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Zero-shot object pose estimation enables the retrieval of object poses from images without necessitating object-specific training. In recent approaches this is facilitated by vision foundation models (VFM), which are pre-trained models that are effectively general-purpose feature extractors. The characteristics exhibited by these VFMs vary depending on the training data, network architecture, and training paradigm. The prevailing choice in this field are self-supervised Vision Transformers (ViT). This study assesses the influence of Latent Diffusion Model (LDM) backbones on zero-shot pose estimation. In order to facilitate a comparison between the two families of models on a common ground we adopt and modify a recent approach. Therefore, a template-based multi-staged method for estimating poses in a zero-shot fashion using LDMs is presented. The efficacy of the proposed approach is empirically evaluated on three standard datasets for object-specific 6DoF pose estimation. The experiments demonstrate an Average Recall improvement of up to 27% over the ViT baseline. The source code is available at: https://github.com/BvG1993/DZOP.

We study the convergence of two of the most widely used and intuitive approaches for computing the spectra of differential operators with quasiperiodic coefficients: the supercell method and the superspace method. In both cases, Floquet-Bloch theory for periodic operators can be used to compute approximations to the spectrum. We illustrate our results with examples of Schr\"odinger and Helmholtz operators.

Meningeal lymphatic vessels (MLVs) are responsible for the drainage of waste products from the human brain. An impairment in their functionality has been associated with aging as well as brain disorders like multiple sclerosis and Alzheimer's disease. However, MLVs have only recently been described for the first time in magnetic resonance imaging (MRI), and their ramified structure renders manual segmentation particularly difficult. Further, as there is no consistent notion of their appearance, human-annotated MLV structures contain a high inter-rater variability that most automatic segmentation methods cannot take into account. In this work, we propose a new rater-aware training scheme for the popular nnU-Net model, and we explore rater-based ensembling strategies for accurate and consistent segmentation of MLVs. This enables us to boost nnU-Net's performance while obtaining explicit predictions in different annotation styles and a rater-based uncertainty estimation. Our final model, MLV$^2$-Net, achieves a Dice similarity coefficient of 0.806 with respect to the human reference standard. The model further matches the human inter-rater reliability and replicates age-related associations with MLV volume., Comment: ML4H 2024

The eROSITA instrument on board Spectrum-Roentgen-Gamma has completed four scans of the X-ray sky, leading to the detection of almost one million X-ray sources in eRASS1 only, including multiple new X-ray binary candidates. We report on analysis of the X-ray binary 1eRASS J085039.9-421151, using a ~55\,ks long NuSTAR observation, following its detection in each eROSITA scan. Analysis of the eROSITA and NuSTAR X-ray spectra in combination with X-shooter data of the optical counterpart provide evidence of an X-ray binary with a red supergiant (RSG) companion, confirming previous results, although we determine a cooler spectral type of M2-3, owing to the presence of TiO bands in the optical and near infrared spectra. The X-ray spectrum is well-described by an absorbed power law with a high energy cutoff typically applied for accreting high mass X-ray binaries. In addition, we detect a strong fluorescent neutral iron line with an equivalent width of ~700\,eV and an absorption edge, the latter indicating strong absorption by a partial covering component. It is unclear if the partial absorber is ionised. There is no significant evidence of a cyclotron resonant scattering feature. We do not detect any pulsations in the NuSTAR lightcurves, possibly on account of a large spin period that goes undetected due to insufficient statistics at low frequencies or potentially large absorption that causes pulsations to be smeared out. Even so, the low persistent luminosity, the spectral parameters observed (photon index, $\Gamma<1.0$), and the minuscule likelihood of detection of RSG-black hole systems, suggest that the compact object is a neutron star., Comment: 17 pages, 11 figures, accepted by A&A

This contribution is dedicated to the exploration of exponential operator splitting methods for the time integration of evolution equations. It entails the review of previous achievements as well as the depiction of novel results. The standard class of splitting methods involving real coefficients is contrasted with an alternative approach that relies on the incorporation of complex coefficients. In view of long-term computations for linear evolution equations, it is expedient to distinguish symmetric, symmetric-conjugate, and alternating-conjugate schemes. The scope of applications comprises high-order reaction-diffusion equations and complex Ginzburg-Landau equations, which are of relevance in the theories of patterns and superconductivity. Time-dependent Gross-Pitaevskii equations and their parabolic counterparts, which model the dynamics of Bose-Einstein condensates and arise in ground state computations, are formally included as special cases. Numerical experiments confirm the validity of theoretical stability conditions and global error bounds as well as the benefits of higher-order complex splitting methods in comparison with standard schemes.

XTE J1829-098 is a transient X-ray pulsar with a period of ~7.8 s. It is a candidate Be star system, although the evidence for this is not yet definitive. We investigated the twenty-year long X-ray light curve using the Rossi X-ray Timing Explorer Proportional Counter Array (PCA), Neil Gehrels Swift Observatory Burst Alert Telescope (BAT), and the Monitor of All-sky X-ray Image (MAXI). We find that all three light curves are clearly modulated on the ~244 day orbital period previously reported from PCA monitoring observations, with outbursts confined to a narrow phase range. The light curves also show that XTE J1829-098 was in an inactive state between approximately December 2008 and April 2018 and no strong outbursts occurred. Such behavior is typical of Be X-ray binary systems, with the absence of outbursts likely related to the dissipation of the Be star's decretion disk. The mean outburst shapes can be approximated with a triangular profile and, from a joint fit of this to all three light curves, we refine the orbital period to 243.95 +/- 0.04 days. The mean outburst profile does not show any asymmetry and has a total phase duration of 0.140 +/- 0.007. However, the PCA light curve shows that there is considerable cycle-to-cycle variability of the individual outbursts. We compare the properties of XTE J1829-098 with other sources that show short phase-duration outbursts, in particular GS 1843-02 (2S 1845-024) which has a very similar orbital period, but longer pulse period, and whose orbit is known to be highly eccentric., Comment: Accepted for publication in the Astrophysical Journal. 21 pages

Robots are increasingly envisioned to interact in real-world scenarios, where they must continuously adapt to new situations. To detect and grasp novel objects, zero-shot pose estimators determine poses without prior knowledge. Recently, vision language models (VLMs) have shown considerable advances in robotics applications by establishing an understanding between language input and image input. In our work, we take advantage of VLMs zero-shot capabilities and translate this ability to 6D object pose estimation. We propose a novel framework for promptable zero-shot 6D object pose estimation using language embeddings. The idea is to derive a coarse location of an object based on the relevancy map of a language-embedded NeRF reconstruction and to compute the pose estimate with a point cloud registration method. Additionally, we provide an analysis of LERF's suitability for open-set object pose estimation. We examine hyperparameters, such as activation thresholds for relevancy maps and investigate the zero-shot capabilities on an instance- and category-level. Furthermore, we plan to conduct robotic grasping experiments in a real-world setting.

