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Quantum computers are available via a variety of different quantum cloud offerings. These offerings are heterogeneous and differ in features, such as pricing models or types of access to quantum computers. Furthermore, quantum circuits can be implemented using different quantum programming languages, which are typically only supported by a small subset of quantum cloud offerings. As a consequence, using a specific quantum programming language for implementing the application at hand can limit the set of compatible quantum cloud offerings and cause a vendor lock-in. Therefore, selecting a suitable quantum cloud offering and a corresponding quantum programming language requires knowledge about their features. In this paper, we (i) analyze the available quantum cloud offerings and extract their features. Moreover, we (ii) introduce the architecture for a unification middleware that facilitates accessing quantum computers available via different quantum cloud offerings by automatically translating between various quantum circuit and result formats. To showcase the practical feasibility of our approach, we (iii) present a prototypical implementation and validate it for three exemplary application scenarios.

The performance of neural networks scales with both their size and the amount of data they have been trained on. This is shown in both language and image generation. However, this requires scaling-friendly network architectures as well as large-scale datasets. Even though scaling-friendly architectures like transformers have emerged for 3D vision tasks, the GPT-moment of 3D vision remains distant due to the lack of training data. In this paper, we introduce ARKit LabelMaker, the first large-scale, real-world 3D dataset with dense semantic annotations. Specifically, we complement ARKitScenes dataset with dense semantic annotations that are automatically generated at scale. To this end, we extend LabelMaker, a recent automatic annotation pipeline, to serve the needs of large-scale pre-training. This involves extending the pipeline with cutting-edge segmentation models as well as making it robust to the challenges of large-scale processing. Further, we push forward the state-of-the-art performance on ScanNet and ScanNet200 dataset with prevalent 3D semantic segmentation models, demonstrating the efficacy of our generated dataset.

The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently christened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs ([U]BV, RIZ, YJH) providing a spectral resolution of $\sim$100,000 with a minimum simultaneous wavelength coverage of 0.4-1.8 $\mu$m with the goal of extending it to 0.35-2.4 $\mu$m with the addition of a U arm to the BV spectrograph and a separate K band spectrograph. It operates both in seeing- and diffraction-limited conditions and the fibre feeding allows several, interchangeable observing modes including a single conjugated adaptive optics module and a small diffraction-limited integral field unit in the NIR. Modularity and fibre-feeding allow ANDES to be placed partly on the ELT Nasmyth platform and partly in the Coud\'e room. ANDES has a wide range of groundbreaking science cases spanning nearly all areas of research in astrophysics and even fundamental physics. Among the top science cases, there are the detection of biosignatures from exoplanet atmospheres, finding the fingerprints of the first generation of stars, tests on the stability of Nature's fundamental couplings, and the direct detection of the cosmic acceleration. The ANDES project is carried forward by a large international consortium, composed of 35 Institutes from 13 countries, forming a team of almost 300 scientists and engineers which include the majority of the scientific and technical expertise in the field that can be found in ESO member states., Comment: SPIE astronomical telescope and instrumentation 2024, in press

We investigate reduced-order models for acoustic and electromagnetic wave problems in parametrically defined domains. The parameter-to-solution maps are approximated following the so-called Galerkin POD-NN method, which combines the construction of a reduced basis via proper orthogonal decomposition (POD) with neural networks (NNs). As opposed to the standard reduced basis method, this approach allows for the swift and efficient evaluation of reduced-order solutions for any given parametric input. As is customary in the analysis of problems in random or parametrically defined domains, we start by transporting the formulation to a reference domain. This yields a parameter-dependent variational problem set on parameter-independent functional spaces. In particular, we consider affine-parametric domain transformations characterized by a high-dimensional, possibly countably infinite, parametric input. To keep the number of evaluations of the high-fidelity solutions manageable, we propose using low-discrepancy sequences to sample the parameter space efficiently. Then, we train an NN to learn the coefficients in the reduced representation. This approach completely decouples the offline and online stages of the reduced basis paradigm. Numerical results for the three-dimensional Helmholtz and Maxwell equations confirm the method's accuracy up to a certain barrier and show significant gains in online speed-up compared to the traditional Galerkin POD method.

