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After a successful kick-off meeting in 2021. two workshops in 2022 and 2023 on the future Global Cosmic-Ray Observatory (GCOS) focused mainly on a straw man design of the detector and science possibilities for astro- and particle physics. About 100 participants gathered for in-person and hybrid panel discussions. In this report, we summarize these discussions, present a preliminary straw-man design for GCOS and collect short write-ups of the flash talks given during the focus sessions., Comment: 48 pages, 27 figures

Optical clock networks connected by phase-coherent links offer significant potential for advancing fundamental research and diverse scientific applications. Free-space optical frequency transfer extends fiber-based connectivity to remote areas and holds the potential for global coverage via satellite links. Here we present a compact and robust portable, rack-integrated two-way free-space link characterization system. Equipped with plug-and-play capabilities, the system enables straightforward interfacing with various optical systems and facilitates quick deployment for field experiments. In this work, we achieve a fractional frequency instability of $2.0 \times 10^{-19}$ for an averaging time of 10 s over a 3.4 km horizontal fully folded intra-city free-space link. Moreover, the system maintains an uptime of $94\%$ over 15 hours, illustrating its reliability and effectiveness for high-precision optical frequency comparisons over free-space.

The properties of interfaces are key to understand the physics of matter. However, the study of quantum interface dynamics has remained an outstanding challenge. Here, we use large-scale Tree Tensor Network simulations to identify the dynamical signature of an interface roughening transition within the ferromagnetic phase of the 2D quantum Ising model. For initial domain wall profiles we find extended prethermal plateaus for smooth interfaces, whereas above the roughening transition the domain wall decays quickly. Our results can be readily explored experimentally in Rydberg atomic systems., Comment: 13 pages, 8 figures

This paper presents the development and implementation of a versatile ad-hoc metropolitan-range Quantum Key Distribution (QKD) network. The approach presented integrates various types of physical channels and QKD protocols, and a mix of trusted and untrusted nodes. Unlike conventional QKD networks that predominantly depend on either fiber-based or free-space optical (FSO) links, the testbed presented amalgamates FSO and fiber-based links, thereby overcoming some inherent limitations. Various network deployment strategies have been considered, including permanent infrastructure and provisional ad-hoc links to eradicate coverage gaps. Furthermore, the ability to rapidly establish a network using portable FSO terminals and to investigate diverse link topologies is demonstrated. The study also showcases the successful establishment of a quantum-secured link to a cloud server.

As part of the overall goal of driverless road vehicles, remote driving is a major emerging field of research of its own. Current remote driving concepts for public road traffic often establish a fallback strategy of immediate braking to a standstill in the event of a connection loss. This may seem like the most logical option when human control of the vehicle is lost. However, our simulation results from hundreds of scenarios based on naturalistic traffic scenes indicate high collision rates for any immediate substantial deceleration to a standstill in urban settings. We show that such a fallback strategy can result in a SOTIF relevant hazard, making it questionable whether such a design decision can be considered acceptable. Therefore, from a safety perspective, we would call this problem a safety blind spot, as safety analyses in this regard seem to be very rare. In this article, we first present a simulation on a naturalistic dataset that shows a high probability of collision in the described case. Second, we discuss the severity of the resulting potential rear-end collisions and provide an even more severe example by including a large commercial vehicle in the potential collision., Comment: submitted for publication, 13 pages, 5 figures

Proteins are dynamic molecular machines whose biological functions, spanning enzymatic catalysis, signal transduction, and structural adaptation, are intrinsically linked to their motions. Designing proteins with targeted dynamic properties, however, remains a challenge due to the complex, degenerate relationships between sequence, structure, and molecular motion. Here, we introduce VibeGen, a generative AI framework that enables end-to-end de novo protein design conditioned on normal mode vibrations. VibeGen employs an agentic dual-model architecture, comprising a protein designer that generates sequence candidates based on specified vibrational modes and a protein predictor that evaluates their dynamic accuracy. This approach synergizes diversity, accuracy, and novelty during the design process. Via full-atom molecular simulations as direct validation, we demonstrate that the designed proteins accurately reproduce the prescribed normal mode amplitudes across the backbone while adopting various stable, functionally relevant structures. Notably, generated sequences are de novo, exhibiting no significant similarity to natural proteins, thereby expanding the accessible protein space beyond evolutionary constraints. Our work integrates protein dynamics into generative protein design, and establishes a direct, bidirectional link between sequence and vibrational behavior, unlocking new pathways for engineering biomolecules with tailored dynamical and functional properties. This framework holds broad implications for the rational design of flexible enzymes, dynamic scaffolds, and biomaterials, paving the way toward dynamics-informed AI-driven protein engineering.

