion device and emulator, with full connectivity between qubits, agree with classical noiseless simulations using circuits with 10 and 24 qubits. Moreover, we visualize the electric field flux configurations that mostly contribute in the wave-function of the quantum ground state in the different regimes of the potential, thus giving insights into the mechanisms of confinement and string breaking. These results are a promising step forward in the grand challenge of solving higher dimensional lattice gauge theory problems with quantum computing algorithms., Comment: 21 pages, 26 figures, 3 tables

In this paper we present a method for line segment detection in images, based on a semi-supervised framework. Leveraging the use of a consistency loss based on differently augmented and perturbed unlabeled images with a small amount of labeled data, we show comparable results to fully supervised methods. This opens up application scenarios where annotation is difficult or expensive, and for domain specific adaptation of models. We are specifically interested in real-time and online applications, and investigate small and efficient learning backbones. Our method is to our knowledge the first to target line detection using modern state-of-the-art methodologies for semi-supervised learning. We test the method on both standard benchmarks and domain specific scenarios for forestry applications, showing the tractability of the proposed method., Comment: 9 pages, 6 figures, 7 tables

In offline reinforcement learning, a policy is learned using a static dataset in the absence of costly feedback from the environment. In contrast to the online setting, only using static datasets poses additional challenges, such as policies generating out-of-distribution samples. Model-based offline reinforcement learning methods try to overcome these by learning a model of the underlying dynamics of the environment and using it to guide policy search. It is beneficial but, with limited datasets, errors in the model and the issue of value overestimation among out-of-distribution states can worsen performance. Current model-based methods apply some notion of conservatism to the Bellman update, often implemented using uncertainty estimation derived from model ensembles. In this paper, we propose Constrained Latent Action Policies (C-LAP) which learns a generative model of the joint distribution of observations and actions. We cast policy learning as a constrained objective to always stay within the support of the latent action distribution, and use the generative capabilities of the model to impose an implicit constraint on the generated actions. Thereby eliminating the need to use additional uncertainty penalties on the Bellman update and significantly decreasing the number of gradient steps required to learn a policy. We empirically evaluate C-LAP on the D4RL and V-D4RL benchmark, and show that C-LAP is competitive to state-of-the-art methods, especially outperforming on datasets with visual observations., Comment: 38th Conference on Neural Information Processing Systems (NeurIPS 2024)

PDS 453 is a rare highly inclined disk where the stellar photosphere is seen at grazing incidence on the disk surface. Our goal is take advantage of this geometry to constrain the structure and composition of this disk, in particular the fact that it shows a 3.1 $\mu$m water ice band in absorption that can be related uniquely to the disk. We observed the system in polarized intensity with the VLT/SPHERE instrument, as well as in polarized light and total intensity using the HST/NICMOS camera. Infrared archival photometry and a spectrum showing the water ice band are used to model the spectral energy distribution under Mie scattering theory. Based on these data, we fit a model using the radiative transfer code MCFOST to retrieve the geometry and dust and ice content of the disk. PDS 453 has the typical morphology of a highly inclined system with two reflection nebulae where the disk partially attenuates the stellar light. The upper nebula is brighter than the lower nebula and shows a curved surface brightness profile in polarized intensity, indicating a ring-like structure. With an inclination of 80{\deg} estimated from models, the line-of-sight crosses the disk surface and a combination of absorption and scattering by ice-rich dust grains produces the water ice band. PDS 453 is seen highly inclined and is composed of a mixture of silicate dust and water ice. The radial structure of the disk includes a significant jump in density and scale height at a radius of 70 au in order to produce a ring-like image. The depth of the 3.1 $\mu$m water ice band depends on the amount of water ice, until it saturates when the optical thickness along the line-of-sight becomes too large. Therefore, quantifying the exact amount of water from absorption bands in edge-on disks requires a detailed analysis of the disk structure and tailored radiative transfer modeling., Comment: 11 pages, 11 figures

