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Error control by means of a posteriori error estimators or indica-tors and adaptive discretizations, such as adaptive mesh refinement, have emerged in the late seventies. Since then, numerous theoretical developments and improvements have been made, as well as the first attempts to introduce them into real-life industrial applications. The present introductory chapter provides an overview of the subject, highlights some of the achievements to date and discusses possible perspectives.

Realization of efficient spin-orbit torque switching of the N\'eel vector in insulating antiferromagnets is a challenge, often complicated by spurious effects. Quantifying the spin-orbit torques in antiferromagnet/heavy metal heterostructures is an important first step towards this goal. Here, we employ magneto-optic techniques to study damping-like spin-orbit torque (DL-SOT) in a-plane $\alpha$-Fe$_2$O$_3$ (hematite) with a Pt spin-orbit overlayer. We find that the DL-SOT efficiency is two orders of magnitude larger than reported in c- and r-plane hematite/Pt using harmonic Hall techniques. The large magnitude of DL-SOT is supported by direct imaging of current-induced motion of antiferromagnetic domains that happens at moderate current densities. Our study introduces a new method for quantifying spin-orbit torque in antiferromagnets with a small canted moment and identifies a-plane $\alpha$-Fe$_2$O$_3$ as a promising candidate to realize efficient SOT switching., Comment: 6 pages, 3 figures

Vision-language models have shown impressive progress in recent years. However, existing models are largely limited to turn-based interactions, where each turn must be stepped (i.e., prompted) by the user. Open-ended, asynchronous interactions, where an AI model may proactively deliver timely responses or feedback based on the unfolding situation in real-time, are an open challenge. In this work, we present the QEVD benchmark and dataset, which explores human-AI interaction in the challenging, yet controlled, real-world domain of fitness coaching -- a task which intrinsically requires monitoring live user activity and providing immediate feedback. The benchmark requires vision-language models to recognize complex human actions, identify possible mistakes, and provide appropriate feedback in real-time. Our experiments reveal the limitations of existing state-of-the-art vision-language models for such asynchronous situated interactions. Motivated by this, we propose a simple end-to-end streaming baseline that can respond asynchronously to human actions with appropriate feedback at the appropriate time., Comment: Accepted to the 2024 NeurIPS Datasets and Benchmarks track; data and code are available at: https://www.qualcomm.com/developer/software/qevd-dataset and https://github.com/Qualcomm-AI-research/FitCoach

We present the discovery of TOI 762 A b and TIC 46432937 b, two giant planets transiting M dwarf stars. Transits of both systems were first detected from observations by the NASA TESS mission, and the transiting objects are confirmed as planets through high-precision radial velocity (RV) observations carried out with VLT/ESPRESSO. TOI 762 A b is a warm sub-Saturn with a mass of 0.251 +- 0.042 M_J, a radius of 0.744 +- 0.017 R_J, and an orbital period of 3.4717 d. It transits a mid-M dwarf star with a mass of 0.442 +- 0.025 M_S and a radius of 0.4250 +- 0.0091 R_S. The star TOI 762 A has a resolved binary star companion TOI 762 B that is separated from TOI 762 A by 3.2" (~ 319 AU) and has an estimated mass of 0.227 +- 0.010 M_S. The planet TIC 46432937 b is a warm Super-Jupiter with a mass of 3.20 +- 0.11 M_J and radius of 1.188 +- 0.030 R_J. The planet's orbital period is P = 1.4404 d, and it undergoes grazing transits of its early M dwarf host star, which has a mass of 0.563 +- 0.029 M_S and a radius of 0.5299 +- 0.0091 R_S. TIC 46432937 b is one of the highest mass planets found to date transiting an M dwarf star. TIC 46432937 b is also a promising target for atmospheric observations, having the highest Transmission Spectroscopy Metric or Emission Spectroscopy Metric value of any known warm Super-Jupiter (mass greater than 3.0 M_J, equilibrium temperature below 1000 K)., Comment: 29 pages, 7 figures, 8 tables, accepted for publication in AAS Journals

