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The inner movement of a trebuchet with swinging counterweight was measured inside a laboratory by the use of rotation sensors to determine angular coordinates for beam, counterweight and sling. Data collection was started before the trebuchet was fired and lasted until some time after the projectile was released and flung into ground right in front of the machine. A single measurement is then sufficient for a semi-empirical determination of longest range and kinetic energy at target as well as mechanical energies and internal forces throughout the entire shot. Loss of mechanical energy was ascertained and attributed to friction. Measurements of actual ranges performed on a flat field with little wind are presented and compared with the semi-empirical determinations and ab initio calculations., Comment: 22 pages, 19 figures

The radius distribution of close-in planets has been observed to have a bimodal distribution with a dearth of planets around ~1.5-2.0 $R_\oplus$ commonly referred to as the ''radius valley''. The origin of the valley is normally attributed to mass-loss process such as photoevaporation or core-powered mass loss. Recent work, however, has suggested that the radius valley may instead arise as a consequence of gas accretion by low-mass planets. In this work we therefore aim to investigate the formation of a primordial radius valley from the formation of planet cores through pebble accretion up until the dissipation of the protoplanetary disc and subsequent contraction of accreted atmospheres. The goal of this work is to explore the conditions for forming a primordial radius valley from first principles of planet formation theory, rather than attempting to explain the detailed structure of the observed valley. We use an analytical model with minimal assumptions to estimate the contraction rate of atmospheres and, indeed, find the formation of a primordial radius valley. The planets smaller than the valley did not reach the pebble isolation mass, which is required for the planets to cool down sufficiently to be able to accrete a significant amount of gas. We also estimate the slopes of the radius gap as a function of orbital period for the intrinsic population as well as for planets with orbital periods <100 days. For the intrinsic population, the radius gap follows the pebble isolation mass and increases with increasing orbital period, while for close-in planets the direction of the slope reverses and decreases with increasing orbital period. We find that planets smaller than the radius valley are predominantly rocky while the population of planets larger than the valley consists of a mixture of rocky and water-rich planets., Comment: 24 Pages, 23 Figures, accepted in A&A

Light emitting diodes have superior performance over most other light sources, but the need for secondary optics to shape their illumination for specific applications yield bulky lighting products. Here, we present an approach to shaping light from incoherent sources, such as light emitting diodes, using non-imaging metasurfaces. We present a theoretical framework and a numerical tool for designing the metasurface phase, and use it to construct a proof-of-principle beam shaping metasurface. We demonstrate the optical performance of the fabricated sample, and perform numerical experiments to investigate the quality of the metasurface design. Our approach bridges the fields of non-imaging optics and metaoptics, and may enable metasurface applications for shaping coherent and incoherent light.

Theoretical work on Temporal Difference (TD) learning has provided finite-sample and high-probability guarantees for data generated from Markov chains. However, these bounds typically require linear function approximation, instance-dependent step sizes, algorithmic modifications, and restrictive mixing rates. We present theoretical findings for TD learning under more applicable assumptions, including instance-independent step sizes, full data utilization, and polynomial ergodicity, applicable to both linear and non-linear functions. \textbf{To our knowledge, this is the first proof of TD(0) convergence on Markov data under universal and instance-independent step sizes.} While each contribution is significant on its own, their combination allows these bounds to be effectively utilized in practical application settings. Our results include bounds for linear models and non-linear under generalized gradients and H\"older continuity., Comment: 12 pages main text

Novel materials drive progress across applications from energy storage to electronics. Automated characterization of material structures with machine learning methods offers a promising strategy for accelerating this key step in material design. In this work, we introduce an autoregressive language model that performs crystal structure prediction (CSP) from powder diffraction data. The presented model, deCIFer, generates crystal structures in the widely used Crystallographic Information File (CIF) format and can be conditioned on powder X-ray diffraction (PXRD) data. Unlike earlier works that primarily rely on high-level descriptors like composition, deCIFer performs CSP from diffraction data. We train deCIFer on nearly 2.3M unique crystal structures and validate on diverse sets of PXRD patterns for characterizing challenging inorganic crystal systems. Qualitative and quantitative assessments using the residual weighted profile and Wasserstein distance show that deCIFer produces structures that more accurately match the target diffraction data when conditioned, compared to the unconditioned case. Notably, deCIFer can achieve a 94% match rate on unseen data. deCIFer bridges experimental diffraction data with computational CSP, lending itself as a powerful tool for crystal structure characterization and accelerating materials discovery., Comment: 24 pages, 17 figures, 6 tables. v2: Figure 8 revision

This paper focuses on the problem of collaborative collision avoidance for autonomous inland ships. Two solutions are provided to solve the problem in a distributed manner. We first present a distributed model predictive control (MPC) algorithm that allows ships to directly negotiate their intention to avoid collision in a synchronous communication framework. Moreover, we introduce a new approach to shape the ship's behavior to follow the waterway traffic regulations. The conditional convergence toward a stationary solution of this algorithm is guaranteed by the theory of the Alternating Direction Method of Multipliers (ADMM). To overcome the problem of asynchronous communication between ships, we adopt a new asynchronous nonlinear ADMM and present an asynchronous distributed MPC algorithm based on it. Several simulations and field experiments show that the proposed algorithms can prevent ship collisions even in complex scenarios.