We prove a reverse Lieb-Thirring inequality with a sharp constant for the matrix Schr\"odinger equation on the half-line., Comment: I corrected misprints and I have added the journal reference

We present the transformations to remove or add bound states or to decrease or increase the multiplicities of any existing bound states for the half-line matrix-valued Schr\"odinger operator with the general selfadjoint boundary condition, without changing the continuous spectrum of the operator. When the matrix-valued potential is selfadjoint, is integrable, and has a finite first moment, the relevant transformations are constructed through the development of the Gel'fand-Levitan method for the corresponding Schr\"odinger operator. In particular, the bound-state normalization matrices are constructed at each bound state for any multiplicity. The transformations are obtained for all relevant quantities, including the matrix potential, the Jost solution, the regular solution, the Jost matrix, the scattering matrix, and the boundary condition. For each bound state, the corresponding dependency matrix is introduced by connecting the normalization matrix used in the Gel'fand-Levitan method and the normalization matrix used in the Marchenko method of inverse scattering. Various estimates are provided to describe the large spacial asymptotics for the change in the potential when the bound states are removed or added or their multiplicities are modified. An explicit example is provided showing that an asymptotic estimate available in the literature in the scalar case for the potential increment is incorrect., Comment: The title has been shortened. The paper has been edited to improve the presentation and some results have been sharpened

Inkjet printing technology achieves the precise deposition of liquid-phase materials via the digitally controlled formation of picoliter-sized droplets. Beyond graphical printing, inkjet printing has been employed for the deposition of separated drops on surfaces or the formation of continuous layers, which allows to construct materials gradients or periodic features that provide enhanced functionalities. Here, we explore the use of multinozzle, drop-on-demand piezoelectric inkjet technology for the manufacturing of mechanochromic materials, i.e., materials that change their color or fluorescence in response to mechanical deformation. To accomplish this, suitable polyurethane polymers of differing hardness grades were tested with a range of organic solvents to formulate low-viscosity, inkjet-printable solutions. Following their rheological characterization, two solutions comprised of "soft" and "hard" polyurethanes were selected for in-depth study. The solutions were imbibed with a mechanochromic additive to yield fluorescent inks, which were either dropcast onto polymeric substrates or printed to form checkerboard patterns of alternating hardness using a lab-built, multimaterial inkjet platform. Fluorescence imaging and spectroscopy were used to identify different hardness grades in the dropcast and printed materials, as well as to monitor the responses of these gradient materials to mechanical deformation. The insights gained in this study are expected to facilitate the development of inkjet-printable, mechanochromic polymer materials for a wide range of applications., Comment: 15 pages, 8 figures, 1 table

We consider a one-dimensional membrane-in-the-middle model for a cavity that consists of two fixed, perfect mirrors and a mobile dielectric membrane between them that has a constant electric susceptibility. We present a sequence of exact cavity angular frequencies that we call structural angular frequencies and that have the remarkable property that they are independent of the position of the membrane inside the cavity. Furthermore, the case of a thin membrane is considered and simple, approximate formulae for the angular frequencies and for the modes of the cavity are obtained. Finally, the cavity electromagnetic potential is numerically calculated and it is found that the potential is accurately described by a multiple scales solution.