We show that a sufficient density condition for Gabor systems with Hermite functions over lattices is not sufficient in general. This follows from a result on how zeros of the Zak transform determine the frame property of integer over-sampled Gabor systems., Comment: 10 pages, 5 figures

We present a monolithic $hp$ space-time multigrid method for tensor-product space-time finite element discretizations of the Stokes equations. Geometric and polynomial coarsening of the space-time mesh is performed, and the entire algorithm is expressed through rigorous mathematical mappings. For the discretization, we use inf-sup stable pairs $\mathbb Q_{r+1}/\mathbb P_{r}^{\text{disc}}$ of elements in space and a discontinuous Galerkin (DG$(k)$) discretization in time with piecewise polynomials of order $k$. The key novelty of this work is the application of $hp$ multigrid techniques in space and time, facilitated and accelerated by the matrix-free capabilities of the deal.II library. While multigrid methods are well-established for stationary problems, their application in space-time formulations encounter unique challenges, particularly in constructing suitable smoothers. To overcome these challenges, we employ a space-time cell-wise Vanka smoother. Extensive tests on high-performance computing platforms demonstrate the efficiency of our $hp$ multigrid approach on problem sizes exceeding a trillion degrees of freedom (dofs), sustaining throughputs of hundreds of millions of dofs per second., Comment: 23 pages, 5 figures, 5 tables, submitted to SISC

We collect and summarize results on the unitary equivalence of Gabor systems by pairs of unitary operators and global isometries. The methods are then used to study Gabor systems with Hermite functions. We provide new proofs of some known results and an outlook on double over-sampling., Comment: In v1 we wrote "operator-isometry pairs" which was replaced by "operator-isomorphism pairs". Not all of the used transformations of the plane preserve distance (they are area-preserving, though) 4 pages (two columns), 3 figures

In this note we discuss additional properties of mixed Poisson distributions. We discuss the convergence of mixed Poisson distributions to its mixing distribution for the scaling parameter tending to infinity. Moreover, we obtain a central limit theorem after centering by its mixing random variable, together with moment convergence., Comment: 7 pages

We introduce a density model for random quotients of a free product of finitely generated groups. We prove that a random quotient in this model has the following properties with overwhelming probability: if the density is below $1/2$, the free factors embed into the random quotient and the random quotient is hyperbolic relative to the free factors. Further, there is a phase transition at $1/2$, with the random quotient being a finite group above this density. If the density is below $1/6$, the random quotient is cubulated relative to the free factors. Moreover, if the free factors are cubulated, then so is the random quotient., Comment: 43 pages, 9 figures

Accurate Direction of Arrival (DoA) estimation is critical for applications in robotics and communication, but high costs and complexity of coherent multi-channel receivers hinder accessibility. This work proposes a cost-effective DoA estimation system for continuous wave (CW) signals in the 2.4 GHz ISM band. A two-channel software-defined radio (SDR) with time-division multiplexing (TDM) enables pseudo-coherent sampling of an eight-element uniform circular array (UCA) with low hardware complexity. A central reference antenna mitigates phase jitter and sampling errors. The system applies an enhanced MUSIC algorithm with spatial smoothing to handle light multipath interference in indoor and outdoor environments. Experiments in an anechoic chamber validate accuracy under ideal conditions, while real-world tests confirm robust performance in multipath-prone scenarios. With 5 Hz DoA updates and post-processing to enhance tracking, the system provides an accessible and reliable solution for DoA estimation in real-world environments., Comment: 4 pages, 8 figures, IEEE IMS 2025

Cluster scaling relations are key ingredients in cluster abundance-based cosmological studies. In optical cluster cosmology, weak gravitational lensing has proven to be a powerful tool to constrain the cluster mass-richness relation. This work is conducted as part of the Dark Energy Science Collaboration (DESC), which aims to analyze the Legacy Survey of Space and Time (LSST) of Vera C. Rubin Observatory, starting in 2026. Weak lensing-inferred cluster properties, such as mass, suffer from several sources of bias. In this paper, we aim to test the impact of modeling choices and observational systematics in cluster lensing on the inference of the mass-richness relation. We constrain the mass-richness relation of 3,600 clusters detected by the redMaPPer algorithm in the cosmoDC2 extra-galactic mock catalog (covering $440$ deg$^2$) of the LSST DESC DC2 simulation, using number count measurements and stacked weak lensing profiles in several intervals of richness ($20 \leq \lambda \leq 200$) and redshift ($0.2 \leq z \leq 1$). By modeling the mean of the scaling relation as $\langle \ln \lambda|M_{\rm 200c}, z\rangle = \ln\lambda_0 + \mu_z\log[(1+z)/(1+0.5)] + \mu_m[\log_{10}(M_{\rm 200c}) - 14.3]$, our baseline constraints are $\ln\lambda_0 = 3.37\pm 0.03$, $\mu_z = 0.08\pm 0.07$ and $\mu_m = 2.18 \pm 0.07$. We have found that, for a LSST-like source galaxy density, our constraints are robust to a change in concentration-mass relation and dark matter density profile modeling choices, when source redshifts and shapes are perfectly known. We have found that photometric redshift uncertainties can introduce bias at the $1\sigma$ level, which can be mitigated by an overall correcting factor, fitted jointly with scaling parameters. We find that including positive shear-richness covariance in the fit shifts the results by up to 0.5$\sigma$., Comment: 26 pages, 15 figures, 3 tables, submitted to A&A (abstract shortened for arXiv)