Dust growth is often indirectly inferred observationally in star-forming environments, theoretically predicted to produce mm-sized particles in circumstellar discs, and also presumably witnessed by the predecessors of the terrestrial meteoritic record. For those reasons it is believed that young gas giants under formation in protoplanetary discs with putative circumplanetary discs (CPDs) surrounding them, such as PDS 70c, should be containing mm-sized particles. We model the spectra of a set of CPDs, which we obtained from radiation hydrodynamic simulations at varying Rosseland opacities kappa_R. The kappa_R from the hydrodynamic simulations are matched with consistent opacity sets of ISM-like composition, but grown to larger sizes. Our high kappa_R hydro data nominally corresponds to 10 mum-sized particles, and our low kappa_R-cases correspond to mm-sized particles. We investigate the resulting broad spectral features at first while keeping the overall optical depth in the planetary envelope constant. Dust growth to size distributions dominated by millimeter particles generally results in broad, featureless spectra with black-body like slopes in the far-infrared, while size distributions dominated by small dust develop steeper slopes in the far-infrared and maintain some features stemming from individual minerals. We find that significant dust growth from microns to millimeters can explain the broad features of the PDS 70c data, when upscaling the dust masses from our simulations by x100. Furthermore our results indicate that the spectral range of 30-500 mum is an ideal hunting ground for broadband features arising from the CPD, but that longer wavelengths observed with ALMA can also be used for massive circumplanetary discs, Comment: 12 pages, 8 figures, Accepted to A&A

The vertical shear instability and the streaming instability are two robust sources of turbulence in protoplanetary disks. The former has been found to induce anisotropic turbulence that is stronger in the vertical than in the radial dimension and to be overall stronger compared to the largely isotropic turbulence caused by the streaming instability. In this study, we shed light on the dust diffusion by the vertical shear instability and the streaming instability separately and together, and in particular on the direction- and scale-dependence of the diffusion. To this end, we employ two-dimensional global models of the two instabilities either in isolation or in combination. The vertical shear instability in isolation diffuses dust more strongly in the vertical direction than the streaming instability in isolation, resulting in a wave-shaped dust layer in our two-dimensional simulations. Compared with this large-scale diffusion, though, our study highlights that the vertical shear instability causes substantially weaker or even negligible small-scale diffusion. We validate this result using previously published three-dimensional simulations. In particular when simulating centimetre-sized dust, the undulating dust layer becomes internally razor-thin. In contrast, the diffusion owing to the streaming instability exhibits only a marginal scale-dependence, with the dust layer possessing a Gaussian shape. In models including both instabilities, the undulating mid-plane layer is broadened to a width set by the intrinsic diffusion level caused by the streaming instability., Comment: 15 pages, 8 figures, 2 tables; accepted for publication in Astronomy & Astrophysics

In this paper we study the evolution of radiative fluxes, flux radii and observable dust masses in protoplanetary discs, in order to understand how these depend on the angular momentum budget and on the assumed heat sources. We use a model that includes the formation and viscous evolution of protoplanetary gas discs, together with the growth and radial drift of the dust component. We find that we are best able to match the observed fluxes and radii of class 0/I discs when we assume (i) an initial total angular momentum budget corresponding to a centrifugal radius of 40 au around solar-like stars, and (ii) inefficient viscous heating. Fluxes and radii of class II discs appear consistent with disc models with angular momentum budgets equivalent to centrifugal radii of both 40 au or 10 au for solar like stars, and with models where viscous heating occurs at either full efficiency or at reduced efficiency. During the first 0.5 Myr of their evolution discs are generally optically thick at a wavelength of 1.3 mm. However, after this discs are optically thin at mm-wavelengths, supporting standard means of dust mass estimates. Using a disc population synthesis model, we then show that the evolution of the cumulative evolution of the observable dust masses agrees well with that observed in young star forming clusters of different ages., Comment: 12 pagers, 12 figures

DevSecOps, as the extension of DevOps with security training and tools, has become a popular way of developing modern software, especially in the Internet of Things arena, due to its focus on rapid development, with short release cycles, involving the user/client very closely. Security classification methods, on the other hand, are heavy and slow processes that require high expertise in security, the same as in other similar areas such as risk analysis or certification. As such, security classification methods are hardly compatible with the DevSecOps culture, which to the contrary, has moved away from the traditional style of penetration testing done only when the software product is in the final stages or already deployed. In this work, we first propose five principles for a security classification to be \emph{DevOps-ready}, two of which will be the focus for the rest of the paper, namely to be tool-based and easy to use for non-security experts, such as ordinary developers or system architects. We then exemplify how one can make a security classification methodology DevOps-ready. We do this through an interaction design process, where we create and evaluate the usability of a tool implementing the chosen methodology. Since such work seems to be new within the usable security community, and even more so in the software development (DevOps) community, we extract from our process a general, three-steps `recipe' that others can follow when making their own security methodologies DevOps-ready. The tool that we build is in itself a contribution of this process, as it can be independently used, extended, and/or integrated by developer teams into their DevSecOps tool-chains. Our tool is perceived (by the test subjects) as most useful in the design phase, but also during the testing phase where the security class would be one of the metrics used to evaluate the quality of their software., Comment: 29 pages of which 7 are appendix with figures

Using the tight-binding model of graphite, incorporating all Slonczewski-Weiss-McClure parameters, we compute the spectrum of two-dimensional states of electrons bound to a stacking fault in Bernal graphite. We find that those bands retain characteristic features of the low-energy bands of a rhombohedral graphene trilayer, which actually represents the lattice structure the fault. Based on the self-consistent analysis of charge and potential distribution across the fault layers, we determine the shape of the Fermi contour for the 2D band, which has the form of three pockets with a hole-like conic dispersion and Dirac points above the Fermi level. The computed frequency of Shubnikov-de Haas oscillations and the cyclotron mass of the fault-bound charge carriers (at the Fermi level) are sufficiently different from the corresponding bulk values in graphite, making such stacking faults identifiable by quantum transport and cyclotron resonance measurements., Comment: 5 pages, 5 figures (main text). 7 pages, 9 figures (supplementary text)