We propose an online 3D semantic segmentation method that incrementally reconstructs a 3D semantic map from a stream of RGB-D frames. Unlike offline methods, ours is directly applicable to scenarios with real-time constraints, such as robotics or mixed reality. To overcome the inherent challenges of online methods, we make two main contributions. First, to effectively extract information from the input RGB-D video stream, we jointly estimate geometry and semantic labels per frame in 3D. A key focus of our approach is to reason about semantic entities both in the 2D input and the local 3D domain to leverage differences in spatial context and network architectures. Our method predicts 2D features using an off-the-shelf segmentation network. The extracted 2D features are refined by a lightweight 3D network to enable reasoning about the local 3D structure. Second, to efficiently deal with an infinite stream of input RGB-D frames, a subsequent network serves as a temporal expert predicting the incremental scene updates by leveraging 2D, 3D, and past information in a learned manner. These updates are then integrated into a global scene representation. Using these main contributions, our method can enable scenarios with real-time constraints and can scale to arbitrary scene sizes by processing and updating the scene only in a local region defined by the new measurement. Our experiments demonstrate improved results compared to existing online methods that purely operate in local regions and show that complementary sources of information can boost the performance. We provide a thorough ablation study on the benefits of different architectural as well as algorithmic design decisions. Our method yields competitive results on the popular ScanNet benchmark and SceneNN dataset.

Semantic annotations are indispensable to train or evaluate perception models, yet very costly to acquire. This work introduces a fully automated 2D/3D labeling framework that, without any human intervention, can generate labels for RGB-D scans at equal (or better) level of accuracy than comparable manually annotated datasets such as ScanNet. Our approach is based on an ensemble of state-of-the-art segmentation models and 3D lifting through neural rendering. We demonstrate the effectiveness of our LabelMaker pipeline by generating significantly better labels for the ScanNet datasets and automatically labelling the previously unlabeled ARKitScenes dataset. Code and models are available at https://labelmaker.org

Since their introduction in 2020, Neural Radiance Fields (NeRFs) have taken the computer vision community by storm. They provide a multi-view representation of a scene or object that is ideal for eXtended Reality (XR) applications and for creative endeavors such as virtual production, as well as change detection operations in geospatial analytics. The computational cost of these generative AI models is quite high, however, and the construction of cloud pipelines to generate NeRFs is neccesary to realize their potential in client applications. In this paper, we present pipelines on a high performance academic computing cluster and compare it with a pipeline implemented on Microsoft Azure. Along the way, we describe some uses of NeRFs in enabling novel user interaction scenarios.

We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personalized treatment and future outcome trajectories through deep generative time series models. In particular, our framework enables the generation of novel multivariate treatment strategies tailored to the personalized patient history and trained for optimal expected future outcomes based on conditional expected utility maximization. We demonstrate our framework by generating personalized insulin treatment strategies and blood glucose predictions for hospitalized diabetes patients, showcasing the potential of our approach for generating improved personalized treatment strategies. Keywords: deep generative model, probabilistic decision support, personalized treatment generation, insulin and blood glucose prediction, Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 17 pages

Despite tremendous growth in the volume of new scientific and technological knowledge, the popular press has recently raised concerns that disruptive innovative activity is slowing. These dire prognoses were mainly driven by Park et al. (2023), a Nature publication that uses decades of data and millions of observations coupled with a novel quantitative metric (the CD index) that characterizes innovation in science and technology as either consolidating or disruptive. We challenge the Park et al. (2023) methodology and findings, principally around concerns of truncation bias and exclusion bias. We show that 88 percent of the decrease in disruptive patents over 1980-2010 reported by the authors can be explained by their truncation of all backward citations before 1976. We also show that this truncation bias varies by technology class. We update the analysis to 2016 and account for a change in U.S. patent law that allows for citations to patent applications in addition to patent grants, which is ignored by the authors in their analysis. We show that the number of highly disruptive patents has increased since 1980 -- particularly in IT technologies. Our results suggest caution in using the Park et al. (2023) methodology as a basis for research and decision making in public policy, industry restructuring or firm reorganization aimed at altering the current innovation landscape.