With the transition to fully autonomous vehicles, non-driving related tasks (NDRTs) become increasingly important, allowing passengers to use their driving time more efficiently. In-car Augmented Reality (AR) gives the possibility to engage in NDRTs while also allowing passengers to engage with their surroundings, for example, by displaying world-fixed points of interest (POIs). This can lead to new discoveries, provide information about the environment, and improve locational awareness. To explore the optimal visualization of POIs using in-car AR, we conducted a field study (N = 38) examining six parameters: positioning, scaling, rotation, render distance, information density, and appearance. We also asked for intention of use, preferred seat positions and preferred automation level for the AR function in a post-study questionnaire. Our findings reveal user preferences and general acceptance of the AR functionality. Based on these results, we derived UX-guidelines for the visual appearance and behavior of location-based POIs in in-car AR., Comment: 16 pages, 8 figures, to be published in CHI '25, April 26-May 1, 2025, Yokohama, Japan

As passengers spend more time in vehicles, the demand for non-driving related tasks (NDRTs) increases. In-car Augmented Reality (AR) has the potential to enhance passenger experiences by enabling interaction with the environment through NDRTs using world-fixed Points of Interest (POIs). However, the effectiveness of existing interaction techniques and visualization methods for in-car AR remains unclear. Based on a survey (N=110) and a pre-study (N=10), we developed an interactive in-car AR system using a video see-through head-mounted display to engage with POIs via eye-gaze and pinch. Users could explore passed and upcoming POIs using three visualization techniques: List, Timeline, and Minimap. We evaluated the system's feasibility in a field study (N=21). Our findings indicate general acceptance of the system, with the List visualization being the preferred method for exploring POIs. Additionally, the study highlights limitations of current AR hardware, particularly the impact of vehicle movement on 3D interaction., Comment: 15 pages, 10 figures, to be published in CHI '25, April 26-May 1, 2025, Yokohama, Japan

Describing chemical reactions in solution on a molecular level is a challenging task due to the high mobility of weakly interacting solvent molecules which requires configurational sampling. For instance, polar and protic solvents can interact strongly with solutes and may interfere in reactions. However, to define and identify representative arrangements of solvent molecules modulating a transition state is a non-trivial task. Here, we propose to monitor their active participation in the decaying normal mode at a transition state, which defines active solvent molecules. Moreover, it is desirable to prepare a low-dimensional microsolvation model in a well-defined, fully automated, high-throughput, and easy-to-deploy fashion, which we propose to derive in a stepwise protocol. First, transition state structures are optimized in a sufficiently solvated quantum-classical hybrid model, which are then subjected to a re-definition of a then reduced quantum region. From the reduced model, minimally microsolvated structures are extracted that contain only active solvent molecules. Modeling the remaining solvation effects is deferred to a continuum model. To establish an easy-to-use free-energy model, we combine the standard thermochemical gas-phase model with a correction for the cavity entropy in solution. We assess our microsolvation and free-energy models for methanediol formation from formaldehyde, for the hydration of carbon dioxide (which we consider in a solvent mixture to demonstrate the versatility of our approach), and, finally, for the chlorination of phenol with hypochlorous acid., Comment: 38 pages, 17 figures

Since 1978, Clarence Barlow developed the ``Indispensability Function''. It operates on a metric tree that is bound to the same prime number of branches for all subtrees of each particular level. It assigns to all leaf postions of this tree a numeric value which indicates how important the acoustic presence of an event at this position is for the meter to be recognized as such. Bernd H\"arpfer extended this concept in 2015 to deal with meters which have arbitrary groupings into two or three at any position of the tree hierarchy. This is called ``Extended Indispensability Algorithm''. This article gives a specification of the Extended Algorithm in a slightly extended version of the Z specification language, and a possible generalization to arbitrary metric trees.

We compute the local intersection cohomology of the irreducible components of varieties of complexes, by using Lusztig's geometric approach to quantum groups and explicit constructions of elements of Lusztig's canonical bases., Comment: 9 pages

We systematically analyse the nuclear moments of inertia determined within the Skyrme and Gogny density-functional theories. The time-odd mean fields generated by collective rotation are self-consistently determined by an exact iterative solution of the adiabatic time-dependent Hartree-Fock-Bogoliubov (ATDHFB) equations. Although details of the results depend on the functional used, the calculated moments of inertia are in good overall agreement with the experimental data, with no adjustable parameters. To show the essential importance of the time-odd mean fields, the ATDHFB moments of inertia are compared with those obtained from the Inglis-Belyaev formula. For Skyrme density functionals, we find strong correlations between the effective mass and the impact of the time-odd mean fields on inertia's rotational and vibrational moments., Comment: 6 pages, 4 figures