Icy dust particles emerge in star-forming clouds and are subsequently incorporated in protoplanetary disks, where they coagulate into larger pebbles up to mm in size. In the disk midplane, ices are shielded from UV radiation, but moderate levels of disk turbulence can lift small particles to the disk surface, where they can be altered, or destroyed. Nevertheless, studies of comets and meteorites generally find that ices at least partly retained their interstellar medium (ISM) composition before being accreted onto these minor bodies. Here we model this process through hydrodynamical simulations with VSI-driven turbulence in the outer protoplanetary disk. We use the PLUTO code in a 2.5 D global accretion setup and include Lagrangian dust particles of 0.1 and 1 mm sizes. In a post-processing step, we use the RADMC3D code to generate the local UV radiation field to assess the level of ice processing of pebbles. We find that a small fraction ($\sim$17$\%$) of 100 $\mu$m size particles are frequently lifted up to $Z/R=0.2$ which can result in the loss of their pristine composition as their residence time in this layer allows for effective CO and water photodissociation. The larger 1 mm size particles remain UV-shielded in the disk midplane throughout the dynamical evolution of the disk. Our results indicate that the assembly of icy bodies via the accretion of drifting mm-size icy pebbles can explain the presence of pristine ice from the ISM, even in VSI-turbulent disks. Nevertheless, particles $\leq$ 100 $\mu$m experience efficient UV processing and may mix with unaltered icy pebbles, resulting in a less ISM-like composition in the midplane., Comment: Accepted to A&A

The problem of incorporating information from observations received serially in time is widespread in the field of uncertainty quantification. Within a probabilistic framework, such problems can be addressed using standard filtering techniques. However, in many real-world problems, some (or all) of the uncertainty is epistemic, arising from a lack of knowledge, and is difficult to model probabilistically. This paper introduces a possibilistic ensemble Kalman filter designed for this setting and characterizes some of its properties. Using possibility theory to describe epistemic uncertainty is appealing from a philosophical perspective, and it is easy to justify certain heuristics often employed in standard ensemble Kalman filters as principled approaches to capturing uncertainty within it. The possibilistic approach motivates a robust mechanism for characterizing uncertainty which shows good performance with small sample sizes, and can outperform standard ensemble Kalman filters at given sample size, even when dealing with genuinely aleatoric uncertainty.

Morbidelli, Kleine & Nimmo (2024) (MKN) recently published a critical analysis on whether the terrestrial planets in the Solar System formed by rapid pebble accretion or by the classical route of multiple giant impacts between planetary embryos after the dissipation of the protoplanetary disc. They arrive at the conclusion that the terrestrial planets did not form by pebble accretion. Although we welcome debate on this topic, we want to emphasize here several points that we disagree on. We will not address in detail every claim made in MKN, but rather stick to four main points. Our conclusion is that pebble accretion remains a viable mechanism to drive significant growth of protoplanets in the protoplanetary disc, with as much as 70% of Earth formed by pebble accretion. This rapid growth phase must nevertheless have been followed by an extended period of collisional growth after the end of the protoplanetary disc phase, likely culminating with the moon-forming giant impact. We emphasize here an important recent result from Olson & Sharp (2023), namely that significant growth by pebble accretion can be reconciled with the Hf-W decay system even for a canonical moon-forming giant impact with a Mars-mass protoplanet and a low equilibration efficiency - a more massive impactor, as proposed in Johansen et al. (2023), is not necessary. Given that terrestrial planet formation naturally involves both pebble accretion and a combination of small and large impactors, this challenges the very notion of making an either/or distinction between the classical collision model and the pebble accretion model., Comment: Comment on arXiv:2411.09271 (slight correction to abstract here), 6 pages, 1 figure, comments and questions welcome

We investigate the effectiveness of Explainable AI (XAI) in verifying Machine Unlearning (MU) within the context of harbor front monitoring, focusing on data privacy and regulatory compliance. With the increasing need to adhere to privacy legislation such as the General Data Protection Regulation (GDPR), traditional methods of retraining ML models for data deletions prove impractical due to their complexity and resource demands. MU offers a solution by enabling models to selectively forget specific learned patterns without full retraining. We explore various removal techniques, including data relabeling, and model perturbation. Then, we leverage attribution-based XAI to discuss the effects of unlearning on model performance. Our proof-of-concept introduces feature importance as an innovative verification step for MU, expanding beyond traditional metrics and demonstrating techniques' ability to reduce reliance on undesired patterns. Additionally, we propose two novel XAI-based metrics, Heatmap Coverage (HC) and Attention Shift (AS), to evaluate the effectiveness of these methods. This approach not only highlights how XAI can complement MU by providing effective verification, but also sets the stage for future research to enhance their joint integration., Comment: ICPRW2024

This paper examines the integration of real-time talking-head generation for interviewer training, focusing on overcoming challenges in Audio Feature Extraction (AFE), which often introduces latency and limits responsiveness in real-time applications. To address these issues, we propose and implement a fully integrated system that replaces conventional AFE models with Open AI's Whisper, leveraging its encoder to optimize processing and improve overall system efficiency. Our evaluation of two open-source real-time models across three different datasets shows that Whisper not only accelerates processing but also improves specific aspects of rendering quality, resulting in more realistic and responsive talking-head interactions. These advancements make the system a more effective tool for immersive, interactive training applications, expanding the potential of AI-driven avatars in interviewer training., Comment: 16 pages, 6 figures, 3 tables. submitted to MDPI journal in as Big Data and Cognitive Computing

The development of programmable quantum devices can be measured by the complexity of manybody states that they are able to prepare. Among the most significant are topologically ordered states of matter, which enable robust quantum information storage and processing. While topological orders are more readily accessible with qudits, experimental realisations have thus far been limited to lattice models of qubits. Here, we prepare a ground state of the Z3 toric code state on 24 qutrits in a trapped ion quantum processor with fidelity per qutrit exceeding 96.5(3)%. We manipulate two types of defects which go beyond the conventional qubit toric code: a parafermion, and its bound state which is related to charge conjugation symmetry. We further demonstrate defect fusion and the transfer of entanglement between anyons and defects, which we use to control topological qutrits. Our work opens up the space of long-range entangled states with qudit degrees of freedom for use in quantum simulation and universal error-correcting codes., Comment: 8+20 pages, 15 figures