This guide is for everyone who seeks advice for creating stable, secure, and persistent Uniform Resource Identifiers (URIs) in order to publish their data in accordance to the FAIR principles. The use case does not matter. It could range from publishing the results of a small research project to a large knowledge graph at a big corporation. The FAIR principles apply equally and this is why it is important to put extra thought into the URI selection process. The title aims to extend the tradition of "Cool URIs don't change" and "Cool URIs for the Semantic Web". Much has changed since the publication of these works and we would like to revisit some of the principles. Many still hold today, some had to be reworked, and we could also identify new ones

Soft gamma-ray emission (100 keV -- 10 MeV) has previously been detected in the hard state of several microquasars. In some sources, this emission was found to be highly polarized and was suggested to be emitted at the base of the jet. Until now, no $\gamma$-ray polarization had been found in any other state. Using INTEGRAL/IBIS, we studied the soft gamma-ray spectral and polarization properties of Swift J1727.8-1613 throughout its outburst. We detect a highly polarized spectral component in both the hard intermediate state and the early stages of the soft intermediate state above 210 keV. In the hard intermediate state, the polarization angle significantly deviates from the compact jet angle projected onto the sky, whereas in the soft intermediate they are closely aligned. This constitutes the first detection of jet-aligned polarization in the soft gamma-ray for a microquasar. We attribute this polarized spectral component to synchrotron emission from the jet, which indicates that some of the jet might persist into the softer states., Comment: 7 pages, 4 figures. Accepted for publication in A&A Letters

Robotic search and rescue (SAR) supports response teams by accelerating disaster assessment and by keeping operators away from hazardous environments. In the event of a chemical, biological, radiological, and nuclear (CBRN) disaster, robots are deployed to identify and locate radiation sources. Human responders then assess the situation and neutralize the danger. The presented system takes a step toward enhanced integration of robots into SAR teams. Integrating autonomous radiation mapping with semi-autonomous substance sampling and online analysis of the CBRN threat lets the human operator localize and assess the threat from a safe distance. Two LiDARs, an IMU, and a Geiger counter are used for mapping the surrounding area and localizing potential radiation sources. A mobile manipulator with six Degrees of Freedom manipulates valves and samples substances that are analyzed by an onboard Raman spectrometer. The human operator monitors the mission's progression from a remote location defining target locations and directing the semi-autonomous manipulation processes. Diverse recovery behaviours aid robot deployment, system state monitoring, as well as recovery of hard- and software. Field tests showcase the capabilities of the presented system during trials at the CBRN disaster response challenge European Robotics Hackathon (EnRicH). We provide recorded sensor data and implemented software through a GitHub repository: https://github.com/TW-Robotics/search-and-rescue-robot-2024.

In 2021, the high-mass X-ray binary EXO 2030+375 underwent a giant X-ray outburst, the first since 2006, that reached a peak flux of ${\sim}600\,\mathrm{mCrab}$ (3-50\,keV). The goal of this work is to study the spectral evolution over the course of the outburst, search for possible cyclotron resonance scattering features (CRSFs), and to associate spectral components with the emission pattern of the accretion column. We used broadband spectra taken with the Nuclear Spectroscopic Telescope Array (NuSTAR), the Neutron Star Interior Composition Explorer (NICER), and Chandra near the peak and during the decline phase of the outburst. We describe the data with established empirical continuum models and perform pulse-phase-resolved spectroscopy. We compare the spectral evolution with pulse phase using a proposed geometrical emission model. We find a significant spectral hardening toward lower luminosity, a behavior that is expected for super-critical sources. The continuum shape and evolution cannot be described by a simple power-law model with exponential cutoff; it requires additional absorption or emission components. We can confirm the presence of a narrow absorption feature at ${\sim}10\,\mathrm{keV}$ in both NuSTAR observations. The absence of harmonics puts into question the interpretation of this feature as a CRSF. The empirical spectral components cannot be directly associated with identified emission components from the accretion column., Comment: 12 pages, 8 figures, accepted in A&A

The Be X-ray binary EXO 2030+375 went through its third recorded giant outburst from June 2021 to early 2022. We present the results of both spectral and timing analysis based on NICER monitoring, covering the 2-10 keV flux range from 20 to 310 mCrab. Dense monitoring with observations carried out about every second day and a total exposure time of 160 ks allowed us to closely track the source evolution over the outburst. Changes in spectral shape and pulse profiles showed a stable luminosity dependence during the rise and decline. The same type of dependence has been seen in past outbursts. The pulse profile is characterized by several distinct peaks and dips. The profiles show a clear dependence on luminosity with a stark transition at a luminosity of 2x10^36 erg/s, indicating a change in the emission pattern. Using relativistic ray-tracing, we demonstrate how anisotropic beaming of emission from an accretion channel with constant geometrical configuration can give rise to the observed pulse profiles over a range of luminosities., Comment: 15 pages, 16 figures, accepted by A&A