We present a comprehensive analysis of the observed Stellar-to-Halo mass relationship (SHMR) spanning redshifts from 0.2 to 4.5. This was enabled through galaxy clustering and abundance measurements from two large (effective area ~ 1.61 deg^2) and homogeneously prepared photometric catalogs - UltraVISTA ultra-deep stripes DR3 (COSMOS) and FENIKS v1 (UDS). To translate these measurements into the SHMR, we introduce a novel halo occupation distribution (HOD) fitting approach (``smooth-$z$'') whereby HOD parameters between neighboring z-bins are connected via physically motivated continuity (smoothing) priors. As a result, the high constraining power at z <~ 2, due to a much wider dynamical range in stellar mass (~ 3 dex), helps constrain the SHMR at z >~ 2, where that range shrinks down to <~ 1 dex. We find that the halo mass is tightly coupled to star formation: the halo mass with peak integrated star-forming efficiency (SFE), M_h^peak remains constant within ~ 10^12.2 - 10^12.4 Msolar throughout the redshifts probed. Furthermore, we show that if we had relied on COSMOS alone (as opposed to COSMOS+UDS), as has been done by many preceding studies, M_h^peak would be systematically lower by up to ~0.15 dex at z < 1.5, highlighting the importance of mitigating cosmic variance. Finally, for the first time, we show how the SFE evolves with redshift as halos grow in mass along their progenitor merger trees, instead of at fixed halo masses., Comment: Submitted to ApJ. 36 pages, 18 figures. Comments welcome

Quantum computing presents a promising avenue for solving complex problems, particularly in quantum chemistry, where it could accelerate the computation of molecular properties and excited states. This work focuses on hybrid quantum-classical algorithms for near-term quantum devices, combining the quantum linear response (qLR) method with a polarizable embedding (PE) environment. We employ the self-consistent operator manifold of quantum linear response (q-sc-LR) on top of a unitary coupled cluster (UCC) wave function in combination with a Davidson solver. The latter removes the need to construct the entire electronic Hessian, improving computational efficiency when going towards larger molecules. We introduce a new superposition-state-based technique to compute Hessian-vector products and show that this approach is more resilient towards noise than our earlier gradient-based approach. We demonstrate the performance of the PE-UCCSD model on systems such as butadiene and para-nitroaniline in water and find that PE-UCCSD delivers comparable accuracy to classical PE-CCSD methods on such simple closed-shell systems. We also explore the challenges posed by hardware noise and propose simple error correction techniques to maintain accurate results on noisy quantum computers., Comment: 36 pages, 4 figures

Convolutional Neural Networks (CNNs) have been heavily used in Deep Learning due to their success in various tasks. Nonetheless, it has been observed that CNNs suffer from redundancy in feature maps, leading to inefficient capacity utilization. Efforts to mitigate and solve this problem led to the emergence of multiple methods, amongst which is kernel orthogonality through variant means. In this work, we challenge the common belief that kernel orthogonality leads to a decrease in feature map redundancy, which is, supposedly, the ultimate objective behind kernel orthogonality. We prove, theoretically and empirically, that kernel orthogonality has an unpredictable effect on feature map similarity and does not necessarily decrease it. Based on our theoretical result, we propose an effective method to reduce feature map similarity independently of the input of the CNN. This is done by minimizing a novel loss function we call Convolutional Similarity. Empirical results show that minimizing the Convolutional Similarity increases the performance of classification models and can accelerate their convergence. Furthermore, using our proposed method pushes towards a more efficient use of the capacity of models, allowing the use of significantly smaller models to achieve the same levels of performance.

The numerical solution of singular generalized eigenvalue problems is still challenging. In Hochstenbach, Mehl, and Plestenjak, Solving Singular Generalized Eigenvalue Problems by a Rank-Completing Perturbation, SIMAX 2019, a rank-completing perturbation was proposed and a related bordering of the singular pencil. For large sparse pencils, we propose an LU factorization that determines a rank completing perturbation that regularizes the pencil and that is then used in the shift-and-invert Arnoldi method to obtain eigenvalues nearest a shift. Numerical examples illustrate the theory and the algorithms.