The increased brightness temperature of young rocky protoplanets during their magma ocean epoch makes them potentially amenable to atmospheric characterization to distances from the solar system far greater than thermally equilibrated terrestrial exoplanets, offering observational opportunities for unique insights into the origin of secondary atmospheres and the near surface conditions of prebiotic environments. The Large Interferometer For Exoplanets (LIFE) mission will employ a space-based mid-infrared nulling interferometer to directly measure the thermal emission of terrestrial exoplanets. Here, we seek to assess the capabilities of various instrumental design choices of the LIFE mission concept for the detection of cooling protoplanets with transient high-temperature magma ocean atmospheres, in young stellar associations in particular. Using the LIFE mission instrument simulator (LIFEsim) we assess how specific instrumental parameters and design choices, such as wavelength coverage, aperture diameter, and photon throughput, facilitate or disadvantage the detection of protoplanets. We focus on the observational sensitivities of distance to the observed planetary system, protoplanet brightness temperature using a blackbody assumption, and orbital distance of the potential protoplanets around both G- and M-dwarf stars. Our simulations suggest that LIFE will be able to detect (S/N $\geq$ 7) hot protoplanets in young stellar associations up to distances of $\approx$100 pc from the solar system for reasonable integration times (up to $\sim$hours). Detection of an Earth-sized protoplanet orbiting a solar-sized host star at 1 AU requires less than 30 minutes of integration time. M-dwarfs generally need shorter integration times. The contribution from wavelength regions $<$6 $\mu$m is important for decreasing the detection threshold and discriminating emission temperatures., Comment: 18 pages, 19 figures; accepted for publication in A&A

The streaming instability is a mechanism whereby pebble-sized particles in protoplanetary discs spontaneously come together in dense filaments, which collapse gravitationally to form planetesimals upon reaching the Roche density. The extent of the filaments along the orbital direction is nevertheless poorly characterised, due to a focus in the literature on small simulation domains where the behaviour of the streaming instability on large scales cannot be determined. We present here computer simulations of the streaming instability in boxes with side lengths up to 6.4 scale heights in the plane. This is 32 times larger than typically considered simulation domains and nearly a factor 1,000 times the volume. We show that the azimuthal extent of filaments in the non-linear state of the streaming instability is limited to approximately one gas scale height. The streaming instability will therefore not transform the pebble density field into axisymmetric rings; rather the non-linear state of the streaming instability appears as a complex structure of loosely connected filaments. Including the self-gravity of the pebbles, our simulations form up to 4,000 planetesimals. This allows us to probe the high-mass end of the initial mass function of planetesimals with much higher statistical confidence than previously. We find that this end is well-described by a steep exponential tapering. Since the resolution of our simulations is moderate -- a necessary trade-off given the large domains -- the mass distribution is incomplete at the low-mass end. When putting comparatively less weight on the numbers at low masses, at intermediate masses we nevertheless reproduce the power-law shape of the distribution established in previous studies., Comment: 15 pages, 14 figures, 2 tables; accepted for publication in A&A

Motivated by a recent method for approximate solution of Fredholm equations of the first kind, we develop a corresponding method for a class of Fredholm equations of the \emph{second kind}. In particular, we consider the class of equations for which the solution is a probability measure. The approach centres around specifying a functional whose gradient flow admits a minimizer corresponding to a regularized version of the solution of the underlying equation and using a mean-field particle system to approximately simulate that flow. Theoretical support for the method is presented, along with some illustrative numerical results.

Rocky planets may acquire a primordial atmosphere by outgassing of volatiles from their magma ocean. The distribution of O between H$_2$O, CO and CO$_2$ in chemical equilibrium subsequently changes significantly with decreasing temperature. We explore here two chemical models: one where CH$_4$ and NH$_3$ are assumed to be irrevocably destroyed by photolysis, and one where these molecules persist. In the first case, we show that CO cannot co-exist with H$_2$O, since CO oxidizes at low temperatures to form CO$_2$ and H$_2$. In both cases, H escapes from the thermosphere within a few ten million years by absorption of stellar XUV radiation. This escape drives an atmospheric self-oxidation process whereby rocky planet atmospheres become dominated by CO$_2$ and H$_2$O, regardless of their initial oxidation state at outgassing. HCN is considered a potential precursor of prebiotic compounds and RNA. Our oxidizing atmospheres are inefficient at producing HCN by lightning. Instead, we demonstrate that lightning-produced NO, which dissolves as nitrate in the oceans, and interplanetary dust particles may be the main sources of fixed nitrogen to emerging biospheres. Our results highlight the need for origin-of-life scenarios where the first metabolism fixes its C from CO$_2$, rather than from HCN and CO., Comment: Accepted for publication in Astrobiology

We present here results of numerical simulations of the formation and early evolution of rocky planets through pebble accretion, with an with an emphasis on hydrogen envelope longevity and the composition of the outgassed atmosphere. We model planets with a range in mass from 0.1 to 5 Earth masses that orbit between 0.7 and 1.7 AU. The composition of the outgassed atmosphere is calculated with the partial pressure of free oxygen fit to geophysical models of magma ocean self-oxidation. XUV radiation powered photoevaporation is considered as the main driver of atmospheric escape. We model planets that remain below the pebble isolation mass and hence accrete tenuous envelopes only. We consider slow, medium or fast initial stellar rotation for the temporal evolution of the XUV flux. The loss of the envelope is a key event that allows the magma ocean to crystallise and outgas its bulk volatiles. The atmospheric composition of the majority of our simulated planets is dominated by CO$_2$. Our planets accrete a total of 11.6 Earth oceans of water, the majority of which enters the core. The hydrospheres of planets lighter than the Earth reach several times the mass of the Earth's modern oceans, while the hydrospheres of planets ranging from 1 to 3.5 Earth masses are comparable to those of our planet. However, planets of 4-5 Earth masses have smaller hydrospheres due to trapping of volatiles in their massive mantles. Overall, our simulations demonstrate that hydrogen envelopes are easily lost from rocky planets and that this envelope loss triggers the most primordial partitioning of volatiles between the solid mantle and the atmosphere., Comment: Accepted for Astronomy & Astrophysics