Context. Current research has established magnetised disc winds as a promising way of driving accretion in protoplanetary discs. Aims. We investigate the evolution of large protoplanetary disc populations under the influence of magnetically driven disc winds as well as internal and external photoevaporation. We aim to constrain magnetic disc wind models through comparisons with observations. Methods. We ran 1D vertically integrated evolutionary simulations for low-viscosity discs, including magnetic braking and various outflows. The initial conditions were varied and chosen to produce populations that are representative of actual disc populations inferred from observations. We then compared the observables from the simulations (e.g. stellar accretion rate, disc mass evolution, disc lifetime, etc.) with observational data. Results. Our simulations show that to reach stellar accretion rates comparable to those found by observations $\sim 10^{-8}\mathrm{M}_\odot / \mathrm{yr}$, it is necessary to have access not only to strong magnetic torques, but weak magnetic winds as well. The presence of a strong magnetic disc wind, in combination with internal photoevaporation, leads to the rapid opening of an inner cavity early on, allowing the stellar accretion rate to drop while the disc is still massive. Furthermore, our model supports the notion that external photoevaporation via the ambient far-ultraviolet radiation of surrounding stars is a driving force in disc evolution and could potentially exert a strong influence on planetary formation. Conclusions. Our disc population syntheses show that for a subset of magnetohydrodynamic wind models (weak disc wind, strong torque), it is possible to reproduce important statistical observational constraints. The magnetic disc wind paradigm thus represents a novel and appealing alternative to the classical $\alpha$-viscosity scenario., Comment: Accepted for publication in A&A

Phosphorescent emissions at room temperature from solid-state self sustained film fabricated using phenazine derivative molecules blended in a solution of poly(methyl methacrylate) were investigated at steady-state experimental conditions. Stripe excitation by a pulsed laser at different levels of intensities and different lengths of stripes, originates a guided emission up to the edge of the film, enabling us to assume higher gain characteristics for triplet states of the medium, due to the relatively higher contribution of the total integrated area for the phosphorescent part of the emission spectra., Comment: The manuscript suffered a profound reediting, including the title, what made it unrecognizable to the shown on arxiv. We seek for a new resubmission in the near future

Noisy Intermediate-Scale Quantum (NISQ) devices are restricted by their limited number of qubits and their short decoherence times. An approach addressing these problems is quantum circuit cutting. It decomposes the execution of a large quantum circuit into the execution of multiple smaller quantum circuits with additional classical postprocessing. Since these smaller quantum circuits require fewer qubits and gates, they are more suitable for NISQ devices. To investigate the effect of quantum circuit cutting in a quantum algorithm targeting NISQ devices, we design two experiments using the Quantum Approximate Optimization Algorithm (QAOA) for the Maximum Cut (MaxCut) problem and conduct them on state-of-the-art superconducting devices. Our first experiment studies the influence of circuit cutting on the objective function of QAOA, and the second evaluates the quality of results obtained by the whole algorithm with circuit cutting. The results show that circuit cutting can reduce the effects of noise in QAOA, and therefore, the algorithm yields better solutions on NISQ devices.

Aims. We want to find the distribution of initial conditions that best reproduces disc observations at the population level. Methods. We first ran a parameter study using a 1D model that includes the viscous evolution of a gas disc, dust, and pebbles, coupled with an emission model to compute the millimetre flux observable with ALMA. This was used to train a machine learning surrogate model that can compute the relevant quantity for comparison with observations in seconds. This surrogate model was used to perform parameter studies and synthetic disc populations. Results. Performing a parameter study, we find that internal photoevaporation leads to a lower dependency of disc lifetime on stellar mass than external photoevaporation. This dependence should be investigated in the future. Performing population synthesis, we find that under the combined losses of internal and external photoevaporation, discs are too short lived. Conclusions. To match observational constraints, future models of disc evolution need to include one or a combination of the following processes: infall of material to replenish the discs, shielding of the disc from internal photoevaporation due to magnetically driven disc winds, and extinction of external high-energy radiation. Nevertheless, disc properties in low-external-photoevaporation regions can be reproduced by having more massive and compact discs. Here, the optimum values of the $\alpha$ viscosity parameter lie between $3\times10^{-4}$ and $10^{-3}$ and with internal photoevaporation being the main mode of disc dispersal., Comment: Accepted for publication in A&A; minor changes in the reference list

Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved with the current NeRF editing frameworks. We propose a framework to remove objects from a NeRF representation created from an RGB-D sequence. Our NeRF inpainting method leverages recent work in 2D image inpainting and is guided by a user-provided mask. Our algorithm is underpinned by a confidence based view selection procedure. It chooses which of the individual 2D inpainted images to use in the creation of the NeRF, so that the resulting inpainted NeRF is 3D consistent. We show that our method for NeRF editing is effective for synthesizing plausible inpaintings in a multi-view coherent manner. We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.

The scattering matrix for the full-line matrix Schr\"odinger equation is analyzed when the corresponding matrix-valued potential is selfadjoint, integrable, and has a finite first moment. The matrix-valued potential is decomposed into a finite number of fragments, and a factorization formula is presented expressing the matrix-valued scattering coefficients in terms of the matrix-valued scattering coefficients for the fragments. Using the factorization formula, some explicit examples are provided illustrating that in general the left and right matrix-valued transmission coefficients are unequal. A unitary transformation is established between the full-line matrix Schr\"odinger operator and the half-line matrix Schr\"odinger operator with a particular selfadjoint boundary condition and by relating the full-line and half-line potentials appropriately. Using that unitary transformation, the relations are established between the full-line and the half-line quantities such as the Jost solutions, the physical solutions, and the scattering matrices. Exploiting the connection between the corresponding full-line and half-line scattering matrices, Levinson's theorem on the full line is proved and is related to Levinson's theorem on the half line., Comment: We have slightly edited the paper in order to improve the presentation of the results, and we have added a reference

This paper is devoted to the study of the large-time asymptotics of the small solutions to the matrix nonlinear Schr\"{o}dinger equation with a potential on the half-line and with general selfadjoint boundary condition, and on the line with a potential and a general point interaction, in the whole supercritical regime. We prove that the small solutions are scattering solutions that asymptotically in time, $t \to\pm\infty, $ behave as solutions to the associated linear matrix Schr\"odinger equation with the potential identically zero. The potential can be either generic or exceptional. Our approach is based on detailed results on the spectral and scattering theory for the associated linear matrix Schr\"{o}dinger equation with a potential, and in a factorization technique that allows us to control the large-time behaviour of the solutions in appropriate norms.

Millimeter astronomy provides valuable information on the birthplaces of planetary systems. In order to compare theoretical models with observations, the dust component has to be carefully calculated. Here, we aim to study the effects of dust entrainment in photoevaporative winds and the ejection and drag of dust due to effects caused by radiation from the central star. We improved and extended the existing implementation of a two-population dust and pebble description in the global Bern/Heidelberg planet formation and evolution model. Modern prescriptions for photoevaporative winds were used and we account for settling and advection of dust when calculating entrainment rates. In order to prepare for future population studies with varying conditions, we explore a wide range of disk-, photoevaporation-, and dust-parameters. We find that if dust can grow to pebble sizes, that is, if they are resistant to fragmentation or turbulence is weak, drift dominates and the entrained mass is small but larger than under the assumption of no vertical advection of grains with the gas flow. For the case of fragile dust shattering at velocities of 1 m/s - as indicated in laboratory experiments -, an order of magnitude more dust is entrained which becomes the main dust removal process. Radiation pressure effects disperse massive, dusty disks on timescales of a few 100 Myr. These results highlight the importance of dust entrainment in winds as a solid mass removal process. Furthermore, this model extension lies the basis for future statistical studies of planet formation in their birth environment., Comment: Accepted for publication in A&A