Lattice quantum field theory calculations may potentially combine the advantages of Hamiltonian formulations with the scalability and control of conventional Lagrangian frameworks. However, such hybrid approaches need to consider (1) the differences in renormalized coupling values between the two formulations, and (2) finite-volume and discretization effects when the Hamiltonian component of the calculation is characterized by a smaller volume or coarser lattice spacing than the Lagrangian component. This work investigates the role of both factors in the application of Hamiltonian-optimized interpolating operator constructions for the conventional Lagrangian framework. The numerical investigation is realized for the pseudoscalar meson in the Schwinger model, using tensor-network and Monte-Carlo calculations. It is demonstrated that tensor-network-optimized constructions are robust to both (1) and (2). In particular, accurate optimized constructions for the pseudoscalar meson can be obtained from calculations with a smaller number of Hamiltonian lattice sites, even when the meson mass itself receives significant finite-volume corrections. To the extent that these results generalize to theories with more complicated spectra, the method holds promise for near-term applications in large-scale calculations of lattice quantum field theory., Comment: 14 pages, 6 figures

We present NOrthern Extended Millimeter Array observations of CO(8-7), (9-8), and (10-9) lines, as well as the underlying continuum for two far-infrared luminous quasars: SDSS J2054-0005 at $\rm z=6.0389$ and SDSS J0129-0035 at $\rm z=5.7788$. Both quasars were previously detected in CO (2-1) and (6-5) transitions, making them candidates for studying the CO Spectral Line Energy Distribution (SLED) of quasars at $z \sim 6$. Utilizing the radiative transfer code CLOUDY, we fit the CO SLED with two heating mechanisms, including the photo-dissociation region (PDR) and X-ray-dominated region (XDR) for both objects. The CO SLEDs can be fitted by either a dense PDR component with an extremely strong far-ultraviolet radiation field (gas density $ n_{\rm H} \sim 10^6 \, \rm cm^{-3}$ and field strength $G_0 \gtrsim 10^6$) or a two-component model including a PDR and an XDR. However, the line ratios, including \tir and previous \cii and \ci measurements, argue against a very high PDR radiation field strength. Thus, the results prefer a PDR+XDR origin for the CO SLED. The excitation of the high-J CO lines in both objects is likely dominated by the central AGN. We then check the CO (9-8)-to-(6-5) line luminosity ratio $r_{96}$ for all $z \sim 6$ quasars with available CO SLEDs (seven in total) and find that there are no clear correlations between $r_{96}$ and both \fir and the AGN UV luminosities. This further demonstrates the complexity of the CO excitation powered by both the AGN and nuclear star formation in these young quasar host galaxies., Comment: 19 pages, 9 figures, accepted for publication in ApJ

This paper explores the integration of symmetries into the Koopman-operator framework for the analysis and efficient learning of equivariant dynamical systems using a group-convolutional approach. Approximating the Koopman operator by finite-dimensional surrogates, e.g., via extended dynamic mode decomposition (EDMD), is challenging for high-dimensional systems due to computational constraints. To tackle this problem with a particular focus on EDMD, we demonstrate -- under suitable equivarance assumptions on the system and the observables -- that the optimal EDMD matrix is equivariant. That is, its action on states can be described by group convolutions and the generalized Fourier transform. We show that this structural property has many advantages for equivariant systems, in particular, that it allows for data-efficient learning, fast predictions and fast eigenfunction approximations. We conduct numerical experiments on the Kuramoto--Sivashinsky equation, a nonlinear and chaotic partial differential equation, providing evidence of the effectiveness of this approach, and highlighting its potential for broader applications in dynamical systems analysis.

We demonstrate dual-wavelength distributed acoustic sensing over 37 km of standard single-mode fiber using $\phi$-OFDR, utilizing a scalable hybrid-integrated dual-wavelength laser chip frequency-locked to a high-Q integrated SiN coil resonator., Comment: 3 pages, 4 figures

Quantum computing is emerging as a transformative technology with the announced potential to solve large-scale problems that are currently intractable for classical computers. With the demonstrated ability to perform calculations in seconds that would take classical supercomputers thousands of years, quantum computers namely hold the promise of radically advancing sustainable IT. Despite impressive milestones, however, classical computing continues to make rapid progress, narrowing the performance gap. Moreover, quantum computers face challenges due to the inherent noise in physical qubits, necessitating error correction for reliable operation in solving industrial-scale problems. Due to error correction techniques, quantum computers potentially require more computation time, energy, and electronic components than initial laboratory-scale quantum experiments. Yet, while researchers have modeled and analyzed the environmental impacts of classical computers using Life Cycle Assessment (LCA), the environmental performance of quantum computing remains unknown to date. This study contributes to filling this critical gap in two ways: (1) by establishing an environmental profile for quantum computers; and (2) by comparing it to a functionally equivalent profile of a state-of-the-art supercomputer. With the comparison based on the problem size, the paper shows how the usage time can drive an environmental advantage for quantum computers. The results emphasize that equipment of quantum error correction has a substantial impact on quantum computers due to the numerous electronic components needed to achieve 100 logical qubits. When comparing quantum computers to classical supercomputers, the latter generally has a higher environmental impact in terms of Climate change, Ecosystems, and Human health, because of the number of computing blades and their total energy use., Comment: 25 pages, 9 figures, 3 tables

Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot policies (i.e., robot foundation models) can address these challenges, and how we can design effective generalist robot policies for complex and highly dexterous tasks. We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We then discuss how this model can be trained on a large and diverse dataset from multiple dexterous robot platforms, including single-arm robots, dual-arm robots, and mobile manipulators. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people and from a high-level VLM policy, and its ability to acquire new skills via fine-tuning. Our results cover a wide variety of tasks, such as laundry folding, table cleaning, and assembling boxes., Comment: See project website for videos: https://physicalintelligence.company/blog/pi0

\textbf{Trial design} Crossover randomized controlled trial. \textbf{Methods} An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a user study in Norway and Sweden. Participants were randomly assigned to two groups, and crossed over between coding complex (longer) texts versus simple (shorter) texts, while using our tool versus not using our tool. \textbf{Results} Based on Mann-Whitney U test, the median coding time difference for complex clinical text sequences was 123 seconds (\emph{P}\textless.001, 95\% CI: 81 to 164), representing a 46\% reduction in median coding time when our tool is used. There was no significant time difference for simpler text sequences. For coding accuracy, the improvement we noted for both complex and simple texts was not significant. \textbf{Conclusions} This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for complex clinical coding tasks. Further studies within hospital workflows are required before these presumed impacts can be more clearly understood., Comment: 13 pages, 4 figures, 4 tables

Bellman's algorithm for Subset Sum is one of the earliest and simplest examples of dynamic programming, dating back to 1957. For a given set of $n$ integers $X$ and a target $t$, it computes the set of subset sums $\mathcal S(X, t)$ (i.e., the set of integers $s \in [0\ldots t]$ for which there is a subset of $X$ summing to $s$) in time $O(|\mathcal S(X, t)| \cdot n)$. Since then, it has been an important question whether Bellman's seminal algorithm can be improved. This question is addressed in many recent works. And yet, while some algorithms improve upon Bellman's algorithm in specific parameter regimes, such as Bringmann's $\tilde O(t + n)$-time algorithm [SODA '17] and Bringmann and Nakos' $\tilde O(|\mathcal S(X, t)|^{4/3})$-time algorithm [STOC '20], none of the known algorithms beats Bellman's algorithm in all regimes. In particular, it remained open whether Subset Sum is in time $\tilde O(|\mathcal S(X, t)| \cdot n^{1-\epsilon})$ (for some $\epsilon > 0$). In this work we positively resolve this question and design an algorithm that outperforms Bellman's algorithm in all regimes. Our algorithm runs in time $\tilde O(|\mathcal S(X, t)| \cdot \sqrt{n})$, thus improving the time complexity by a factor of nearly $\sqrt n$. Our key innovation is the use of a result from additive combinatorics, which has not been applied in an algorithmic context before and which we believe to be of further independent interest for algorithm design. To demonstrate the broader applicability of our approach, we extend our ideas to a variant of Subset Sum on vectors as well as to Unbounded Subset Sum., Comment: To appear at SODA25

Vision-based object detectors are a crucial basis for robotics applications as they provide valuable information about object localisation in the environment. These need to ensure high reliability in different lighting conditions, occlusions, and visual artifacts, all while running in real-time. Collecting and annotating real-world data for these networks is prohibitively time consuming and costly, especially for custom assets, such as industrial objects, making it untenable for generalization to in-the-wild scenarios. To this end, we present Synthetica, a method for large-scale synthetic data generation for training robust state estimators. This paper focuses on the task of object detection, an important problem which can serve as the front-end for most state estimation problems, such as pose estimation. Leveraging data from a photorealistic ray-tracing renderer, we scale up data generation, generating 2.7 million images, to train highly accurate real-time detection transformers. We present a collection of rendering randomization and training-time data augmentation techniques conducive to robust sim-to-real performance for vision tasks. We demonstrate state-of-the-art performance on the task of object detection while having detectors that run at 50-100Hz which is 9 times faster than the prior SOTA. We further demonstrate the usefulness of our training methodology for robotics applications by showcasing a pipeline for use in the real world with custom objects for which there do not exist prior datasets. Our work highlights the importance of scaling synthetic data generation for robust sim-to-real transfer while achieving the fastest real-time inference speeds. Videos and supplementary information can be found at this URL: https://sites.google.com/view/synthetica-vision., Comment: 21 pages, 11 figures, 5 tables