Intracorporeal needle-based therapeutic ultrasound (NBTU) is a minimally invasive option for intervening in malignant brain tumors, commonly used in thermal ablation procedures. This technique is suitable for both primary and metastatic cancers, utilizing a high-frequency alternating electric field (up to 10 MHz) to excite a piezoelectric transducer. The resulting rapid deformation of the transducer produces an acoustic wave that propagates through tissue, leading to localized high-temperature heating at the target tumor site and inducing rapid cell death. To optimize the design of NBTU transducers for thermal dose delivery during treatment, numerical modeling of the acoustic pressure field generated by the deforming piezoelectric transducer is frequently employed. The bioheat transfer process generated by the input pressure field is used to track the thermal propagation of the applicator over time. Magnetic resonance thermal imaging (MRTI) can be used to experimentally validate these models. Validation results using MRTI demonstrated the feasibility of this model, showing a consistent thermal propagation pattern. However, a thermal damage isodose map is more advantageous for evaluating therapeutic efficacy. To achieve a more accurate simulation based on the actual brain tissue environment, a new finite element method (FEM) simulation with enhanced damage evaluation capabilities was conducted. The results showed that the highest temperature and ablated volume differed between experimental and simulation results by 2.1884{\deg}C (3.71%) and 0.0631 cm$^3$ (5.74%), respectively. The lowest Pearson correlation coefficient (PCC) for peak temperature was 0.7117, and the lowest Dice coefficient for the ablated area was 0.7021, indicating a good agreement in accuracy between simulation and experiment., Comment: 9 pages, 9 figures, 7 tables

Previous measurements of neutron energy using fast plastic scintillators while operating the Fusion Z Pinch Experiment (FuZE) constrained the energy of any yield-producing deuteron beams to less than $4.65 keV$. FuZE has since been operated at increasingly higher input power, resulting in increased plasma current and larger fusion neutron yields. A detailed experimental study of the neutron energy isotropy in these regimes applies more stringent limits to possible contributions from beam-target fusion. The FuZE device operated at $-25~kV$ charge voltage has resulted in average plasma currents of $370~kA$ and D-D fusion neutron yields of $4\times10^7$ neutrons per discharge. Measurements of the neutron energy isotropy under these operating conditions demonstrates the energy of deuteron beams is less than $7.4 \pm 5.6^\mathrm{(stat)} \pm 3.7^\mathrm{(syst)}~keV$. Characterization of the detector response has reduced the number of free parameters in the fit of the neutron energy distribution, improving the confidence in the forward-fit method. Gamma backgrounds have been measured and the impact of these contributions on the isotropy results have been studied. Additionally, a time dependent measurement of the isotropy has been resolved for the first time, indicating increases to possible deuteron beam energies at late times. This suggests the possible growth of $m$=0 instabilities at the end of the main radiation event but confirms that the majority of the neutron production exhibits isotropy consistent with thermonuclear origin., Comment: 16 pages, 11 figures, submitted to Journal of Nuclear Fusion

By utilizing recently developed tools for constructing gradient flows on Wasserstein spaces, we extend an analysis technique commonly employed to understand alternating minimization algorithms on Euclidean space to the Expectation Maximization (EM) algorithm via its representation as coordinate-wise minimization on the product of a Euclidean space and a space of probability distributions due to Neal and Hinton (1998). In so doing we obtain finite sample error bounds and exponential convergence of the EM algorithm under a natural generalisation of a log-Sobolev inequality. We further demonstrate that the analysis technique is sufficiently flexible to allow also the analysis of several variants of the EM algorithm.

Semi-implicit variational inference (SIVI) enriches the expressiveness of variational families by utilizing a kernel and a mixing distribution to hierarchically define the variational distribution. Existing SIVI methods parameterize the mixing distribution using implicit distributions, leading to intractable variational densities. As a result, directly maximizing the evidence lower bound (ELBO) is not possible, so they resort to one of the following: optimizing bounds on the ELBO, employing costly inner-loop Markov chain Monte Carlo runs, or solving minimax objectives. In this paper, we propose a novel method for SIVI called Particle Variational Inference (PVI) which employs empirical measures to approximate the optimal mixing distributions characterized as the minimizer of a free energy functional. PVI arises naturally as a particle approximation of a Euclidean--Wasserstein gradient flow and, unlike prior works, it directly optimizes the ELBO whilst making no parametric assumption about the mixing distribution. Our empirical results demonstrate that PVI performs favourably compared to other SIVI methods across various tasks. Moreover, we provide a theoretical analysis of the behaviour of the gradient flow of a related free energy functional: establishing the existence and uniqueness of solutions as well as propagation of chaos results., Comment: NeurIPS 2024 Camera ready

We show that genealogical trees arising from a broad class of non-neutral models of population evolution converge to the Kingman coalescent under a suitable rescaling of time. As well as non-neutral biological evolution, our results apply to genetic algorithms encompassing the prominent class of sequential Monte Carlo (SMC) methods. The time rescaling we need differs slightly from that used in classical results for convergence to the Kingman coalescent, which has implications for the performance of different resampling schemes in SMC algorithms. In addition, our work substantially simplifies earlier proofs of convergence to the Kingman coalescent, and corrects an error common to several earlier results.

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.

Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. Many attempts to close this gap have utilized classical simulations based on tensor network techniques, and their limitations shed light on the improvements to quantum hardware required to frustrate classical simulability. In particular, quantum computers having in excess of $\sim 50$ qubits are primarily vulnerable to classical simulation due to restrictions on their gate fidelity and their connectivity, the latter determining how many gates are required (and therefore how much infidelity is suffered) in generating highly-entangled states. Here, we describe recent hardware upgrades to Quantinuum's H2 quantum computer enabling it to operate on up to $56$ qubits with arbitrary connectivity and $99.843(5)\%$ two-qubit gate fidelity. Utilizing the flexible connectivity of H2, we present data from random circuit sampling in highly connected geometries, doing so at unprecedented fidelities and a scale that appears to be beyond the capabilities of state-of-the-art classical algorithms. The considerable difficulty of classically simulating H2 is likely limited only by qubit number, demonstrating the promise and scalability of the QCCD architecture as continued progress is made towards building larger machines., Comment: Includes minor updates to the text and an updated author list to include researchers who made technical contributions in upgrading the machine to 56 qubits but were left off the original version by mistake

A planar superfluid is considered and interpreted in terms of electromagnetism and gravity. It has previously been suggested that the superfluid flow can be regarded as analogous to an electromagnetic field and that a non-vanishing density gradient give rise to a gravity-like force. The present work seeks to reconcile these hitherto distinct pictures into a unified exposition of vortex electrodynamics in a curved analogue space-time. By constructing a theory in which the planar Maxwell's equations are coupled to a (d+1)-dimensional conformally curved space-time, we expose a correspondence between the resulting equations of motion of the embedded fields and the dynamics of a quantum fluid. Finally, an effective vortex metric whose connection components are torsional is studied and its effect on the superfluid Maxwell's equations is elucidated upon., Comment: 9 pages

During their formation, nascent planetary systems are subject to FU Orionis outbursts that heat a substantial part of the disc. This causes water ice in the affected part of the disc to sublimate as the ice line moves outwards to several to tens of astronomical units. In this paper, we investigate how the subsequent cooling of the disc impacts the particle sizes. We calculate the resulting particle sizes in a disc model with cooling times between 100 and 1,000 years, corresponding to typical FU Orionis outbursts. As the disc cools and the ice line retreats inwards, water vapour forms icy mantles on existing silicate particles. This process is called heterogeneous nucleation. The nucleation rate per surface area of silicate substrate strongly depends on the degree of super-saturation of the water vapour in the gas. Fast cooling results in high super-saturation levels, high nucleation rates, and limited condensation growth because the main ice budget is spent in the nucleation. Slow cooling, on the other hand, leads to rare ice nucleation and efficient growth of ice-nucleated particles by subsequent condensation. We demonstrate that close to the quiescent ice line, pebbles with a size of about centimetres to decimetres form by this process. The largest of these are expected to undergo cracking collisions. However, their Stokes numbers still reach values that are high enough to potentially trigger planetesimal formation by the streaming instability if the background turbulence is weak. Stellar outbursts may thus promote planetesimal formation around the water ice line in protoplanetary discs., Comment: Accepted for publication in Astronomy & Astrophysics. 8 pages, 6 figures

The architecture and masses of planetary systems in the habitable zone could be strongly influenced by outer giant planets, if present. We investigate here the impact of outer giants on terrestrial planet formation, under the assumption that the final assembly of the planetary system is set by a giant impact phase. Utilizing a state-of-the-art N-body simulation software, GENGA, we interpret how the late stage of terrestrial planet formation results in diversity within planetary systems. We design two global model setups: in the first we place a gas giant on the outer side of planetesimals and embryos disk, while the other only has planetesimals and embryos but no giant. For the model including the outer giant, we study the effect of different giant initial masses, in the range 1.0-3.0 Jupiter mass, and orbital radii, in the range 2.0-5.8 AU.We also study the influence of different initial positions of planetesimals and embryos on the results. Our N-body simulation time is approximately 50 Myr. The results show that the existence of outer giant will promote the interaction between planetesimals and embryos, making the orbits of the formed terrestrial planets more compact, but placing the giant planet too close to the planetesimals and embryos disk suppresses the formation of massive rocky planets. In addition, under the classical theory, where planetary embryos and planetesimals collide to form terrestrial planets, our results show that the presence of a giant planet actually decreases the gap complexity of the inner planetary system., Comment: 12 pages, 15 figures

In optimal policy problems where treatment effects vary at the individual level, optimally allocating treatments to recipients is complex even when potential outcomes are known. We present an algorithm for multi-arm treatment allocation problems that is guaranteed to find the optimal allocation in strongly polynomial time, and which is able to handle arbitrary potential outcomes as well as constraints on treatment requirement and capacity. Further, starting from an arbitrary allocation, we show how to optimally re-allocate treatments in a Pareto-improving manner. To showcase our results, we use data from Danish nurse home visiting for infants. We estimate nurse specific treatment effects for children born 1959-1967 in Copenhagen, comparing nurses against each other. We exploit random assignment of newborn children to nurses within a district to obtain causal estimates of nurse-specific treatment effects using causal machine learning. Using these estimates, and treating the Danish nurse home visiting program as a case of an optimal treatment allocation problem (where a treatment is a nurse), we document room for significant productivity improvements by optimally re-allocating nurses to children. Our estimates suggest that optimal allocation of nurses to children could have improved average yearly earnings by USD 1,815 and length of education by around two months.

Quantum state teleportation is commonly used in designs for large-scale fault-tolerant quantum computers. Using Quantinuum's H2 trapped-ion quantum processor, we implement the first demonstration of a fault-tolerant state teleportation circuit for a quantum error correction code - in particular, the planar topological [[7,1,3]] color code, or Steane code. The circuits use up to 30 trapped ions at the physical layer qubits and employ real-time quantum error correction - decoding mid-circuit measurement of syndromes and implementing corrections during the protocol. We conduct experiments on several variations of logical teleportation circuits using both transversal gates and lattice surgery protocols. Among the many measurements we report on, we measure the logical process fidelity of the transversal teleportation circuit to be 0.975(2) and the logical process fidelity of the lattice surgery teleportation circuit to be 0.851(9). Additionally, we run a teleportation circuit that is equivalent to Knill-style quantum error correction and measure the process fidelity to be 0.989(2).