Carbon-rich Asymptotic Giant Branch (AGB) stars are among the most important contributors of enriched materials to the interstellar medium due to their strong stellar winds. To fully characterize mass loss on the AGB, it is necessary to determine the distributions of dust and gas around the stars, where the dust begins to condense from the gas, and how this extended atmospheric structure evolves over the pulsational period of the star. We present an analysis of L-band (2.8-4.2 $\mu$m) interferometric observations of the carbon-rich AGB star V Oph made with the MATISSE instrument at the VLTI at the maximum and minimum of the star's visual light curve. Using the radiative transfer software RADMC-3D, we model the circumstellar dust shell, and find stellar radii of 395 and 495 $R_{\odot}$ at the two phases, and dust radii of 790 and 742.5 $R_{\odot}$ at the two epochs, respectively. By adding C$_2$H$_2$ and HCN gas to the RADMC-3D models, we are able to fit the visibility spectra well, with some deviations at the 3.11 $\mu$m feature. Reasons for this deviation and interpretation of the best fitting models are discussed in the text, and we discuss motivations for follow-up imaging observations of V Oph., Comment: 20 pages, 16 Figures

Designing, assuring and releasing safe automated vehicles is a highly interdisciplinary process. As complex systems, automated driving systems will inevitably be subject to emergent properties, i. e., the properties of the overall system will be more than just a sum of the properties of its integrated elements. Safety is one example of such emergent properties. In this regard, it must be ensured that effects of emergence do not render an overall system that is composed of safety-approved sub systems unsafe. The key challenges in this regard are twofold: Regarding the interdisciplinary character of the development and assurance processes, all relevant stakeholders must speak a common language and have a common understanding of the key concepts that influence system safety. Additionally, the individual properties of system elements should remain traceable to the system level. Model-Based Systems Engineering (MBSE) provides an interdisciplinary mindset, as well as methods and processes to manage emergent system properties over the entire system lifecycle. By this, MBSE provides tools that can assist the assurance process for automated vehicles. However, concepts from the domain of MBSE have a reputation for not being directly accessible for domain experts who are no experts in the field of Systems Engineering. This paper highlights challenges when applying MBSE methods to the design and development of automated driving systems. It will present an approach to create and apply domain-specific SysML profiles, which can be a first step for enhancing communication between different stake-holders in the development and safety assurance processes., Comment: Accepted at 16. Uni-DAS e.V. Workshop Fahrerassistenz und Automatisiertes Fahren

"Safety" and "Risk" are key concepts for the design and development of automated vehicles. For the market introduction or large-scale field tests, both concepts are not only relevant for engineers developing the vehicles, but for all stakeholders (e.g., regulators, lawyers, or the general public) who have stakes in the technology. In the communication between stakeholder groups, common notions of these abstract concepts are key for efficient communication and setting mutual expectations. In the European market, automated vehicles require Europe-wide type approval or at least operating permits in the individual states. For this, a central means of communication between regulators and engineers are regulatory documents. Flawed terminology regarding the safety expectations for automated vehicles can unnecessarily complicate relations between regulators and manufacturers, and thus hinder the introduction of the technology. In this paper, we review relevant documents at the UN- and EU-level, for the UK, and Germany regarding their (implied) notions of safety and risk. We contrast the regulatory notions with established and more recently developing notions of safety and risk in the field of automated driving. Based on the analysis, we provide recommendations on how explicit definitions of safety and risk in regulatory documents can support rather than hinder the market introduction of automated vehicles., Comment: Submitted to IEEE Intelligent Vehicles Symposium (IEEE IV) 2025

In July 2024, Sunrise completed its third successful science flight. The Sunrise III observatory had been upgraded significantly after the two previous successful flights in 2009 and 2013. Three completely new instruments focus on the small-scale physical processes and their complex interaction from the deepest observable layers in the photosphere up to chromospheric heights. Previously poorly explored spectral regions and lines are exploited to paint a three-dimensional picture of the solar atmosphere with unprecedented completeness and level of detail. The full polarimetric information is captured by all three instruments to reveal the interaction between the magnetic fields and the hydrodynamic processes. Two slit- based spectropolarimeters, the Sunrise UV Spectropolarimeter and Imager (SUSI) and the Sunrise Chromospheric Infrared spectro-Polarimeter (SCIP), focus on the near-ultraviolet and the near-infrared regions respectively, and the imaging spectropolarimeter Tunable Magnetograph (TuMag) simultaneously obtains maps of the full field-of-view of $46 \times 46$ Mm$^2$ in the photosphere and the chromosphere in the visible. The instruments are operated in an orchestrated mode, benefiting from a new Image Stabilization and Light Distribution unit (ISLiD), with the Correlating Wavefront Sensor (CWS) providing the autofocus control and an image stability with a root-mean-square value smaller than 0.005''. A new gondola was constructed to significantly improve the telescope pointing stability, required to achieve uninterrupted observations over many hours. Sunrise III was launched successfully on July 10, 2024, from the Esrange Space Center near Kiruna (Sweden). It reached the landing site between the Mackenzie River and the Great Bear Lake in Canada after a flight duration of 6.5 days. In this paper, we give an overview of the Sunrise III observatory and its instruments., Comment: 67 pages, 25 figures; to be published in Solar Physics Topical Collection "The Sunrise III Solar Observatory" (https://link.springer.com/collections/jegdciedig)