The Neutron Star Interior Composition Explorer (NICER) monitoring campaign of Cyg X-1 allows us to study its spectral-timing behavior at energies ${<}1$ keV across all states. The hard state power spectrum can be decomposed into two main broad Lorentzians with a transition at around 1 Hz. The lower-frequency Lorentzian is the dominant component at low energies. The higher-frequency Lorentzian begins to contribute significantly to the variability above 1.5 keV and dominates at high energies. We show that the low- and high-frequency Lorentzians likely represent individual physical processes. The lower-frequency Lorentzian can be associated with a (possibly Comptonized) disk component, while the higher-frequency Lorentzian is clearly associated with the Comptonizing plasma. At the transition of these components, we discover a low-energy timing phenomenon characterized by an abrupt lag change of hard (${\gtrsim}2$ keV) with respect to soft (${\lesssim}1.5$ keV) photons, accompanied by a drop in coherence, and a reduction in amplitude of the second broad Lorentzian. The frequency of the phenomenon increases with the frequencies of the Lorentzians as the source softens and cannot be seen when the power spectrum is single-humped. A comparison to transient low-mass X-ray binaries shows that this feature does not only appear in Cyg X-1, but that it is a general property of accreting black hole binaries. In Cyg X-1, we find that the variability at low and high energies is overall highly coherent in the hard and intermediate states. The high coherence shows that there is a process at work which links the variability, suggesting a physical connection between the accretion disk and Comptonizing plasma. This process fundamentally changes in the soft state, where strong red noise at high energies is incoherent to the variability at low energies., Comment: 18+29 pages, 17+54 figures, accepted for publication in Astronomy & Astrophysics

Context. The High-Mass X-ray Binary (HMXB) system GX 301$-$2 is a persistent source with a well-known variable cyclotron line centered at 35 keV. Recently, a second cyclotron line at 50 keV has been reported with a presumably different behavior than the 35 keV line. Aims. We investigate the presence of the newly discovered cyclotron line in the phase-averaged and phase-resolved spectra at higher luminosities than before. We further aim to determine the pulse-phase variability of both lines. Methods. We analyze a NuSTAR observation of GX 301$-$2 covering the pre-periastron flare, where the source luminosity reached its peak of ${\sim} 4 \times 10^{37}\,\mathrm{erg}\,\mathrm{s}^{-1}$ in the 5-50 keV range. We analyze the phase-averaged spectra in the NuSTAR energy range from 3.5-79 keV for both the complete observation and three time segments of it. We further analyze the phase-resolved spectra and the pulse-phase variability of continuum and cyclotron line parameters. Results. We confirm that the description of the phase-averaged spectrum requires a second absorption feature at $51.5^{+1.1}_{-1.0}$ keV besides the established line at 35 keV. The statistical significance of this feature in the phase-averaged spectrum is $>99.999\%$. We further find that the 50 keV cyclotron line is present in three of eight phase bins. Conclusions. Based on the results of our analysis, we confirm that the detected absorption feature is very likely to be a cyclotron line. We discuss a variety of physical scenarios which could explain the proposed anharmonicity, but also outline circumstances under which the lines are harmonically related. We further present the cyclotron line history of GX 301$-$2 and evaluate concordance among each other. We also discuss an alternative spectral model including cyclotron line emission wings., Comment: accepted for publication in Astronomy & Astrophysics, 20 pages, 16 figures

Estimating 2D-3D correspondences between RGB images and 3D space is a fundamental problem in 6D object pose estimation. Recent pose estimators use dense correspondence maps and Point-to-Point algorithms to estimate object poses. The accuracy of pose estimation depends heavily on the quality of the dense correspondence maps and their ability to withstand occlusion, clutter, and challenging material properties. Currently, dense correspondence maps are estimated using image-to-image translation models based on GANs, Autoencoders, or direct regression models. However, recent advancements in image-to-image translation have led to diffusion models being the superior choice when evaluated on benchmarking datasets. In this study, we compare image-to-image translation networks based on GANs and diffusion models for the downstream task of 6D object pose estimation. Our results demonstrate that the diffusion-based image-to-image translation model outperforms the GAN, revealing potential for further improvements in 6D object pose estimation models., Comment: Submitted to the First Austrian Symposium on AI, Robotics, and Vision 2024

Recent advances in machine learning have greatly benefited object detection and 6D pose estimation. However, textureless and metallic objects still pose a significant challenge due to few visual cues and the texture bias of CNNs. To address his issue, we propose a strategy for inducing a shape bias to CNN training. In particular, by randomizing textures applied to object surfaces during data rendering, we create training data without consistent textural cues. This methodology allows for seamless integration into existing data rendering engines, and results in negligible computational overhead for data rendering and network training. Our findings demonstrate that the shape bias we induce via randomized texturing, improves over existing approaches using style transfer. We evaluate with three detectors and two pose estimators. For the most recent object detector and for pose estimation in general, estimation accuracy improves for textureless and metallic objects. Additionally we show that our approach increases the pose estimation accuracy in the presence of image noise and strong illumination changes. Code and datasets are publicly available at github.com/hoenigpeter/randomized_texturing.

When applying Hamiltonian operator splitting methods for the time integration of multi-species Vlasov-Maxwell-Landau systems, the reliable and efficient numerical approximation of the Landau equation represents a fundamental component of the entire algorithm. Substantial computational issues arise from the treatment of the physically most relevant three-dimensional case with Coulomb interaction. This work is concerned with the introduction and numerical comparison of novel approaches for the evaluation of the Landau collision operator. In the spirit of collocation, common tools are the identification of fundamental integrals, series expansions of the integral kernel and the density function on the main part of the velocity domain, and interpolation as well as quadrature approximation nearby the singularity of the kernel. Focusing on the favourable choice of the Fourier spectral method, their practical implementation uses the reduction to basic integrals, fast Fourier techniques, and summations along certain directions. Moreover, an important observation is that a significant percentage of the overall computational effort can be transferred to precomputations which are independent of the density function. For the purpose of exposition and numerical validation, the cases of constant, regular, and singular integral kernels are distinguished, and the procedure is adapted accordingly to the increasing complexity of the problem. With regard to the time integration of the Landau equation, the most expedient approach is applied in such a manner that the conservation of mass is ensured.