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors., Comment: arXiv admin note: substantial text overlap with arXiv:2305.17033

Current research in robotic sounds generally focuses on either masking the consequential sound produced by the robot or on sonifying data about the robot to create a synthetic robot sound. We propose to capture, modify, and utilise rather than mask the sounds that robots are already producing. In short, this approach relies on capturing a robot's sounds, processing them according to contextual information (e.g., collaborators' proximity or particular work sequences), and playing back the modified sound. Previous research indicates the usefulness of non-semantic, and even mechanical, sounds as a communication tool for conveying robotic affect and function. Adding to this, this paper presents a novel approach which makes two key contributions: (1) a technique for real-time capture and processing of consequential robot sounds, and (2) an approach to explore these sounds through direct human-robot interaction. Drawing on methodologies from design, human-robot interaction, and creative practice, the resulting 'Robotic Blended Sonification' is a concept which transforms the consequential robot sounds into a creative material that can be explored artistically and within application-based studies., Comment: Paper accepted at ISEA 24, The 29th International Symposium on Electronic Art, Brisbane, Australia, 21-29 June 2024

Few-shot knowledge distillation recently emerged as a viable approach to harness the knowledge of large-scale pre-trained models, using limited data and computational resources. In this paper, we propose a novel few-shot feature distillation approach for vision transformers. Our approach is based on two key steps. Leveraging the fact that vision transformers have a consistent depth-wise structure, we first copy the weights from intermittent layers of existing pre-trained vision transformers (teachers) into shallower architectures (students), where the intermittence factor controls the complexity of the student transformer with respect to its teacher. Next, we employ an enhanced version of Low-Rank Adaptation (LoRA) to distill knowledge into the student in a few-shot scenario, aiming to recover the information processing carried out by the skipped teacher layers. We present comprehensive experiments with supervised and self-supervised transformers as teachers, on six data sets from various domains (natural, medical and satellite images) and tasks (classification and segmentation). The empirical results confirm the superiority of our approach over state-of-the-art competitors. Moreover, the ablation results demonstrate the usefulness of each component of the proposed pipeline. We release our code at https://github.com/dianagrigore/WeCoLoRA., Comment: Accepted at WACV 2025

We implement logically encoded three-qubit circuits for the quantum Fourier transform (QFT), using the [[7,1,3]] Steane code, and benchmark the circuits on the Quantinuum H2-1 trapped-ion quantum computer. The circuits require multiple logical two-qubit gates, which are implemented transversally, as well as logical non-Clifford single-qubit rotations, which are performed by non-fault-tolerant state preparation followed by a teleportation gadget. First, we benchmark individual logical components using randomized benchmarking for the logical two-qubit gate, and a Ramsey-type experiment for the logical $T$ gate. We then implement the full QFT circuit, using two different methods for performing a logical control-$T$, and benchmark the circuits by applying it to each basis state in a set of bases that is sufficient to lower bound the process fidelity. We compare the logical QFT benchmark results to predictions based on the logical component benchmarks.

The promise of quantum computers hinges on the ability to scale to large system sizes, e.g., to run quantum computations consisting of more than 100 million operations fault-tolerantly. This in turn requires suppressing errors to levels inversely proportional to the size of the computation. As a step towards this ambitious goal, we present experiments on a trapped-ion QCCD processor where, through the use of fault-tolerant encoding and error correction, we are able to suppress logical error rates to levels below the physical error rates. In particular, we entangled logical qubits encoded in the [[7,1,3]] code with error rates 9.8 times to 500 times lower than at the physical level, and entangled logical qubits encoded in a [[12,2,4]] code based on Knill's C4/C6 scheme with error rates 4.7 times to 800 times lower than at the physical level, depending on the judicious use of post-selection. Moreover, we demonstrate repeated error correction with the [[12,2,4]] code, with logical error rates below physical circuit baselines corresponding to repeated CNOTs, and show evidence that the error rate per error correction cycle, which consists of over 100 physical CNOTs, approaches the error rate of two physical CNOTs. These results signify a transition from noisy intermediate scale quantum computing to reliable quantum computing, and demonstrate advanced capabilities toward large-scale fault-tolerant quantum computing., Comment: (v1) 13 pages, 8 figures; (v2) Fixed typos, added authors; (v3) Added Carbon details (instead of separate article), improved decoder, got more data, added authors, fixed misinterpreted physical teleportation baseline, added a figure, and fixed typos

Polytypes of tetralayer graphene (TLG: Bernal, rhombohedral and mixed stacking) are crystalline structures with different symmetries. Among those, mixed-stacking tetralayers lack inversion symmetry, which allows for intrinsic spontaneous out-of-plane electrical polarisation, inverted in the mirror-image pair, ABCB and ABAC stackings. Here, we compare the intrinsic polarisation of such TLGs with the symmetry-breaking effect of a substrate, which can also generate out-of-plane electric dipole moments with different sizes in all four polytypes, including ABCB and ABAC twins. We analyse their temperature and magnetic field dependence, in view of understanding the origin of the recently measured Kelvin probe force microscopy maps of tetralayer flakes, and notice that the intrinsic contribution could be singled out based on magnetic field dependence of polarisation measured at low temperatures., Comment: 11 pages, 8 figures

This article explores the latest Convolutional Neural Networks (CNNs) for cloud detection aboard hyperspectral satellites. The performance of the latest 1D CNN (1D-Justo-LiuNet) and two recent 2D CNNs (nnU-net and 2D-Justo-UNet-Simple) for cloud segmentation and classification is assessed. Evaluation criteria include precision and computational efficiency for in-orbit deployment. Experiments utilize NASA's EO-1 Hyperion data, with varying spectral channel numbers after Principal Component Analysis. Results indicate that 1D-Justo-LiuNet achieves the highest accuracy, outperforming 2D CNNs, while maintaining compactness with larger spectral channel sets, albeit with increased inference times. However, the performance of 1D CNN degrades with significant channel reduction. In this context, the 2D-Justo-UNet-Simple offers the best balance for in-orbit deployment, considering precision, memory, and time costs. While nnU-net is suitable for on-ground processing, deployment of lightweight 1D-Justo-LiuNet is recommended for high-precision applications. Alternatively, lightweight 2D-Justo-UNet-Simple is recommended for balanced costs between timing and precision in orbit., Comment: Hyperspectral Satellite Data, Cloud Segmentation, Classification, Convolutional Neural Networks, Principal Component Analysis