Metallic microcalorimeters (MMCs) are cryogenic single-particle detectors that rely on a calorimetric detection principle. Due to their excellent energy resolution, close-to-ideal linear detector response, fast signal rise time and the potential for \SI{100}{\%} quantum efficiency, MMCs outperform conventional detectors by several orders of magnitude in resolution. These attributes make them particularly interesting for a broad spectrum of applications, including a next-generation neutrino mass experiment based on the measurement of the tritium beta-decay spectrum, with an objective of achieving a sensitivity surpassing that of the pioneering KATRIN experiment. However, although MMCs have been used in measurements of photons and heavy ions with great success, no information is currently available on the interaction between MMCs and external light charged particles such as electrons. This work aims to provide such missing information and to demonstrate that MMC-based detectors are suitable for high-resolution spectroscopy of external electron sources. Particularly, we present the first-ever measurements of external electrons using a metallic microcalorimeter, comprehensively discuss the characteristics of the signal shape and the calibration function and give a direct comparison between well-defined conversion electron and X-ray photon signals from the same $^{83}$Rb/$^{83m}$Kr source., Comment: 18 pages, 8 figures. Will be submitted to Nuclear Instruments and Methods in Physics Research - section A (NIM-A)

Diffusion models have emerged as a powerful class of generative models, capable of producing high-quality images by mapping noise to a data distribution. However, recent findings suggest that image likelihood does not align with perceptual quality: high-likelihood samples tend to be smooth, while lower-likelihood ones are more detailed. Controlling sample density is thus crucial for balancing realism and detail. In this paper, we analyze an existing technique, Prior Guidance, which scales the latent code to influence image detail. We introduce score alignment, a condition that explains why this method works and show that it can be tractably checked for any continuous normalizing flow model. We then propose Density Guidance, a principled modification of the generative ODE that enables exact log-density control during sampling. Finally, we extend Density Guidance to stochastic sampling, ensuring precise log-density control while allowing controlled variation in structure or fine details. Our experiments demonstrate that these techniques provide fine-grained control over image detail without compromising sample quality., Comment: 27 pages, 15 figures

The companion GL229B was recently resolved by Xuan et al. (2024) as a tight binary of two brown dwarfs (Ba and Bb) through VLTI-GRAVITY interferometry and VLT-CRIRES+ RV measurements. Here, we present Bayesian models of the interferometric and RV data in additional detail, along with an updated outer orbit of the brown dwarf pair about the primary. To create a model of the inner orbit with robust uncertainties, we apply kernel phases to the GRAVITY data to address baseline redundancy in the raw closure phases. Using parallel tempering, we constrain the binary's orbit using only VLTI-GRAVITY data, despite each epoch having low visibility-plane coverage and/or SNR. We demonstrate very agreement the VLTI-GRAVITY and CRIRES+ datasets and find that the inner binary has a period of 12.1346$\pm$0.0011 days, eccentricity of 0.2317$\pm$0.0025, and total mass of 71.0$\pm$0.4 Mjup, with Ba and Bb having masses of 37.7$\pm$1.1Mjup and 33.4$\pm$1.0Mjup respectively. With new Keck/NIRC2 astrometry, we update the outer orbit GL229B around the primary. We find a semi-major axis of 42.9+3.0-2.4AU, eccentricity of 0.736$\pm$0.014, and a total mass for B of 71.7$\pm$0.6Mjup, consistent with that derived from the inner orbit. We find a mutual inclination of 31$\pm$2.5deg, below the threshold for Kozai-Lidov oscillations. The agreement on the mass of Ba+Bb between the inner and outer orbits is an important test of our ability to model RV, astrometry, and Hipparcos-Gaia proper motion anomaly. Our methodological advances in handling interferometric data with low SNR and sparse UV-coverage will benefit future observations of rapidly-orbiting companions with VLTI-GRAVITY., Comment: Resubmitted to AJ

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://aortaseg24.grand-challenge.org.

Ensemble learning is a widespread technique to improve the prediction performance of neural networks. However, it comes at the price of increased memory and inference time. In this work we propose a novel model fusion technique called \emph{Neuron Transplantation (NT)} in which we fuse an ensemble of models by transplanting important neurons from all ensemble members into the vacant space obtained by pruning insignificant neurons. An initial loss in performance post-transplantation can be quickly recovered via fine-tuning, consistently outperforming individual ensemble members of the same model capacity and architecture. Furthermore, NT enables all the ensemble members to be jointly pruned and jointly trained in a combined model. Comparing it to alignment-based averaging (like Optimal-Transport-fusion), it requires less fine-tuning than the corresponding OT-fused model, the fusion itself is faster and requires less memory, while the resulting model performance is comparable or better. The code is available under the following link: https://github.com/masterbaer/neuron-transplantation., Comment: 18 pages, 7 figures, conference: ECML-PKDD 2024

Recent technical and conceptual advancements in the asymptotic safety approach to quantum gravity have enabled studies of the UV completion of Lorentzian Einstein gravity, emphasizing the role of the state dependence. We present here the first complete investigation of the flow equations of the Einstein-Hilbert action within a cosmological spacetime, namely de Sitter spacetime. Using the newly derived graviton propagator for general gauges and masses in de Sitter spacetime, we analyze the dependence on the gauge and on finite renormalization parameters. Our results provide evidence of a UV fixed point for the most commonly used gauges., Comment: Comments welcome

We develop early stopping rules for growing regression tree estimators. The fully data-driven stopping rule is based on monitoring the global residual norm. The best-first search and the breadth-first search algorithms together with linear interpolation give rise to generalized projection or regularization flows. A general theory of early stopping is established. Oracle inequalities for the early-stopped regression tree are derived without any smoothness assumption on the regression function, assuming the original CART splitting rule, yet with a much broader scope. The remainder terms are of smaller order than the best achievable rates for Lipschitz functions in dimension $d\ge 2$. In real and synthetic data the early stopping regression tree estimators attain the statistical performance of cost-complexity pruning while significantly reducing computational costs.