The Local Ultraviolet to Infrared Treasury (LUVIT) is a Hubble Space Telescope program that combines newly acquired data in the near ultraviolet (NUV), optical, and near infrared (NIR) with archival optical and NIR imaging to produce multiband panchromatic resolved stellar catalogs for 23 pointings in 22 low-mass, star-forming galaxies ranging in distance from the outskirts of the Local Group to ~3.8 Mpc. We describe the survey design, detail the LUVIT broadband filter observations and the archival datasets included in the LUVIT reductions, and summarize the simultaneous multiband data reduction steps. The spatial distributions and color-magnitude diagrams (CMDs) from the resulting stellar catalogs are presented for each target, from the NUV to the NIR. We demonstrate in which regions of the CMDs stars with NUV and optical, optical and NIR, and NUV through NIR detections reside. For each target, we use the results from artificial star tests to measure representative completeness, bias, and total photometric uncertainty as a function of magnitude in each broadband filter. We also assess which LUVIT targets have significant spatial variation in the fraction of stars recovered at a given magnitude. The panchromatic LUVIT stellar catalogs will provide a rich legacy dataset for a host of resolved stellar population studies., Comment: 48 pages, 14 figures, 8 tables, accepted for publication in ApJS

Reinforcement Learning (RL) has the potential to enable robots to learn from their own actions in the real world. Unfortunately, RL can be prohibitively expensive, in terms of on-robot runtime, due to inefficient exploration when learning from a sparse reward signal. Designing dense reward functions is labour-intensive and requires domain expertise. In our work, we propose GCR (Goal-Contrastive Rewards), a dense reward function learning method that can be trained on passive video demonstrations. By using videos without actions, our method is easier to scale, as we can use arbitrary videos. GCR combines two loss functions, an implicit value loss function that models how the reward increases when traversing a successful trajectory, and a goal-contrastive loss that discriminates between successful and failed trajectories. We perform experiments in simulated manipulation environments across RoboMimic and MimicGen tasks, as well as in the real world using a Franka arm and a Spot quadruped. We find that GCR leads to a more-sample efficient RL, enabling model-free RL to solve about twice as many tasks as our baseline reward learning methods. We also demonstrate positive cross-embodiment transfer from videos of people and of other robots performing a task. Appendix: \url{https://tinyurl.com/gcr-appendix-2}.

By analyzing the spectral energy distributions (SEDs) of resolved stars in nearby galaxies, we can constrain their stellar properties and line-of-sight dust extinction. From the Scylla survey, we obtain ultraviolet to near-infrared photometry from Wide Field Camera 3 onboard the {\it Hubble Space Telescope} for more than 1.5 million stars in the SMC and LMC. We use the Bayesian Extinction and Stellar Tool (BEAST) to analyze the multi-band SEDs of these sources and characterize their initial masses, ages, metallicities, distances, and line-of-sight extinction properties (e.g.~$A_V$, $R_V$). We apply quality cuts and perform validation simulations to construct a catalog of over 550,000 stars with high-reliability SED fits, which we use to analyze the stellar content and extinction properties of the SMC and LMC. We detect stars with masses as low as 0.6 $M_{\odot}$. Observed stellar age distributions show a jump in stars around 6 Gyrs ago, which is in agreement with other star-formation histories. Extinctions ($A_V$) in both galaxies follow a log-normal distribution. We compare $A_V$ with ancillary gas and dust tracers like $HI$, $H_\alpha$, and far infrared (FIR) dust emission and find positive correlations on a field-by-field basis. We convert observed $A_V$ to predicted dust surface densities using the Draine et. al. (2014) model and find $A_V$-based dust surface densities are a factor of $\sim$2.5 lower than observed FIR-based dust surface densities, a correction factor similar to other studies., Comment: Submitted to ApJ, 31 pages