We establish new results on the spectra and pseudo-spectra of tridiagonal $k$-Toeplitz operators and matrices. In particular, we prove the connection between the winding number of the eigenvalues of the symbol function and the exponential decay of the associated eigenvectors (or pseudo-eigenvectors). Our results elucidate the topological origin of the non-Hermitian skin effect in general one-dimensional polymer systems of subwavelength resonators with imaginary gauge potentials, proving the observation and conjecture in arXiv:2307.13551. We also numerically verify our theory for these systems., Comment: 18 pages, 6 figures

The present work provides a comprehensive study of symmetric-conjugate operator splitting methods in the context of linear parabolic problems and demonstrates their additional benefits compared to symmetric splitting methods. Relevant applications include nonreversible systems and ground state computations for linear Schr\"odinger equations based on the imaginary time propagation. Numerical examples confirm the favourable error behaviour of higher-order symmetric-conjugate splitting methods and illustrate the usefulness of a time stepsize control, where the local error estimation relies on the computation of the imaginary parts and thus requires negligible costs., Comment: Paper to be published in Journal of Computational Dynamics

The present work is concerned with the extension of modified potential operator splitting methods to specific classes of nonlinear evolution equations. The considered partial differential equations of Schr{\"o}dinger and parabolic type comprise the Laplacian, a potential acting as multiplication operator, and a cubic nonlinearity. Moreover, an invariance principle is deduced that has a significant impact on the efficient realisation of the resulting modified operator splitting methods for the Schr{\"o}dinger case.} Numerical illustrations for the time-dependent Gross--Pitaevskii equation in the physically most relevant case of three space dimensions and for its parabolic counterpart related to ground state and excited state computations confirm the benefits of the proposed fourth-order modified operator splitting method in comparison with standard splitting methods. The presented results are novel and of particular interest from both, a theoretical perspective to inspire future investigations of modified operator splitting methods for other classes of nonlinear evolution equations and a practical perspective to advance the reliable and efficient simulation of Gross--Pitaevskii systems in real and imaginary time., Comment: 30 pages, 6 figures

As robotic systems increasingly encounter complex and unconstrained real-world scenarios, there is a demand to recognize diverse objects. The state-of-the-art 6D object pose estimation methods rely on object-specific training and therefore do not generalize to unseen objects. Recent novel object pose estimation methods are solving this issue using task-specific fine-tuned CNNs for deep template matching. This adaptation for pose estimation still requires expensive data rendering and training procedures. MegaPose for example is trained on a dataset consisting of two million images showing 20,000 different objects to reach such generalization capabilities. To overcome this shortcoming we introduce ZS6D, for zero-shot novel object 6D pose estimation. Visual descriptors, extracted using pre-trained Vision Transformers (ViT), are used for matching rendered templates against query images of objects and for establishing local correspondences. These local correspondences enable deriving geometric correspondences and are used for estimating the object's 6D pose with RANSAC-based PnP. This approach showcases that the image descriptors extracted by pre-trained ViTs are well-suited to achieve a notable improvement over two state-of-the-art novel object 6D pose estimation methods, without the need for task-specific fine-tuning. Experiments are performed on LMO, YCBV, and TLESS. In comparison to one of the two methods we improve the Average Recall on all three datasets and compared to the second method we improve on two datasets.

Background: We aimed at improving image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determining the best tradeoff between number of views, IQ, and diagnostic confidence. Methods: CT images from 41 subjects aged 62.8 $\pm$ 10.6 years (mean $\pm$ standard deviation), 23 men, 34 with lung metastasis, 7 healthy, were retrospectively selected (2016-2018) and forward projected onto 2,048-view sinograms. Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views. A dual-frame U-Net was trained and evaluated for each subsampling level on 8,658 images from 22 diseased subjects. A representative image per scan was selected from 19 subjects (12 diseased, 7 healthy) for a single-blinded multireader study. These slices, for all levels of subsampling, with and without U-Net postprocessing, were presented to three readers. IQ and diagnostic confidence were ranked using predefined scales. Subjective nodule segmentation was evaluated using sensitivity and Dice similarity coefficient (DSC); clustered Wilcoxon signed-rank test was used. Results: The 64-projection sparse-view images resulted in 0.89 sensitivity and 0.81 DSC, while their counterparts, postprocessed with the U-Net, had improved metrics (0.94 sensitivity and 0.85 DSC) (p = 0.400). Fewer views led to insufficient IQ for diagnosis. For increased views, no substantial discrepancies were noted between sparse-view and postprocessed images. Conclusions: Projection views can be reduced from 2,048 to 64 while maintaining IQ and the confidence of the radiologists on a satisfactory level.

Object pose estimation is a core perception task that enables, for example, object grasping and scene understanding. The widely available, inexpensive and high-resolution RGB sensors and CNNs that allow for fast inference based on this modality make monocular approaches especially well suited for robotics applications. We observe that previous surveys on object pose estimation establish the state of the art for varying modalities, single- and multi-view settings, and datasets and metrics that consider a multitude of applications. We argue, however, that those works' broad scope hinders the identification of open challenges that are specific to monocular approaches and the derivation of promising future challenges for their application in robotics. By providing a unified view on recent publications from both robotics and computer vision, we find that occlusion handling, novel pose representations, and formalizing and improving category-level pose estimation are still fundamental challenges that are highly relevant for robotics. Moreover, to further improve robotic performance, large object sets, novel objects, refractive materials, and uncertainty estimates are central, largely unsolved open challenges. In order to address them, ontological reasoning, deformability handling, scene-level reasoning, realistic datasets, and the ecological footprint of algorithms need to be improved., Comment: Accepted for publication in Transactions on Robotics: https://ieeexplore.ieee.org/document/10609560 arXiv admin note: substantial text overlap with arXiv:2302.11827

Object pose estimation is important for object manipulation and scene understanding. In order to improve the general applicability of pose estimators, recent research focuses on providing estimates for novel objects, that is objects unseen during training. Such works use deep template matching strategies to retrieve the closest template connected to a query image. This template retrieval implicitly provides object class and pose. Despite the recent success and improvements of Vision Transformers over CNNs for many vision tasks, the state of the art uses CNN-based approaches for novel object pose estimation. This work evaluates and demonstrates the differences between self-supervised CNNs and Vision Transformers for deep template matching. In detail, both types of approaches are trained using contrastive learning to match training images against rendered templates of isolated objects. At test time, such templates are matched against query images of known and novel objects under challenging settings, such as clutter, occlusion and object symmetries, using masked cosine similarity. The presented results not only demonstrate that Vision Transformers improve in matching accuracy over CNNs, but also that for some cases pre-trained Vision Transformers do not need fine-tuning to do so. Furthermore, we highlight the differences in optimization and network architecture when comparing these two types of network for deep template matching.