The SLAM paper demonstrated that on-device Small Language Models (SLMs) are a viable and cost-effective alternative to API-based Large Language Models (LLMs), such as OpenAI's GPT-4, offering comparable performance and stability. However, SLAM also identified discrepancies between human preferences and traditional auto-evaluators. This follow-up paper explores methods to align LLM evaluator preferences with human evaluations by addressing biases, particularly toward higher token counts. We employed Bayesian statistics and a t-test to quantify this bias and developed a recalibration procedure to adjust the GPTScorer. Our findings significantly improve aligning the recalibrated LLM evaluator with human evaluations across multiple use cases. For instance, spearman's ranking correlation score in the Recommendation use case improved from -27.27 to 44.55. These results highlight the importance of accounting for biases in automated evaluations to ensure fair and accurate model assessments. The recalibration process enhances the reliability of automated evaluators, leading to better AI models that align with human values and expectations. This study provides a robust methodology for future research into bias correction and emphasizes the feasibility and benefits of developing human-aligned AI evaluation systems.

The recent discoveries of Neptune-sized ultra-short period planets (USPs) challenge existing planet formation theories. It is unclear whether these residents of the Hot Neptune Desert have similar origins to smaller, rocky USPs, or if this discrete population is evidence of a different formation pathway altogether. We report the discovery of TOI-3261b, an ultra-hot Neptune with an orbital period $P$ = 0.88 days. The host star is a $V = 13.2$ magnitude, slightly super-solar metallicity ([Fe/H] $\simeq$ 0.15), inactive K1.5 main sequence star at $d = 300$ pc. Using data from the Transiting Exoplanet Survey Satellite and the Las Cumbres Observatory Global Telescope, we find that TOI-3261b has a radius of $3.82_{-0.35}^{+0.42}$ $R_{\oplus}$. Moreover, radial velocities from ESPRESSO and HARPS reveal a mass of $30.3_{-2.4}^{+2.2}$ $M_{\oplus}$, more than twice the median mass of Neptune-sized planets on longer orbits. We investigate multiple mechanisms of mass loss that can reproduce the current-day properties of TOI-3261b, simulating the evolution of the planet via tidal stripping and photoevaporation. Thermal evolution models suggest that TOI-3261b should retain an envelope potentially enriched with volatiles constituting $\sim$5% of its total mass. This is the second highest envelope mass fraction among ultra-hot Neptunes discovered to date, making TOI-3261b an ideal candidate for atmospheric follow-up observations., Comment: 20 pages, 11 figures, accepted to AJ

We theoretically study the conformational and dynamical properties of semiflexible active polar ring polymers under linear shear flow. A ring is described as a continuous Gaussian polymer with a tangential active force of a constant density along its contour. The linear but non-Hermitian equation of motion is solved using an eigenfunction expansion, which yields activity-independent, but shear-rate-dependent, relaxation times and activity-dependent frequencies. As a consequence, the ring's stationary-state properties are independent of activity, and its conformations as well as rheological properties are equal to those of a passive ring under shear. The presence of characteristic time scales by the relaxation and the frequency gives rise to a particular dynamical behavior. A tank-treading-like motion emerges for large relaxation times and high frequencies, specifically for stiffer rings, governed by the activity-dependent frequencies. In the case of very flexible polymers, the relaxation behavior dominates over tank-treading. Shear strongly affects the crossover from a tank-treading to a relaxation-time dominated dynamics and suppresses tank-treading. This is reflected in the tumbling frequency, which exhibits two shear-rate dependent regimes, with an activity-dependent plateau at low shear rates followed by a power-law regime with increasing tumbling frequency for large shear rates., Comment: 7 figures

We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency departments. Our solution consists of a Myrna (TM) Instrument and embedded TriVerity (TM) classifiers. The instrument measures abundances of 29 messenger RNAs in patient's blood, subsequently used as features for machine learning. The classifiers convert the input features to an intuitive test report comprising the separate likelihoods of (1) a bacterial infection (2) a viral infection, and (3) severity (need for Intensive Care Unit-level care). In internal validation, the system achieved AUROC = 0.83 on the three-class disease diagnosis (bacterial, viral, or non-infected) and AUROC = 0.77 on binary prognosis of disease severity. The Myrna, TriVerity system was granted breakthrough device designation by the United States Food and Drug Administration (FDA). This engineering manuscript teaches the standard and novel machine learning methods used to translate an academic research concept to a clinical product aimed at improving patient care, and discusses lessons learned., Comment: 16 pages, 6 figures