This paper introduces a computationally efficient algorithm in system theory for solving inverse problems governed by linear partial differential equations (PDEs). We model solutions of linear PDEs using Gaussian processes with priors defined based on advanced commutative algebra and algebraic analysis. The implementation of these priors is algorithmic and achieved using the Macaulay2 computer algebra software. An example application includes identifying the wave speed from noisy data for classical wave equations, which are widely used in physics. The method achieves high accuracy while enhancing computational efficiency.

The gamma-ray emission from Flat Spectrum Radio Quasars (FSRQs), a sub-class of blazars, is believed to be generated through interactions of high-energy leptons and/or hadrons in the jet with the ambient photon fields, including those from the accretion disk, the broad line region (BLR), and the dusty torus. However, these same photon fields can also attenuate gamma-rays through internal photon-photon (gamma-gamma) absorption, imprinting characteristic spectral features. Investigating the internal absorption is crucial for unraveling the complex structure of FSRQs and constraining the poorly known location of the gamma-ray emission region. In this study, we select a sample of gamma-ray detected FSRQs with high redshift (z >= 3), to search for absorption features appearing at lower photon energies due to a substantial redshift. We extract the Fermi-LAT gamma-ray spectra of these sources and perform physical modeling using a detailed gamma-gamma opacity model, assuming that the BLR photon field dominates the absorption and focusing on the energy range ~25 GeV/(1+z), where the absorption feature due to Ly{\alpha} photons is expected. Our analysis reveals a hint of internal absorption for one source (the lowest redshift object in our sample, z~3) and provides constraints on the location of its gamma-ray emitting region along the jet. For the remaining, higher-redshift sources, the limited photon statistics prevent a reliable detection of internal opacity features., Comment: 10 pages, 3 figures. Accepted for publication in ApJ

The intrinsic uncertainty of fluid properties, including the equation of state, viscosity, and thermal conductivity, on boundary layer stability has scarcely been addressed. When a fluid is operating in the vicinity of the Widom line (defined as the maximum of isobaric specific heat) in supercritical state, its properties exhibit highly non-ideal behavior, which is an ongoing research field leading to refined and more accurate fluid property databases. Upon crossing the Widom line, new mechanisms of flow instability emerge, feasibly leading to changes in dominating modes that yield turbulence. The present work investigates the sensitivity of three-dimensional boundary-layer modal instability to these intrinsic uncertainties in fluid properties. The uncertainty, regardless of its source and the fluid regimes, gives rise to distortions of all profiles that constitute the inputs of the stability operator. The effect of these distortions on flow stability is measured by sensitivity coefficients, which are formulated with the adjoint operator and validated against linear modal stability analysis. The results are presented for carbon dioxide at a representative supercritical pressure of about 80 bar. The sensitivity to different inputs of the stability operator across various thermodynamic regimes show an immense range of sensitivity amplitude. A balancing relationship between the density gradient and its perturbation leads to a quadratic effect across the Widom line, provoking significant sensitivity to distortions of the second derivative of the pressure with respect to the density, $\partial^2 p/\partial \rho^2$. From an application-oriented point of view, one important question is whether the correct baseflow profiles can be meaningfully analyzed by the simplified ideal-fluid model...

We introduce a modification of Random Forests to estimate functions when unobserved confounding variables are present. The technique is tailored for high-dimensional settings with many observed covariates. We use spectral deconfounding techniques to minimize a deconfounded version of the least squares objective, resulting in the Spectrally Deconfounded Random Forests (SDForests). We show how the omitted variable bias gets small given some assumptions. We compare the performance of SDForests to classical Random Forests in a simulation study and a semi-synthetic setting using single-cell gene expression data. Empirical results suggest that SDForests outperform classical Random Forests in estimating the direct regression function, even if the theoretical assumptions, requiring linear and dense confounding, are not perfectly met, and that SDForests have comparable performance in the non-confounded case.

Two-dimensional electronic spectroscopy provides information on coupling and energy transfer between excited states on ultrafast timescales. Only recently, incoherent fluorescence detection has made it possible to combine this method with single-molecule optical spectroscopy to reach the ultimate limit of sensitivity. The main obstacle has been the low number of photons detected due to limited photostability. Here we discuss the key experimental choices that allowed us to overcome these obstacles: broadband acousto-optic modulation, accurate phase-locked loops, photon-counting lock-in detection, delay stage linearization, and detector dead-time compensation. We demonstrate how the acquired photon stream data can be used to post-select detection events according to specific criteria., Comment: 9 pages, 5 figures

This paper investigates the approximation of functions with finite smoothness defined on domains with a Cartesian product structure. The recently proposed tensor product multilevel method (TPML) combines Smolyak's sparse grid method with a kernel-based residual correction technique. The contributions of this paper are twofold. First, we present two improvements on the TPML that reduce the computational cost of point evaluations compared to a naive implementation. Second, we provide numerical examples that demonstrate the effectiveness and innovation of the TPML.