We present JWST/NIRSpec PRISM spectroscopic characterization of GHZ9 at z= 10.145 $\pm$ 0.010, currently the most distant source detected by the Chandra X-ray Observatory. The spectrum reveals several UV high-ionization lines, including CII, SiIV, [NIV], CIV, HeII, OIII], NIII], and CIII]. The prominent rest-frame equivalent widths (EW(CIV)$\simeq$65A, EW(HeII)$\simeq$18A, EW(CIII])$\simeq$48A) show the presence of a hard radiation field, while the analysis of line ratio diagnostics suggest this galaxy hosts both AGN and star-formation activity. GHZ9 is nitrogen-enriched (6--9.5 times solar), carbon-poor (0.2--0.65 times solar), metal-poor (Z = 0.01--0.1 Z$_{\odot}$), and compact ($<$ 106 pc), similarly to GNz11, GHZ2, and recently discovered N-enhanced high redshift objects. We exploited the newly available JWST/NIRSpec and NIRCam dataset to perform an independent analysis of the Chandra data confirming that GHZ9 is the most likely JWST source associated to X-ray emission at 0.5-7 keV. Assuming a spectral index $\Gamma$ = 2.3 (1.8), we estimate a black hole (BH) mass of 1.60 $\pm$ 0.31 (0.48 $\pm$ 0.09) $\times$ 10$^8$M$_{\odot}$, which is consistent either with Eddington-accretion onto heavy ($\geq$ 10$^6$ M$_{\odot}$) BH seeds formed at z=18, or super-Eddington accretion onto a light seed of $\sim$ 10$^2-10^4$ M$_{\odot}$ at z = 25. The corresponding BH-to-stellar mass ratio M$_{BH}$/M$_{star}$= 0.33$\pm$0.22 (0.10$\pm$0.07), with a stringent limit $>$0.02, implies an accelerated growth of the BH mass with respect to the stellar mass. GHZ9 is the ideal target to constrain the early phases of AGN-galaxy coevolution with future multi-frequency observations., Comment: Submitted to ApJ

We investigate the existence of solutions of reversible and irreversible port-Hamiltonian systems. To this end, we utilize the associated exergy, a function that is composed of the system's Hamiltonian and entropy, to prove global existence in time for bounded control functions. The results are then leveraged to prove existence of solutions of energy- and entropy-optimal control problems. Last, we explore model predictive control tailored to irreversible port-Hamiltonian systems by means of a numerical case study with a heat exchanger network., Comment: 24 pages, 6 figures

In steganography, selecting an optimal cover image, referred to as cover selection, is pivotal for effective message concealment. Traditional methods have typically employed exhaustive searches to identify images that conform to specific perceptual or complexity metrics. However, the relationship between these metrics and the actual message hiding efficacy of an image is unclear, often yielding less-than-ideal steganographic outcomes. Inspired by recent advancements in generative models, we introduce a novel cover selection framework, which involves optimizing within the latent space of pretrained generative models to identify the most suitable cover images, distinguishing itself from traditional exhaustive search methods. Our method shows significant advantages in message recovery and image quality. We also conduct an information-theoretic analysis of the generated cover images, revealing that message hiding predominantly occurs in low-variance pixels, reflecting the waterfilling algorithm's principles in parallel Gaussian channels. Our code can be found at: https://github.com/karlchahine/Neural-Cover-Selection-for-Image-Steganography.

Macroscopic studies of cortical tissue reveal a prevalence of oscillatory activity, that reflect a fine tuning of neural interactions. This research extends neural field theories by incorporating generalized oscillatory dynamics into previous work on conservative or semi-conservative neural field dynamics. Prior studies have largely assumed isotropic connections among neural units; however, this study demonstrates that a broad range of anisotropic and fluctuating connections can still sustain oscillations. Using Lagrangian field methods, we examine different types of connectivity, their dynamics, and potential interactions with neural fields. From this theoretical foundation, we derive a framework that incorporates Hebbian and non-Hebbian learning, i.e., plasticity, into the study of neural fields via the concept of a connectivity field., Comment: 31 pages, 4 figures