Previous theoretical work on contrastive learning (CL) with InfoNCE showed that, under certain assumptions, the learned representations uncover the ground-truth latent factors. We argue these theories overlook crucial aspects of how CL is deployed in practice. Specifically, they assume that within a positive pair, all latent factors either vary to a similar extent, or that some do not vary at all. However, in practice, positive pairs are often generated using augmentations such as strong cropping to just a few pixels. Hence, a more realistic assumption is that all latent factors change, with a continuum of variability across these factors. We introduce AnInfoNCE, a generalization of InfoNCE that can provably uncover the latent factors in this anisotropic setting, broadly generalizing previous identifiability results in CL. We validate our identifiability results in controlled experiments and show that AnInfoNCE increases the recovery of previously collapsed information in CIFAR10 and ImageNet, albeit at the cost of downstream accuracy. Additionally, we explore and discuss further mismatches between theoretical assumptions and practical implementations, including extensions to hard negative mining and loss ensembles.

We use Brownian dynamics simulations to study a model of a cyclic bacterial heat engine based on a harmonically confined colloidal probe particle in a bath formed by active Brownian particles. For intermediate activities, active noise experienced by large enough probes becomes Gaussian with exponential autocorrelation function. We show that, in this experimentally pertinent regime, the probability densities for stochastic work, heat, and efficiency are well represented by those of an effective active Ornstein-Uhlenbeck particle (AOUP). Due to the probe's fast relaxation in the potential in typical experimental implementations, good agreement can prevail even when the noise autocorrelation function develops non-exponential tails. Our results show that the AOUP provides a convenient and accurate, analytically tractable effective model to mimic and analyze experimental bacterial heat engines, especially when operating with comparatively large probes and stiff traps., Comment: 10 pages, 7 figures

Statistical models that involve latent Markovian state processes have become immensely popular tools for analysing time series and other sequential data. However, the plethora of model formulations, the inconsistent use of terminology, and the various inferential approaches and software packages can be overwhelming to practitioners, especially when they are new to this area. With this review-like paper, we thus aim to provide guidance for both statisticians and practitioners working with latent Markov models by offering a unifying view on what otherwise are often considered separate model classes, from hidden Markov models over state-space models to Markov-modulated Poisson processes. In particular, we provide a roadmap for identifying a suitable latent Markov model formulation given the data to be analysed. Furthermore, we emphasise that it is key to applied work with any of these model classes to understand how recursive techniques exploiting the models' dependence structure can be used for inference. The R package LaMa adapts this unified view and provides an easy-to-use framework for very fast (C++ based) numerical maximum likelihood estimation of any of the models discussed in this paper, allowing users to tailor a latent Markov model to their data using a Lego-type approach., Comment: 52 pages, 7 figures

We present a Brownian dynamics simulation of the bacterial Stirling engine studied by Krishnamurthy et al., Nat. Phys. 12, 1134 (2016). In their experimental setup, an overdamped colloid in an optical trap with time-modulated stiffness interacts with a bacterial bath that we represent by an ensemble of overdamped active particles. In the parameter regime of the experiment the thermodynamic performance is governed by an effective temperature and can be parametrized analytically by active Brownian particle models. We quantitatively reproduce the efficiencies reported for the experiments under the assumption that energy exchange with the bath during isochores of the cycle is entirely recuperated and in agreement with the second law., Comment: 12 pages, 7 figures

Cosmic rays travelling through interstellar space have their propagation directions repeatedly scattered by fluctuating interstellar magnetic fields. The nature of this scattering is a major unsolved problem in astrophysics, one that has resisted solution largely due to a lack of direct observational constraints on the scattering rate. Here we show that very high-energy $\gamma$-ray emission from the globular cluster Terzan 5, which has unexpectedly been found to be displaced from the cluster, presents a direct probe of this process. We show that this displacement is naturally explained by cosmic rays accelerated in the bow shock around the cluster propagating a finite distance before scattering processes re-orient enough of them towards Earth to produce a detectable $\gamma$-ray signal. The angular distance between the cluster and the signal places tight constraints on the scattering rate, which we show are consistent with a model whereby scattering is primarily due to excitation of magnetic waves by the cosmic rays themselves. The analysis method we develop here will make it possible to use sources with similarly displaced non-thermal X-ray and TeV $\gamma$-ray signals as direct probes of cosmic ray scattering across a range of Galactic environments., Comment: Accepted to Nature Astronomy; 16 pages, 7 figures; this submission contains the main article, supplementary information, and extended data in a single document