The theory of topological quantum computation is underpinned by two important classes of models. One is based on non-abelian Chern-Simons theory, which yields the so-called $\rm{SU}(2)_k$ anyon models that often appear in the context of electrically charged quantum fluids. The physics of the other is captured by symmetry broken Yang-Mills theory in the absence of a Chern-Simons term, and results in the so-called quantum double models. Extensive resources have been invested into the search for $\rm{SU}(2)_k$ anyon quasi-particles; in particular the so-called Ising anyons ($k=2$) of which Majorana zero modes are believed to be an incarnation. In contrast to the $\rm{SU}(2)_k$ models, quantum doubles have attracted little attention in experiments despite their pivotal role in the theory of error correction. Beyond topological error correcting codes, the appearance of quantum doubles has been limited to contexts primarily within mathematical physics, and as such, they are of seemingly little relevance for the study of experimentally tangible systems. However, recent works suggest that quantum double anyons may be found in spinor Bose-Einstein condensates. In light of this, the core purpose of this article is to provide a self-contained exposition of the quantum double structure, framed in the context of spinor condensates, by constructing explicitly the quantum doubles for various ground state symmetry groups and discuss their experimental realisability. We also derive analytically an equation for the quantum double Clebsch-Gordan coefficients from which the relevant braid matrices can be worked out. Finally, the existence of a particle-vortex duality is exposed and illuminated upon in this context., Comment: 70 pages

We prove non-asymptotic error bounds for particle gradient descent (PGD)~(Kuntz et al., 2023), a recently introduced algorithm for maximum likelihood estimation of large latent variable models obtained by discretizing a gradient flow of the free energy. We begin by showing that, for models satisfying a condition generalizing both the log-Sobolev and the Polyak--{\L}ojasiewicz inequalities (LSI and P{\L}I, respectively), the flow converges exponentially fast to the set of minimizers of the free energy. We achieve this by extending a result well-known in the optimal transport literature (that the LSI implies the Talagrand inequality) and its counterpart in the optimization literature (that the P{\L}I implies the so-called quadratic growth condition), and applying it to our new setting. We also generalize the Bakry--\'Emery Theorem and show that the LSI/P{\L}I generalization holds for models with strongly concave log-likelihoods. For such models, we further control PGD's discretization error, obtaining non-asymptotic error bounds. While we are motivated by the study of PGD, we believe that the inequalities and results we extend may be of independent interest.

Rapidly Exploring Random Tree (RRT) algorithms, notably used for nonholonomic vehicle navigation in complex environments, are often not thoroughly evaluated for their specific challenges. This paper presents a first such comparison study of the variants Potential-Quick RRT* (PQ-RRT*), Informed RRT* (IRRT*), RRT*, and RRT, in maritime single-query nonholonomic motion planning. Additionally, the practicalities of using these algorithms in maritime environments are discussed and outlined. We also contend that these algorithms are beneficial not only for trajectory planning in Collision Avoidance Systems (CAS) but also for CAS verification when used as vessel behavior generators. Optimal RRT variants tend to produce more distance-optimal paths but require more computational time due to complex tree wiring and nearest neighbor searches. Our findings, supported by Welch`s t-test at a significance level of Alpha = 0.05, indicate that PQ-RRT* slightly outperform IRRT* and RRT* in achieving shorter trajectory length but at the expense of higher tuning complexity and longer run-times. Based on the results, we argue that these RRT algorithms are better suited for smaller-scale problems or environments with low obstacle congestion ratio. This is attributed to the curse of dimensionality, and trade-off with available memory and computational resources.

This paper presents a distributed solution for the problem of collaborative collision avoidance for autonomous inland waterway ships. A two-layer collision avoidance framework that considers inland waterway traffic regulations is proposed to increase navigational safety for autonomous ships. Our approach allows for modifying traffic rules without changing the collision avoidance algorithm, and is based on a novel formulation of model predictive control (MPC) for collision avoidance of ships. This MPC formulation is designed for inland waterway traffic and can handle complex scenarios. The alternating direction method of multipliers is used as a scheme for exchanging and negotiating intentions among ships. Simulation results show that the proposed algorithm can comply with traffic rules. Furthermore, the proposed algorithm can safely deviate from traffic rules when necessary to increase efficiency in complex scenarios.

Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data volume exceeds the capacity of the computational resources, particularly in the case of remote sensing platforms (e.g., drones, satellites), but also in the case of multi-year datasets. Moreover, the computational strategies of unsupervised dimensionality reduction often provide the basis for more complicated supervised techniques. Seven unsupervised dimensionality reduction algorithms are tested on hyperspectral data from the HYPSO-1 earth observation satellite. Each particular algorithm is chosen to be representative of a broader collection. The experiments probe the computational complexity, reconstruction accuracy, signal clarity, sensitivity to artifacts, and effects on target detection and classification of the different algorithms. No algorithm consistently outperformed the others across all tests, but some general trends regarding the characteristics of the algorithms did emerge. With half a million pixels, computational time requirements of the methods varied by 5 orders of magnitude, and the reconstruction error varied by about 3 orders of magnitude. A relationship between mutual information and artifact susceptibility was suggested by the tests. The relative performance of the algorithms differed significantly between the target detection and classification tests. Overall, these experiments both show the power of dimensionality reduction and give guidance regarding how to evaluate a technique prior to incorporating it into a processing pipeline., Comment: 16 pages, 11 figures