This is a preprint. The latest version has been published here: https://pubs.rsna.org/doi/10.1148/ryai.230275 Purpose: Sparse-view computed tomography (CT) is an effective way to reduce dose by lowering the total number of views acquired, albeit at the expense of image quality, which, in turn, can impact the ability to detect diseases. We explore deep learning-based artifact reduction in sparse-view cranial CT scans and its impact on automated hemorrhage detection. Methods: We trained a U-Net for artefact reduction on simulated sparse-view cranial CT scans from 3000 patients obtained from a public dataset and reconstructed with varying levels of sub-sampling. Additionally, we trained a convolutional neural network on fully sampled CT data from 17,545 patients for automated hemorrhage detection. We evaluated the classification performance using the area under the receiver operator characteristic curves (AUC-ROCs) with corresponding 95% confidence intervals (CIs) and the DeLong test, along with confusion matrices. The performance of the U-Net was compared to an analytical approach based on total variation (TV). Results: The U-Net performed superior compared to unprocessed and TV-processed images with respect to image quality and automated hemorrhage diagnosis. With U-Net post-processing, the number of views can be reduced from 4096 (AUC-ROC: 0.974; 95% CI: 0.972-0.976) views to 512 views (0.973; 0.971-0.975) with minimal decrease in hemorrhage detection (P<.001) and to 256 views (0.967; 0.964-0.969) with a slight performance decrease (P<.001). Conclusion: The results suggest that U-Net based artifact reduction substantially enhances automated hemorrhage detection in sparse-view cranial CTs. Our findings highlight that appropriate post-processing is crucial for optimal image quality and diagnostic accuracy while minimizing radiation dose., Comment: 11 pages, 6 figures, 1 table

We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. 7,194 CT images (3,597 with COPD; 3,597 healthy controls) from 78 subjects (43 with COPD; 35 healthy controls) were selected retrospectively (10.2018-12.2019) and preprocessed. For each image, intensity values were manually clipped to the emphysema window setting and a baseline 'full-range' window setting. Class-balanced train, validation, and test sets contained 3,392, 1,114, and 2,688 images. The network backbone was optimized by comparing various CNN architectures. Furthermore, automated WSO was implemented by adding a customized layer to the model. The image-level area under the Receiver Operating Characteristics curve (AUC) [lower, upper limit 95% confidence] was utilized to compare model variations. Repeated inference (n=7) on the test set showed that the DenseNet was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85] without WSO. Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89]. By adding a customized WSO layer to the DenseNet, an optimal window in the proximity of the emphysema window setting was learned automatically, and a mean AUC of 0.82 [0.78, 0.86] was achieved. Detection of COPD with DenseNet models was improved by WSO of CT data to the emphysema window setting range.

Object pose estimation is a non-trivial task that enables robotic manipulation, bin picking, augmented reality, and scene understanding, to name a few use cases. Monocular object pose estimation gained considerable momentum with the rise of high-performing deep learning-based solutions and is particularly interesting for the community since sensors are inexpensive and inference is fast. Prior works establish the comprehensive state of the art for diverse pose estimation problems. Their broad scopes make it difficult to identify promising future directions. We narrow down the scope to the problem of single-shot monocular 6D object pose estimation, which is commonly used in robotics, and thus are able to identify such trends. By reviewing recent publications in robotics and computer vision, the state of the art is established at the union of both fields. Following that, we identify promising research directions in order to help researchers to formulate relevant research ideas and effectively advance the state of the art. Findings include that methods are sophisticated enough to overcome the domain shift and that occlusion handling is a fundamental challenge. We also highlight problems such as novel object pose estimation and challenging materials handling as central challenges to advance robotics., Comment: Revised version in the making

Transparent objects are common in day-to-day life and hence find many applications that require robot grasping. Many solutions toward object grasping exist for non-transparent objects. However, due to the unique visual properties of transparent objects, standard 3D sensors produce noisy or distorted measurements. Modern approaches tackle this problem by either refining the noisy depth measurements or using some intermediate representation of the depth. Towards this, we study deep learning 6D pose estimation from RGB images only for transparent object grasping. To train and test the suitability of RGB-based object pose estimation, we construct a dataset of RGB-only images with 6D pose annotations. The experiments demonstrate the effectiveness of RGB image space for grasping transparent objects.

When accreting X-ray pulsars (XRPs) undergo bright X-ray outbursts, their luminosity-dependent spectral and timing features can be analysed in detail. The XRP GRO J1750-27 recently underwent one of such episodes, during which it was observed with $NuSTAR$ and monitored with $NICER$. Such a data set is rarely available, as it samples the outburst over more than a month at a luminosity that is always exceeding ${\sim}5\times10^{37}\,$erg/s. This value is larger than the typical critical luminosity value, where a radiative shock is formed above the neutron star's surface. Our data analysis of the joint spectra returns a highly ($N_H\sim(5-8)\times10^{22}\,$cm$^{-2}$) absorbed spectrum showing a K$\alpha$ iron line, a soft blackbody component likely originating from the inner edge of the accretion disk, and confirms the discovery of one of the deepest cyclotron lines, at a centroid energy of ${\sim}44\,$keV corresponding to a magnetic field strength of $4.7\times10^{12}\,$G. This value is independently supported by the best-fit physical model for spectral formation in accreting XRPs which, in agreement with recent findings, favours a distance of $14$ kpc and also reflects a bulk-Comptonization dominated accretion flow. Contrary to theoretical expectations and observational evidence from other similar sources, the pulse profiles as observed by $NICER$ through the outburst raise, peak and decay remain remarkably steady. The $NICER$ spectrum, including the iron K$\alpha$ line best-fit parameters, also remain almost unchanged at all probed outburst stages, similar to the pulsed fraction behaviour. We argue that all these phenomena are linked and interpret them as resulting from a saturation effect of the accretion column's emission, which occurs in the high-luminosity regime., Comment: Accepted on A&A -- proof version