Ransomware presents a significant and increasing threat to individuals and organizations by encrypting their systems and not releasing them until a large fee has been extracted. To bolster preparedness against potential attacks, organizations commonly conduct red teaming exercises, which involve simulated attacks to assess existing security measures. This paper proposes a novel approach utilizing reinforcement learning (RL) to simulate ransomware attacks. By training an RL agent in a simulated environment mirroring real-world networks, effective attack strategies can be learned quickly, significantly streamlining traditional, manual penetration testing processes. The attack pathways revealed by the RL agent can provide valuable insights to the defense team, helping them identify network weak points and develop more resilient defensive measures. Experimental results on a 152-host example network confirm the effectiveness of the proposed approach, demonstrating the RL agent's capability to discover and orchestrate attacks on high-value targets while evading honeyfiles (decoy files strategically placed to detect unauthorized access).

We present a novel approach to achieve spatial variations in the degree of non-covalent functionalization of twisted bilayer graphene (tBLG). The tBLG with twist angles varying between ~ 5{\deg} and 7{\deg} was non-covalently functionalized with 1,4,5,8,9,11-hexaazatriphenylenehexacarbonitrile (HATCN) molecules. Our results show a correlation between the degree of functionalization and the twist angle of tBLG. This correlation was determined through Raman spectroscopy, where areas with larger twist angles exhibited a lower HATCN peak intensity compared to areas with smaller twist angles. We suggest that the HATCN adsorption follows the moir\'e pattern of tBLG by avoiding AA-stacked areas and attach predominantly to areas with a local AB-stacking order of tBLG, forming an overall ABA-stacking configuration. This is supported by density functional theory (DFT) calculations. Our work highlights the role of the moir\'e lattice in controlling the non-covalent functionalization of tBLG. Our approach can be generalized for designing nanoscale patterns on two-dimensional (2D) materials using moir\'e structures as a template.

In real-world industrial environments, modern robots often rely on human operators for crucial decision-making and mission synthesis from individual tasks. Effective and safe collaboration between humans and robots requires systems that can adjust their motion based on human intentions, enabling dynamic task planning and adaptation. Addressing the needs of industrial applications, we propose a motion control framework that (i) removes the need for manual control of the robot's movement; (ii) facilitates the formulation and combination of complex tasks; and (iii) allows the seamless integration of human intent recognition and robot motion planning. For this purpose, we leverage a modular and purely reactive approach for task parametrization and motion generation, embodied by Riemannian Motion Policies. The effectiveness of our method is demonstrated, evaluated, and compared to \remove{state-of-the-art approaches}\add{a representative state-of-the-art approach} in experimental scenarios inspired by realistic industrial Human-Robot Interaction settings., Comment: 9 pages; Robotics, Science and Systems (RSS) 2024

Systems of motile microorganisms exhibit a multitude of collective phenomena, including motility-induced phase separation and turbulence. Sensing of the environment and adaptation of movement plays an essential role in the emergent behavior. We study the collective motion of wet self-steering polar microswimmers, which align their propulsion direction hydrodynamically with that of their neighbors, by mesoscale hydrodynamics simulations. The simulations of the employed squirmer model reveal a distinct dependence on the swimmer flow field, i.e., pullers versus pushers. The collective motion of pushers is characterized by active turbulence, with nearly homogeneous density and a Gaussian velocity distribution. Pullers exhibit a strong tendency for clustering and display velocity and vorticity distributions with fat exponential tails; their dynamics is chaotic, with a temporal appearance of vortex rings and fluid jets. Our results show that the collective behavior of intelligent microswimmers is very diverse and still offers many surprises to be discovered., Comment: 6 figures

We give a rigorous proof for the existence of a finite-energy, self-similar solution to the focusing cubic Schr\"odinger equation in three spatial dimensions., Comment: 77 pages, now with data files included

We define the Schwarzian Field Theory as a finite measure on $\mathrm{Diff}^1(\mathbb{T})/\mathrm{PSL}(2,\mathbb{R})$ and compute its generalised partition functions exactly using methods of stochastic analysis. Our results rigorously implement an approach by Belokurov--Shavgulidze. The Schwarzian Field Theory has attracted recent attention due to its role in the analysis of the Sachdev--Ye--Kitaev model and as the proposed holographic dual to Jackiw--Teitelboim gravity. In two companion papers by Losev, the predicted exact cross-ratio correlation functions for non-crossing Wilson lines and the large deviations are derived from the probability measure.