In recent years, researchers have been exploring ways to generalize Boltzmann machines (BMs) to quantum systems, leading to the development of variations such as fully-visible and restricted quantum Boltzmann machines (QBMs). Due to the non-commuting nature of their Hamiltonians, restricted QBMs face trainability issues, whereas fully-visible QBMs have emerged as a more tractable option, as recent results demonstrate their sample-efficient trainability. These results position fully-visible QBMs as a favorable choice, offering potential improvements over fully-visible BMs without suffering from the trainability issues associated with restricted QBMs. In this work, we show that low-dimensional, fully-visible QBMs can learn to generate distributions typically associated with higher-dimensional systems. We validate our findings through numerical experiments on both artificial datasets and real-world examples from the high energy physics problem of jet event generation. We find that non-commuting terms and Hamiltonian connectivity improve the learning capabilities of QBMs, providing flexible resources suitable for various hardware architectures. Furthermore, we provide strategies and future directions to maximize the learning capacity of fully-visible QBMs., Comment: 13 pages, 7 figures; supplementary material 5 pages, 5 figures

We introduce a novel technique to numerically calculate R\'enyi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network architecture around a lattice defect connecting two replicas. Numerical tests for the $\phi^4$ scalar field theory in two and three dimensions demonstrate that our technique outperforms state-of-the-art Monte Carlo calculations, and exhibit a promising scaling with the defect size., Comment: 10 pages, 9 figures

We characterise regions in the complex plane that contain all non-embedded eigenvalues of a perturbed periodic Dirac operator on the real line with real-valued periodic potential and a generally non-symmetric matrix-valued perturbation V . We show that the eigenvalues are located close to the end-points of the spectral bands for small V in L^1(R)^{2x2} , but only close to the spectral bands as a whole for small V in L^p(R)^{2x2} , p > 1. As auxiliary results, we prove the relative compactness of matrix multiplication operators in L^{2p}(R)^{2x2} with respect to the periodic operator under minimal hypotheses, and find the asymptotic solution of the Dirac equation on a finite interval for spectral parameters with large imaginary part.

Novel optical imaging techniques, such as hyperspectral imaging (HSI) combined with machine learning-based (ML) analysis, have the potential to revolutionize clinical surgical imaging. However, these novel modalities face a shortage of large-scale, representative clinical data for training ML algorithms, while preclinical animal data is abundantly available through standardized experiments and allows for controlled induction of pathological tissue states, which is not ethically possible in patients. To leverage this situation, we propose a novel concept called "xeno-learning", a cross-species knowledge transfer paradigm inspired by xeno-transplantation, where organs from a donor species are transplanted into a recipient species. Using a total of 11,268 HSI images from humans as well as porcine and rat models, we show that although spectral signatures of organs differ across species, shared pathophysiological mechanisms manifest as comparable relative spectral changes across species. Such changes learnt in one species can thus be transferred to a new species via a novel "physiology-based data augmentation" method, enabling the large-scale secondary use of preclinical animal data for humans. The resulting ethical, monetary, and performance benefits of the proposed knowledge transfer paradigm promise a high impact of the methodology on future developments in the field., Comment: Jan Sellner and Alexander Studier-Fischer contributed equally to this work

The proximity of the Large and Small Magellanic Clouds (LMC and SMC) provides the opportunity to study the impact of dwarf-dwarf interactions on their mass assembly with a unique level of detail. To this end, we analyze two-filter broadband imaging of 83 Hubble Space Telescope (HST) pointings covering 0.203 deg$^2$ towards the SMC, extending out to $\sim$3.5 kpc in projection from its optical center. Lifetime star formation histories (SFHs) fit to each pointing independently reveal an outside-in age gradient such that fields in the SMC outskirts are older on average. We measure radial gradients of the lookback time to form 90%, 75% and 50% of the cumulative stellar mass for the first time, finding $\delta$($\tau_{90}$, $\tau_{75}$, $\tau_{50}$)/$\delta$R = (0.61$^{+0.08}_{-0.07}$, 0.65$^{+0.09}_{-0.08}$, 0.82$^{+0.12}_{-0.16}$) Gyr/kpc assuming PARSEC evolutionary models and a commonly used elliptical geometry of the SMC, although our results are robust to these assumptions. The wing of the SMC deviates from this trend, forming 25\% of its cumulative mass over the most recent 3 Gyr due to a best-fit star formation rate that remains approximately constant. Our results are consistent with chemodynamical evidence of a tidally stripped SMC component in the foreground, and imply contributions to the observed SFH from multiple previous LMC-SMC interactions. We also compare our SMC SFH with results from a companion study of the LMC, finding that while the two galaxies present different internal, spatially resolved SFH trends, both the LMC and SMC have similar near-constant lifetime SFHs when viewed globally., Comment: ApJ in press. 40 pages, 18 figures, 5 tables including Appendices