In this article, we study strictly elliptic, second-order differential operators on a bounded Lipschitz domain in $\mathbb{R}^d$, subject to certain non-local Wentzell-Robin boundary conditions. We prove that such operators generate strongly continuous semigroups on $L^2$-spaces and on spaces of continuous functions. We also provide a characterisation of positivity and (sub-)Markovianity of these semigroups. Moreover, based on spectral analysis of these operators, we discuss further properties of the semigroup such as asymptotic behaviour and, in the case of a non-positive semigroup, the weaker notion of eventual positivity of the semigroup., Comment: 33 pages, 2 figures

This study explores the implementation of large Quantum Restricted Boltzmann Machines (QRBMs), a key advancement in Quantum Machine Learning (QML), as generative models on D-Wave's Pegasus quantum hardware to address dataset imbalance in Intrusion Detection Systems (IDS). By leveraging Pegasus's enhanced connectivity and computational capabilities, a QRBM with 120 visible and 120 hidden units was successfully embedded, surpassing the limitations of default embedding tools. The QRBM synthesized over 1.6 million attack samples, achieving a balanced dataset of over 4.2 million records. Comparative evaluations with traditional balancing methods, such as SMOTE and RandomOversampler, revealed that QRBMs produced higher-quality synthetic samples, significantly improving detection rates, precision, recall, and F1 score across diverse classifiers. The study underscores the scalability and efficiency of QRBMs, completing balancing tasks in milliseconds. These findings highlight the transformative potential of QML and QRBMs as next-generation tools in data preprocessing, offering robust solutions for complex computational challenges in modern information systems., Comment: accapted at IEEE International Conference on Next Generation Information System Engineering

Optomechanical systems subjected to environmental noise give rise to rich physical phenomena. We investigate entanglement between a mechanical oscillator and the reflected coherent optical field in a general, not necessarily Markovian environment. For the input optical field, we consider stationary Gaussian states and frequency dependent squeezing. We demonstrate that for a coherent laser drive, either unsqueezed or squeezed in a frequency-independent manner, optomechanical entanglement is destroyed after a threshold that depends only on the environmental noises -- independent of the coherent coupling between the oscillator and the optical field, or the squeeze factor. In this way, we have found a universal entangling-disentangling transition. We also show that for a configuration in which the oscillator and the reflected field are separable, entanglement cannot be generated by incorporating frequency-dependent squeezing in the optical field., Comment: 7 pages, 2 figures

We derive two formulas for motives of nullcones of quiver representations, one being explicit, the other being of wall-crossing type., Comment: 20 pages

We introduce AutoGraph, a novel autoregressive framework for generating large attributed graphs using decoder-only transformers. At the core of our approach is a reversible "flattening" process that transforms graphs into random sequences. By sampling and learning from these sequences, AutoGraph enables transformers to model and generate complex graph structures in a manner akin to natural language. In contrast to diffusion models that rely on computationally intensive node features, our approach operates exclusively on these sequences. The sampling complexity and sequence length scale linearly with the number of edges, making AutoGraph highly scalable for generating large sparse graphs. Empirically, AutoGraph achieves state-of-the-art performance across diverse synthetic and molecular graph generation benchmarks, while delivering a 100-fold generation and a 3-fold training speedup compared to leading diffusion models. Additionally, it demonstrates promising transfer capabilities and supports substructure-conditioned generation without additional fine-tuning. By extending language modeling techniques to graph generation, this work paves the way for developing graph foundation models.

Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. In this work, we introduce the $\textit{end-to-end}$ optimization framework AIDO that leverages a diffusion model as a surrogate for the full simulation and reconstruction chain, enabling gradient-based design exploration in both continuous and discrete parameter spaces. Although this framework is applicable to a broad range of detectors, we illustrate its power using the specific example of a sampling calorimeter, focusing on charged pions and photons as representative incident particles. Our results demonstrate that the diffusion model effectively captures critical performance metrics for calorimeter design, guiding the automatic search for layer arrangement and material composition that aligns with known calorimeter principles. The success of this proof-of-concept study provides a foundation for future applications of end-to-end optimization to more complex detector systems, offering a promising path toward systematically exploring the vast design space in next-generation experiments., Comment: 14 pages, 9 figures, submitted to MDPI particles

We study an optomechanical system, where a mechanical oscillator interacts with a Gaussian input optical field. In the linearized picture, we analytically prove that if the input light field is the vacuum state, or is frequency-independently squeezed, the stationary entanglement between the oscillator and the output optical field is independent of the coherent coupling between them, which we refer to as the universality of entanglement. Furthermore, we demonstrate that entanglement cannot be generated by performing arbitrary frequency-dependent squeezing on the input optical field. Our results hold in the presence of general, Gaussian environmental noise sources, including non-Markovian noise., Comment: 19 pages, 1 figure