We calculate the Fuglede-Kadison determinant of arbitrary matrix-valued semicircular operators in terms of the capacity of the corresponding covariance mapping. We also improve a lower bound by Garg, Gurvits, Oliveira, and Widgerson on this capacity, by making it dimension-independent.

We present HTMPC, a Heavily Templated C++ library for large-scale simulations implementing multi-particle collision dynamics (MPC), a particle-based mesoscale hydrodynamic simulation method. The implementation is plugin-based, and designed for distributed computing over an arbitrary number of MPI ranks. By abstracting the hardware-dependent parts of the implementation, we provide an identical application-code base for various architectures, currently supporting CPUs and CUDA-capable GPUs. We have examined the code for a system of more than a trillion MPC particles distributed over a few thousand MPI ranks (GPUs), demonstrating the scalability of the implementation and its applicability to large-scale hydrodynamic simulations. As showcases, we examine passive and active suspension of colloids, which confirms the extensibility and versatility of our plugin-based implementation., Comment: 7 figures

BlueMUSE is a blue-optimised, medium spectral resolution, panoramic integral field spectrograph under development for the Very Large Telescope (VLT). With an optimised transmission down to 350 nm, spectral resolution of R$\sim$3500 on average across the wavelength range, and a large FoV (1 arcmin$^2$), BlueMUSE will open up a new range of galactic and extragalactic science cases facilitated by its specific capabilities. The BlueMUSE consortium includes 9 institutes located in 7 countries and is led by the Centre de Recherche Astrophysique de Lyon (CRAL). The BlueMUSE project development is currently in Phase A, with an expected first light at the VLT in 2031. We introduce here the Top Level Requirements (TLRs) derived from the main science cases, and then present an overview of the BlueMUSE system and its subsystems fulfilling these TLRs. We specifically emphasize the tradeoffs that are made and the key distinctions compared to the MUSE instrument, upon which the system architecture is built., Comment: 20 pages, 10 figures, proceedings of the SPIE astronomical telescopes and instrumentation conference, Yokohama, 16-21 June

Volume-phase holographic gratings (VPHGs) are widely used in astronomical spectrographs due to their adaptability and high diffraction efficiency. Most VPHGs in operation use dichromated gelatin as a recording material, whose performance is sensitive to the coating and development process, especially in the near-UV. In this letter, we present the characterization of two UV-blue VPHG prototypes for the BlueMUSE integral field spectrograph on the VLT, based on dichromated gelatin and the Bayfol$\circledR$HX photopolymer film as recording materials. Our measurements show that both prototypes meet the required diffraction efficiency and exhibit similar performance with a wavelength-average exceeding 70% in the 350-580 nm range. Deviations from theoretical models increase towards 350 nm, consistently with previous studies on similar gratings. We also report similar performances in terms spatial uniformity and grating-to-grating consistency. Likewise, no significant differences in wavefront error or scattered light are observed between the prototypes., Comment: 7 pages, 5 figures, 1 table. Presented at SPIE "Astronomical Telescopes and Instrumentation", June 2024, in Yokohama, as part of "Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation VI"

We investigate the dynamics of a gas of ultracold atoms that are trapped in an array of harmonic potentials and that interacts with a Bose-Einstein condensate (BEC) that acts as a reservoir of Bogoliubov excitations. The ground and excited energy levels of these trapped ultracold atoms are coupled to each other via detuned Raman lasers with corresponding Rabi frequencies. Once excited via the Raman lasers, these trapped ultracold atoms then return to their ground energy levels, but not necessarily to their original trap locations, by emitting Bogoliubov excitations into the BEC. This combination of driving via Raman lasers to excited energy levels and dissipation via interaction with the BEC resulting in emission of Bogoliubov excitations into it will result in the trapped ultracold atoms approaching a steady state, whereby the expectation value of the number of trapped ultracold atoms in each harmonic trap of the array will attain a constant value over time. One can then use this system to prepare states that require a definite number of atoms in a particular energy level, such as BECs and atom lasers used for atom interferometry and for tests of the foundations of quantum mechanics., Comment: 42 pages, 14 figures. Revised version uploaded