The proximity of the Magellanic Clouds provides the opportunity to study interacting dwarf galaxies near a massive host, and spatial trends in their stellar population properties in particular, with a unique level of detail. The Scylla pure parallel program has obtained deep (80% complete to >1 mag below the ancient main sequence turnoff), homogeneous two-filter Hubble Space Telescope (HST) imaging sampling the inner star-forming disk of the Large Magellanic Cloud (LMC), the perfect complement to shallower, contiguous ground-based surveys. We harness this imaging together with extant archival data and fit lifetime star formation histories (SFHs) to resolved color-magnitude diagrams (CMDs) of 111 individual fields, using three different stellar evolutionary libraries. We validate per-field recovered distances and extinctions as well as the combined global LMC age-metallicity relation and SFH against independent estimates. We find that the present-day radial age gradient reverses from an inside-out gradient in the inner disk to an outside-in gradient beyond $\sim$2 disk scalelengths, supported by ground-based measurements. The gradients become relatively flatter at earlier lookback times, while the location of the inversion remains constant over an order of magnitude in lookback time, from $\sim$1$-$10 Gyr. This suggests at least one mechanism that predates the recent intense LMC-SMC interaction. We compare observed radial age trends to other late-type galaxies at fixed stellar mass and discuss similarities and differences in the context of potential drivers, implying strong radial migration in the LMC., Comment: ApJ in press. 45 pages, 17 figures, 9 tables including Appendices

Scylla is a deep Hubble Space Telescope survey of the stellar populations, interstellar medium and star formation in the LMC and SMC. As a pure-parallel complement to the Ultraviolet Legacy Library of Young Stars as Essential Standards (ULLYSES) survey, Scylla obtained 342 orbits of ultraviolet (UV) through near-infrared (IR) imaging of the LMC and SMC with Wide Field Camera 3. In this paper, we describe the science objectives, observing strategy, data reduction procedure, and initial results from our photometric analysis of 96 observed fields. Although our observations were constrained by ULYSSES primary exposures, we imaged all fields in at least two filters (F475W and F814W), and 64% of fields in at least three and as many as seven WFC3 filters spanning the UV to IR. Overall, we reach average 50% completeness of $m_{\rm F225W}=26.0$, $m_{\rm F275W}=26.2$, $m_{\rm F336W}=26.9$, $m_{\rm F475W}=27.8$, $m_{\rm F814W}=25.5$, $m_{\rm F110W}=24.7$, and $m_{\rm F160W}=24.0$ Vega magnitudes in our photometric catalogs, which is faintward of the ancient main sequence turnoff in all filters. The primary science goals of Scylla include characterizing the structure and properties of dust in the MCs, as well as their spatially-resolved star formation and chemical enrichment histories. Our images and photometric catalogs, which represent the widest-area coverage of MCs with HST photometry to date, are available as a high-level science product at the Barbara A. Mikulski Archive for Space Telescopes., Comment: 25 pages, 16 figures, 8 tables. Accepted for publication in ApJS

6G networks will be highly dynamic, re-configurable, and resilient. To enable and support such features, employing AI has been suggested. Integrating AIin networks will likely require distributed AI deployments with resilient connectivity, e.g., for communication between RL agents and environment. Such approaches need to be validated in realistic network environments. In this demo, we use ContainerNet to emulate AI-capable and autonomic networks that employ the routing protocol KIRA to provide resilient connectivity and service discovery. As an example AI application, we train and infer deep RL agents learning medium access control (MAC) policies for a wireless network environment in the emulated network., Comment: Accepted for presentation in the Demo Session at the IEEE International Conference on Network Protocols (ICNP), 2024