Due to the structure of the NuSTAR telescope, photons at large off-axis (> 1deg) can reach the detectors directly (stray light), without passing through the instrument optics. At these off-axis angles NuSTAR essentially turns into a collimated instrument and the spectrum can extend to energies above the Pt k-edge (79 keV) of the multi-layers, which limits the effective area bandpass of the optics. We present the first scientific spectral analysis beyond 79 keV using a Cygnus X-1 observation in StrayCats, the catalog of stray light observations. This serendipitous stray light observation occurred simultaneously with an INTEGRAL observation. When the spectra are modeled together in the 30-120 keV energy band, we find that the NuSTAR stray light flux is well calibrated and constrained to be consistent with the INTEGRAL flux at the 90% confidence level. Furthermore, we explain how to treat the background of the stray light spectral analysis, which is especially important at high energies., Comment: Accepted for publication in ApJ

State-of-the-art object pose estimation handles multiple instances in a test image by using multi-model formulations: detection as a first stage and then separately trained networks per object for 2D-3D geometric correspondence prediction as a second stage. Poses are subsequently estimated using the Perspective-n-Points algorithm at runtime. Unfortunately, multi-model formulations are slow and do not scale well with the number of object instances involved. Recent approaches show that direct 6D object pose estimation is feasible when derived from the aforementioned geometric correspondences. We present an approach that learns an intermediate geometric representation of multiple objects to directly regress 6D poses of all instances in a test image. The inherent end-to-end trainability overcomes the requirement of separately processing individual object instances. By calculating the mutual Intersection-over-Unions, pose hypotheses are clustered into distinct instances, which achieves negligible runtime overhead with respect to the number of object instances. Results on multiple challenging standard datasets show that the pose estimation performance is superior to single-model state-of-the-art approaches despite being more than ~35 times faster. We additionally provide an analysis showing real-time applicability (>24 fps) for images where more than 90 object instances are present. Further results show the advantage of supervising geometric-correspondence-based object pose estimation with the 6D pose.

Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nanoparticle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration., Comment: 181 pages, 61 figures

A black hole x-ray binary (XRB) system forms when gas is stripped from a normal star and accretes onto a black hole, which heats the gas sufficiently to emit x-rays. We report a polarimetric observation of the XRB Cygnus X-1 using the Imaging x-ray Polarimetry Explorer. The electric field position angle aligns with the outflowing jet, indicating that the jet is launched from the inner x-ray emitting region. The polarization degree is (4.01+-0.20)% at 2 to 8 kiloelectronvolts, implying that the accretion disk is viewed closer to edge-on than the binary orbit. The observations reveal that hot x-ray emitting plasma is spatially extended in a plane perpendicular to the jet axis, not parallel to the jet., Comment: 51 pages, 15 figures, 5 tables

Dynamics of large-scale network processes underlies crucial phenomena ranging across all sciences. Forward simulation of large network models is often computationally prohibitive. Yet, most networks have intrinsic community structure. We exploit these communities and propose a fast simulation algorithm for network dynamics. In particular, aggregating the inputs a node receives constitutes the limiting factor in numerically simulating large-scale network dynamics. We develop community integration algorithms (CIAs) significantly reducing function-evaluations. We obtain a substantial reduction from polynomial to linear computational complexity. We illustrate our results in multiple applications including classical and higher-order Kuramoto-type systems for synchronisation and Cucker--Smale systems exhibiting flocking behaviour on synthetic as well as real-world networks. Numerical comparison and theoretical analysis confirm the robustness and efficiency of CIAs., Comment: 12 pages, 6 figures

We present a method to measure the optical torque applied to particles of arbitrary shape held in an optical trap, inferred from the change of angular momentum of light induced by the particle. All torque components can be determined from a single interference pattern recorded by a camera in the back focal plane of a high-NA condenser collecting all forward scattered light. We derive explicit expressions mapping the measured complex field in this plane to the torque components. The required phase is retrieved by an iterative algorithm, using the known position of the optical traps as constraints. The torque pertaining to individual particles is accessible, as well as separate spin or orbital parts of the total torque., Comment: V2, with updated figures

Purpose: We aimed to estimate the total lung volume (TLV) from real and synthetic frontal X-ray radiographs on a pixel level using lung thickness maps generated by a U-Net. Methods: 5,959 thorax X-ray computed tomography (CT) scans were retrieved from two publicly available datasets of the lung nodule analysis 2016 (n=656) and the RSNA pulmonary embolism detection challenge 2020 (n=5,303). Additionally, thorax CT scans from 72 subjects (33 healthy: 20 men, mean age [range] = 62.4 [34, 80]; 39 suffering from chronic obstructive pulmonary disease: 25 men, mean age [range] = 69.0 [47, 91]) were retrospectively selected (10.2018-12.2019) from our in-house dataset such that for each subject, a frontal chest X-ray radiograph no older than seven days was available. All CT scans and their corresponding lung segmentation were forward projected using a simulated X-ray spectrum to generate synthetic radiographs and lung thickness maps, respectively. A U-Net model was trained and tested on synthetic radiographs from the public datasets to predict lung thickness maps and consequently estimate TLV. Model performance was further assessed by evaluating the TLV estimations for the in-house synthetic and real radiograph pairs using Pearson correlation coefficient (r) and significance testing. Results: Strong correlations were measured between the predicted and CT-derived ground truth TLV values for test data from synthetic ($n_{Public}$=1,191, r=0.987, P < 0.001; $n_{In-house}$=72, r=0.973, P < 0.001) and real radiographs (n=72, r=0.908, P < 0.001). Conclusion: TLV from U-Net-generated pixel-level lung thickness maps were successfully estimated for synthetic and real radiographs.