We present AsterX, a novel open-source, GPU-accelerated, fully general relativistic magnetohydrodynamic (GRMHD) code designed for dynamic spacetimes in 3D Cartesian coordinates, and tailored for exascale computing. We utilize block-structured adaptive mesh refinement (AMR) through CarpetX, the new driver for the Einstein Toolkit, which is built on AMReX, a software framework for massively parallel applications. AsterX employs the Valencia formulation for GRMHD, coupled with the 'Z4c' formalism for spacetime evolution, while incorporating high resolution shock capturing schemes to accurately handle the hydrodynamics. AsterX has undergone rigorous testing in both static and dynamic spacetime, demonstrating remarkable accuracy and agreement with other codes in literature. Benchmarking the code through scaling tests on OLCF's Frontier supercomputer, we demonstrate a weak scaling efficiency of about 67%-77% on 4096 nodes compared to an 8-node performance., Comment: 41 pages, 12 figures, to be submitted to Classical and Quantum Gravity

This works reports evidence for the topological signatures of ambiguity in sentence embeddings that could be leveraged for ranking and/or explanation purposes in the context of vector search and Retrieval Augmented Generation (RAG) systems. We proposed a working definition of ambiguity and designed an experiment where we have broken down a proprietary dataset into collections of chunks of varying size - 3, 5, and 10 lines and used the different collections successively as queries and answers sets. It allowed us to test the signatures of ambiguity with removal of confounding factors. Our results show that proxy ambiguous queries (size 10 queries against size 3 documents) display different distributions of homologies 0 and 1 based features than proxy clear queries (size 5 queries against size 10 documents). We then discuss those results in terms increased manifold complexity and/or approximately discontinuous embedding submanifolds. Finally we propose a strategy to leverage those findings as a new scoring strategy of semantic similarities.

This paper presents an active inference method for Extended Finite State Machines, where inputs and outputs are parametrized, and transitions can be conditioned by guards involving input parameters and internal variables called registers. The method applies to (software) systems that cannot be reset, so it learns an EFSM model of the system on a single trace., Comment: 14 pages (last page blank), 8 figures, 4 algorithms Submitted to LearnAut workshop 2024 (not published)

We present an experimental investigation of coherent crosstalk cancellation methods for light delivered to a linear ion chain cryogenic quantum register. The ions are individually addressed using focused laser beams oriented perpendicular to the crystal axis, which are created by imaging each output of a multi-core photonic-crystal fibre waveguide array onto a single ion. The measured nearest-neighbor native crosstalk intensity of this device for ions spaced by 5 $\mu$m is found to be $\sim 10^{-2}$. We show that we can suppress this intensity crosstalk from waveguide channel coupling and optical diffraction effects by a factor $>10^3$ using cancellation light supplied to neighboring channels which destructively interferes with the crosstalk. We measure a rotation error per gate on the order of $\epsilon_{x} \sim 10^{-5}$ on spectator qubits, demonstrating a suppression of crosstalk error by a factor of $> 10^2$. We compare the performance to composite pulse methods for crosstalk cancellation, and describe the appropriate calibration methods and procedures to mitigate phase drifts between these different optical paths, including accounting for problems arising due to pulsing of optical modulators.

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

The ability to traverse an unknown environment is crucial for autonomous robot operations. However, due to the limited sensing capabilities and system constraints, approaching this problem with a single robot agent can be slow, costly, and unsafe. For example, in planetary exploration missions, the wear on the wheels of a rover from abrasive terrain should be minimized at all costs as reparations are infeasible. On the other hand, utilizing a scouting robot such as a micro aerial vehicle (MAV) has the potential to reduce wear and time costs and increasing safety of a follower robot. This work proposes a novel cooperative IPP framework that allows a scout (e.g., an MAV) to efficiently explore the minimum-cost-path for a follower (e.g., a rover) to reach the goal. We derive theoretic guarantees for our algorithm, and prove that the algorithm always terminates, always finds the optimal path if it exists, and terminates early when the found path is shown to be optimal or infeasible. We show in thorough experimental evaluation that the guarantees hold in practice, and that our algorithm is 22.5% quicker to find the optimal path and 15% quicker to terminate compared to existing methods., Comment: 8 pages, 9 figures, code will be available at https://github.com/ethz-asl/scouting-ipp