Due to the complexity of modeling the radiative transfer inside the accretion columns of neutron star binaries, their X-ray spectra are still commonly described with phenomenological models, for example, a cutoff power law. While the behavior of these models is well understood and they allow for a comparison of different sources and studying source behavior, the extent to which the underlying physics can be derived from the model parameters is very limited. During recent years, several physically motivated spectral models have been developed to overcome these limitations. Their application, however, is generally computationally much more expensive and they require a high number of parameters which are difficult to constrain. Previous works have presented an analytical solution to the radiative transfer equation inside the accretion column assuming a velocity profile that is linear in the optical depth. An implementation of this solution that is both fast and accurate enough to be fitted to observed spectra is available as a model in XSPEC. The main difficulty of this implementation is that some solutions violate energy conservation and therefore have to be rejected by the user. We propose a novel fitting strategy that ensures energy conservation during the $\chi^2$-minimization which simplifies the application of the model considerably. We demonstrate this approach as well a study of possible parameter degeneracies with a comprehensive Markov-chain Monte Carlo analysis of the complete parameter space for a combined NuSTAR and Swift/XRT dataset of Cen X-3. The derived accretion-flow structure features a small column radius of $\sim$63 m and a spectrum dominated by bulk-Comptonization of bremsstrahlung seed photons, in agreement with previous studies., Comment: 14 pages, 7 figures, A&A accepted

The results of the broadband spectral and timing study of the recently discovered transient X-ray pulsar MAXI J0903-531 in a wide range of luminosities differing by a factor of ~30 are reported. The observed X-ray spectrum in both states can be described as a classical pulsar-like spectrum consisting of the power-law with the high-energy cutoff. We argue that absence of the spectrum transformation to the two-hump structure expected at low fluxes points to a relatively weak magnetic field of the neutron star below (2-3)$\times10^{12}$ G. This estimate is consistent with other indirect constraints and non-detection of any absorption features which can be interpreted as a cyclotron absorption line. Timing analysis of the NuSTAR data revealed only slight variations of a single-peaked pulse profile of the source as a function of the energy band and mass accretion rate. In both intensity states the pulsed fraction increases from 40% to roughly 80% with the energy. Finally we were also able to obtain the orbital solution for the binary system using data from the Fermi/GBM, NICER and NuSTAR instruments., Comment: Accepted to A&A for publication in the Special Issue: The Early Data Release of eROSITA and Mikhail Pavlinsky ART-XC on the SRG Mission. 8 pages, 6 figures

The blazar Mrk 421 shows frequent, short flares in the TeV energy regime. Due to the fast nature of such episodes, we often fail to obtain sufficient simultaneous information about flux variations in several energy bands. To overcome this lack of multi-wavelength (MWL) coverage, especially for the pre- and post-flare periods, we have set up a monitoring program with the FACT telescope (TeV energies) and the Neil Gehrels Swift Observatory (X-rays). On 2019 June 9, Mrk 421 showed a TeV outburst reaching a flux level of more than two times the flux of the Crab Nebula at TeV energies. We acquired simultaneous data in the X-rays with additional observations by XMM-Newton and INTEGRAL. For the first time, we can study a TeV blazar in outburst taking advantage of highly sensitive X-ray data from XMM-Newton and INTEGRAL combined. Our dataset is complemented by pointed radio observations by Effelsberg at GHz frequencies. We present our first results, including the {\gamma}-ray and X-ray light curves, a timing analysis of the X-ray data obtained with XMM-Newton , as well as the radio spectra before, during and after the flare., Comment: 8 pages, 4 figures; Proceedings of the 37th International Cosmic Ray Conference (ICRC2021). 12-23 July, 2021. Berlin, Germany. Online at https://pos.sissa.it/395/

The clean world of digital information is based on noisy physical devices. Landauer's principle provides a deep connection between information processing and the underlying thermodynamics by setting a lower limit on the energy consumption and heat production of logically irreversible transformations. While Landauer's original formulation assumes equilibrium, real devices often do operate far from equilibrium. We show experimentally that the nonequilibrium character of a memory state enables full erasure with reduced power consumption as well as negative heat production. We implement the optimized erasure protocols in an optomechanical two-state memory. To this end, we introduce dynamical shaping of nonlinear potential landscapes as a powerful tool for levitodynamics as well as the investigation of far-from-equilibrium processes., Comment: RevTex 4-2, 6 pages, 4 figures

Recent observations of X-ray pulsars at low luminosities allow, for the first time, to compare theoretical models for the emission from highly magnetized neutron star atmospheres at low mass accretion rates ($\dot{M} \lesssim 10^{15}$ g s$^{-1}$) with the broadband X-ray data. The purpose of this paper is to investigate the spectral formation in the neutron star atmosphere at low $\dot{M}$ and to conduct a parameter study of physical properties of the emitting region. We obtain the structure of the static atmosphere, assuming that Coulomb collisions are the dominant deceleration process. The upper part of the atmosphere is strongly heated by the braking plasma, reaching temperatures of 30-40 keV, while its denser isothermal interior is much cooler (~2 keV). We numerically solve the polarized radiative transfer in the atmosphere with magnetic Compton scattering, free-free processes, and non-thermal cyclotron emission due to possible collisional excitations of electrons. The strongly polarized emitted spectrum has a double-hump shape that is observed in low-luminosity X-ray pulsars. A low-energy "thermal" component is dominated by extraordinary photons that can leave the atmosphere from deeper layers due to their long mean free path at soft energies. We find that a high-energy component is formed due to resonant Comptonization in the heated non-isothermal part of the atmosphere even in the absence of collisional excitations. The latter, however, affect the ratio of the two components. A strong cyclotron line originates from the optically thin, uppermost zone. A fit of the model to NuSTAR and Swift/XRT observations of GX 304-1 provides an accurate description of the data with reasonable parameters. The model can thus reproduce the characteristic double-hump spectrum observed in low-luminosity X-ray pulsars and provides insights into spectral formation., Comment: 18 pages, 10 figures, A&A accepted

In this work, a novel approach for the reliable and efficient numerical integration of the Kuramoto model on graphs is studied. For this purpose, the notion of order parameters is revisited for the classical Kuramoto model describing all-to-all interactions of a set of oscillators. First numerical experiments confirm that the precomputation of certain sums significantly reduces the computational cost for the evaluation of the right-hand side and hence enables the simulation of high-dimensional systems. In order to design numerical integration methods that are favourable in the context of related dynamical systems on network graphs, the concept of localised order parameters is proposed. In addition, the detection of communities for a complex graph and the transformation of the underlying adjacency matrix to block structure is an essential component for further improvement. It is demonstrated that for a submatrix comprising relatively few coefficients equal to zero, the precomputation of sums is advantageous, whereas straightforward summation is appropriate in the complementary case. Concluding theoretical considerations and numerical comparisons show that the strategy of combining effective community detection algorithms with the localisation of order parameters potentially reduces the computation time by several orders of magnitude., Comment: 33 pages, 19 figures

Object pose estimation enables robots to understand and interact with their environments. Training with synthetic data is necessary in order to adapt to novel situations. Unfortunately, pose estimation under domain shift, i.e., training on synthetic data and testing in the real world, is challenging. Deep learning-based approaches currently perform best when using encoder-decoder networks but typically do not generalize to new scenarios with different scene characteristics. We argue that patch-based approaches, instead of encoder-decoder networks, are more suited for synthetic-to-real transfer because local to global object information is better represented. To that end, we present a novel approach based on a specialized feature pyramid network to compute multi-scale features for creating pose hypotheses on different feature map resolutions in parallel. Our single-shot pose estimation approach is evaluated on multiple standard datasets and outperforms the state of the art by up to 35%. We also perform grasping experiments in the real world to demonstrate the advantage of using synthetic data to generalize to novel environments.

The objective of this work is the introduction and investigation of favourable time integration methods for the Gross--Pitaevskii equation with rotation term. Employing a reformulation in rotating Lagrangian coordinates, the equation takes the form of a nonlinear Schr{\"o}dinger equation involving a space-time-dependent potential. A natural approach that combines commutator-free quasi-Magnus exponential integrators with operator splitting methods and Fourier spectral space discretisations is proposed. Furthermore, the special structure of the Hamilton operator permits the design of specifically tailored schemes. Numerical experiments confirm the good performance of the resulting exponential integrators., Comment: 24 pages, 13 figures

Ultracold neutrons (UCNs) are formidable probes in precision tests of gravity. With their negligible electric charge, dielectric moment, and polarizability they naturally evade some of the problems plaguing gravity experiments with atomic or macroscopic test bodies. Taking advantage of this fact, the qBounce collaboration has developed a technique - gravity resonance spectroscopy (GRS) - to study bound quantum states of UCN in the gravity field of the Earth. This technique is used as a high-precision tool to search for hypothetical Non-Newtonian gravity on the micrometer scale. In the present article, we describe the recently commissioned Ramsey-type GRS setup, give an unambiguous proof of principle, and discuss possible measurements that will be performed., Comment: accepted by J. Phys.: Conf. Ser. (2019), Proceedings of PPNS 2018

We propose new local error estimators for splitting and composition methods. They are based on the construction of lower order schemes obtained at each step as a linear combination of the intermediate stages of the integrator, so that the additional computational cost required for their evaluation is almost insignificant. These estimators can be subsequently used to adapt the step size along the integration. Numerical examples show the efficiency of the procedure., Comment: 23 pages, 4 figures

The standard model of cosmology provides a robust description of the evolution of the universe. Nevertheless, the small magnitude of the vacuum energy is troubling from a theoretical point of view. An appealing resolution to this problem is to introduce additional scalar fields. However, these have so far escaped experimental detection, suggesting some kind of screening mechanism may be at play. Although extensive exclusion regions in parameter space have been established for one screening candidate - chameleon fields - another natural screening mechanism based on spontaneous symmetry breaking has also been proposed, in the form of symmetrons 11. Such fields would change the energy of quantum states of ultra-cold neutrons in the gravitational potential of the earth. Here we demonstrate a spectroscopic approach based on the Rabi resonance method that probes these quantum states with a resolution of E=2 x 10^(-15) eV. This allows us to exclude the symmetron as the origin of Dark Energy for a large volume of the three-dimensional parameter space., Comment: 13 pages, 4 figures

This work is concerned with the study of fundamental models from nonlinear acoustics. In Part~I, a hierarchy of nonlinear damped wave equations arising in the description of sound propagation in thermoviscous fluids is deduced. In particular, a rigorous justification of two classical models, the Kuznetsov and Westervelt equations, retained as limiting systems for consistent initial data, is given. Numerical comparisons that confirm and complement the theoretical results are provided in Part~II.

The (asymptotic) behaviour of the second moment of solutions to stochastic differential equations is treated in mean-square stability analysis. This property is discussed for approximations of infinite-dimensional stochastic differential equations and necessary and sufficient conditions ensuring mean-square stability are given. They are applied to typical discretization schemes such as combinations of spectral Galerkin, finite element, Euler-Maruyama, Milstein, Crank-Nicolson, and forward and backward Euler methods. Furthermore, results on the relation to stability properties of corresponding analytical solutions are provided. Simulations of the stochastic heat equation illustrate the theory., Comment: 22 pages, 4 figures; deleted a section; shortened the presentation of results; corrected typos

The error behavior of exponential operator splitting methods for nonlinear Schr{\"o}dinger equations in the semiclassical regime is studied. For the Lie and Strang splitting methods, the exact form of the local error is determined and the dependence on the semiclassical parameter is identified. This is enabled within a defect-based framework which also suggests asymptotically correct a~posteriori local error estimators as the basis for adaptive time stepsize selection. Numerical examples substantiate and complement the theoretical investigations.