We present a framework for quantum computation, similar to Adiabatic Quantum Computation (AQC), that is based on the quantum Zeno effect. By performing randomised dephasing operations at intervals determined by a Poisson process, we are able to track the eigenspace associated to a particular eigenvalue. We derive a simple differential equation for the fidelity, leading to general theorems bounding the time complexity of a whole class of algorithms. We also use eigenstate filtering to optimise the scaling of the complexity in the error tolerance $\epsilon$. In many cases the bounds given by our general theorems are optimal, giving a time complexity of $O(1/\Delta_m)$ with $\Delta_m$ the minimum of the gap. This allows us to prove optimal results using very general features of problems, minimising the problem-specific insight necessary. As two applications of our framework, we obtain optimal scaling for the Grover problem (i.e.\ $O(\sqrt{N})$ where $N$ is the database size) and the Quantum Linear System Problem (i.e.\ $O(\kappa\log(1/\epsilon))$ where $\kappa$ is the condition number and $\epsilon$ the error tolerance) by direct applications of our theorems., Comment: 19 pages

Continuous action spaces in reinforcement learning (RL) are commonly defined as multidimensional intervals. While intervals usually reflect the action boundaries for tasks well, they can be challenging for learning because the typically large global action space leads to frequent exploration of irrelevant actions. Yet, little task knowledge can be sufficient to identify significantly smaller state-specific sets of relevant actions. Focusing learning on these relevant actions can significantly improve training efficiency and effectiveness. In this paper, we propose to focus learning on the set of relevant actions and introduce three continuous action masking methods for exactly mapping the action space to the state-dependent set of relevant actions. Thus, our methods ensure that only relevant actions are executed, enhancing the predictability of the RL agent and enabling its use in safety-critical applications. We further derive the implications of the proposed methods on the policy gradient. Using proximal policy optimization (PPO), we evaluate our methods on four control tasks, where the relevant action set is computed based on the system dynamics and a relevant state set. Our experiments show that the three action masking methods achieve higher final rewards and converge faster than the baseline without action masking.

The quasi-two-dimensional magnetic topological insulator MnBi$_2$Se$_4$, stabilized via non-equilibrium molecular beam epitaxy, is investigated by resonant soft x-ray scattering. Kiessig fringes are observed, confirming a high sample quality and a thin film thickness of 10 septuple layers ($\sim$13 nm). An antiferromagnetic Bragg peak is observed at the structurally forbidden reflection, whose magnetic nature is validated by studying its temperature, energy, and polarization dependence. Through a detailed analysis, an A-type antiferromagetic order with in-plane moments is implied. This alternative spin structure in MnBi$_2$Se$_4$, in contrast to the Ising antiferromagnetic states in other magnetic topological insulators, might be relevant for hosting new topological states.

The omega-regular separability problem for B\"uchi VASS coverability languages has recently been shown to be decidable, but with an EXPSPACE lower and a non-primitive recursive upper bound -- the exact complexity remained open. We close this gap and show that the problem is EXPSPACE-complete. A careful analysis of our complexity bounds additionally yields a PSPACE procedure in the case of fixed dimension >= 1, which matches a pre-established lower bound of PSPACE for one dimensional B\"uchi VASS. Our algorithm is a non-deterministic search for a witness whose size, as we show, can be suitably bounded. Part of the procedure is to decide the existence of runs in VASS that satisfy certain non-linear properties. Therefore, a key technical ingredient is to analyze a class of systems of inequalities where one variable may occur in non-linear (polynomial) expressions. These so-called singly non-linear systems (SNLS) take the form A(x).y >= b(x), where A(x) and b(x) are a matrix resp. a vector whose entries are polynomials in x, and y ranges over vectors in the rationals. Our main contribution on SNLS is an exponential upper bound on the size of rational solutions to singly non-linear systems. The proof consists of three steps. First, we give a tailor-made quantifier elimination to characterize all real solutions to x. Second, using the root separation theorem about the distance of real roots of polynomials, we show that if a rational solution exists, then there is one with at most polynomially many bits. Third, we insert the solution for x into the SNLS, making it linear and allowing us to invoke standard solution bounds from convex geometry. Finally, we combine the results about SNLS with several techniques from the area of VASS to devise an EXPSPACE decision procedure for omega-regular separability of B\"uchi VASS.

The transport of spin currents across interfaces is relatively well studied, while the transport properties of orbital currents are just starting to be examined. In Cr/Ni bilayers, the spin-orbit torque (SOT) due to the orbital current generated in the Cr layer is believed to dominate over torques of other origins. In this work, we study SOT in Cr/X/Ni trilayers, where X is an ultra-thin spacer of a different material. Using the SOT as a proxy for the orbital current transferred from the Cr to the Ni layer, we compare Cr/X/Ni results to Pt/X/Ni, the system in which spin current generated in the Pt layer plays a dominant role. We find that across 12 different spacers the apparent interface transparency to the orbital current is comparable or larger than to the spin current.

Let $\mathcal{M}_{C}(2, 0)$ be the moduli space of semistable rank two and degree zero Higgs bundles on a smooth complex hyperelliptic curve $C$ of genus three. We prove that the quotient of $\mathcal{M}_{C}(2, 0)$ by a twisted version of the hyperelliptic involution is an 18-dimensional holomorphic symplectic variety admitting a crepant resolution, whose local model was studied by Kaledin and Lehn to describe O'Grady's singularities. Similarly, by considering the moduli space of Higgs bundles with trivial determinant $\mathcal{M}_C(2, \mathcal{O}_{C})\subseteq \mathcal{M}_C(2, 0)$, we show that the quotient of $\mathcal{M}_C(2, \mathcal{O}_{C})$ by the hyperelliptic involution is a 12-dimensional holomorphic symplectic variety admitting a crepant resolution.

The integration of observational data into numerical models, known as data assimilation (DA), is fundamental for making Numerical Weather Prediction (NWP) possible, with breathtaking success over the past 60 years (Bauer et al. 2015). Traditional DA methods, such as variational techniques and ensemble Kalman filters, are basic pillars of current NWP by incorporating diverse observational data. However, the emergence of artificial intelligence (AI) presents new opportunities for further improvements. AI-based approaches can emulate the complex computations of traditional NWP models at a reduced computational cost, offering the potential to speed up and improve analyses and forecasts dramatically (e.g. Pathak et al., 2022; Bi et al., 2023; Lam et al., 2023; Bouallegue et al., 2023). AI itself plays a growing role in optimization (e.g. Fan et al., 2024), which offers new possibilities also beyond model emulation. In this paper, we introduce a novel AI-based variational DA approach designed to replace classical methods of DA by leveraging deep learning techniques. Unlike previous hybrid approaches, our method integrates the DA process directly into a neural network, utilizing the variational DA framework. This innovative AI-based system, termed AI-Var, employs a neural network trained to minimize the variational cost function, enabling it to perform DA without relying on pre-existing analysis datasets. We present a proof-of-concept implementation of this approach, demonstrating its feasibility through a series of idealized and real-world test cases. Our results indicate that the AI-Var system can efficiently assimilate observations and produce accurate initial conditions for NWP, highlighting its potential to carry out the DA process in weather forecasting. This advancement paves the way for fully data-driven NWP systems, offering a significant leap forward in computational efficiency.

The majority of coastal flows are characterized by turbulence, rendering the application of shallow water equations an inadequate approach for their accurate description. This paper presents a theory for characterizing accelerated coastal flows, offering an enhanced representation of turbulent flows in the long-wave approximation. The fundamental premise of the theory is as follows: Fluctuating velocities present in the water flow are found to correlate and generate additional motion (or fluctuating motion), which is associated with a velocity of the same order and regularity as the mean flow velocity. The resulting high-order long-wave model is both stable and efficient, comprising classical shallow water equations and additional equations that describe the transport of kinetic energy due to turbulence. The model's detailed eigenstructure and Rankine-Hugoniot relations, steady state solutions are presented in this paper. The proposed theory shows promise for addressing a variety of engineering problems, including roll waves, hydraulic jumps, dam breaks, and flooding. The derived model can accurately address the problem of the mixing layer interaction with a free surface and its transition into a turbulent surface jet. Recent application to sediment transport gives interesting results and good numerical convergences.