Single-wavelength interferometry achieves high resolution for smooth surfaces but struggles with rough, industrially relevant ones due to limited unambiguous measuring range and speckle effects. Multi-wavelength interferometry addresses these challenges by using synthetic waveleths, enabling a balance between extended measurement range and resolution by combining several synthetic wavelengths. This approach holds immense potential for diverse industrial applications, yet it remains largely untapped due to the lack of suitable light sources. Existing solutions are constrained by limited flexibility in synthetic-wavelength generation and slow switching speeds. We demonstrate a light source for multi-wavelength interferometry based on electro-optic single-sideband modulation. It reliably generates synthetic wavelengths with arbitrary values from centimeters to meters and switching times below 30 ms. This breakthrough paves the way for dynamic, reconfigurable multi-wavelength interferometry capable of adapting to complex surfaces and operating efficiently even outside laboratory settings. These capabilities unlock the full potential of multi-wavelength interferometry, offering unprecedented flexibility and speed for industrial and technological applications., Comment: 16 pages, 6 figures

Graph generative models often face a critical trade-off between learning complex distributions and achieving fast generation speed. We introduce Autoregressive Noisy Filtration Modeling (ANFM), a novel approach that addresses both challenges. ANFM leverages filtration, a concept from topological data analysis, to transform graphs into short sequences of monotonically increasing subgraphs. This formulation extends the sequence families used in previous autoregressive models. To learn from these sequences, we propose a novel autoregressive graph mixer model. Our experiments suggest that exposure bias might represent a substantial hurdle in autoregressive graph generation and we introduce two mitigation strategies to address it: noise augmentation and a reinforcement learning approach. Incorporating these techniques leads to substantial performance gains, making ANFM competitive with state-of-the-art diffusion models across diverse synthetic and real-world datasets. Notably, ANFM produces remarkably short sequences, achieving a 100-fold speedup in generation time compared to diffusion models. This work marks a significant step toward high-throughput graph generation., Comment: 32 pages, 27 tables, 6 figures

This study explores the feasibility of using scintillation detectors for muon calorimetry through experiments conducted at CERN's T10 beamline. Results from organic scintillators with varying thicknesses and readout methods, as well as a lead-glass calorimeter, showed minimal correlation between muon energy and the peak or spread of the detected signal amplitudes. These findings highlight the influence of temperature on SiPMs and indicate a significant amount of contamination in the muon beam., Comment: 12 pages, 11 figures

Traffic assignment analyzes traffic flows in road networks that emerge due to traveler interaction. Traditionally, travelers are assumed to use private cars, so road costs grow with the number of users due to congestion. However, in sustainable transit systems, travelers share vehicles s.t. more users on a road lead to higher sharing potential and reduced cost per user. Thus, we invert the usual avoidant traffic assignment (ATA) and instead consider synergistic traffic assignment (STA) where road costs decrease with use. We find that STA is significantly different from ATA from a game-theoretical point of view. We show that a simple iterative best-response method with simultaneous updates converges to an equilibrium state. This enables efficient computation of equilibria using optimized speedup techniques for shortest-path queries. In contrast, ATA requires slower sequential updates or more complicated iteration schemes that only approximate an equilibrium. Experiments with a realistic scenario for the city of Stuttgart indicate that STA indeed quickly converges to an equilibrium. We envision STA as a part of software-defined transportation systems that dynamically adapt to current travel demand. As a first demonstration, we show that an STA equilibrium can be used to incorporate traveler synergism in a simple bus line planning algorithm to potentially greatly reduce the required vehicle resources., Comment: 10 pages, 7 figures, 2 tables. To be presented at AAMAS'25

We explore the influence maximisation problem in networks with negative ties. Where prior work has focused on unsigned networks, we investigate the need to consider negative ties in networks while trying to maximise spread in a population - particularly under competitive conditions. Given a signed network we optimise the strategies of a focal controller, against competing influence in the network, using two approaches - either the focal controller uses a sign-agnostic approach or they factor in the sign of the edges while optimising their strategy. We compare the difference in vote-shares (or the share of population) obtained by both these methods to determine the need to navigate negative ties in these settings. More specifically, we study the impact of: (a) network topology, (b) resource conditions and (c) competitor strategies on the difference in vote shares obtained across both methodologies. We observe that gains are maximum when resources available to the focal controller are low and the competitor avoids negative edges in their strategy. Conversely, gains are insignificant irrespective of resource conditions when the competitor targets the network indiscriminately. Finally, we study the problem in a game-theoretic setting, where we simultaneously optimise the strategies of both competitors. Interestingly we observe that, strategising with the knowledge of negative ties can occasionally also lead to loss in vote-shares., Comment: 27 pages

Current software stacks for real-world applications of autonomous driving leverage map information to ensure reliable localization, path planning, and motion prediction. An important field of research is the generation of point cloud maps, referring to the topic of simultaneous localization and mapping (SLAM). As most recent developments do not include global position data, the resulting point cloud maps suffer from internal distortion and missing georeferencing, preventing their use for map-based localization approaches. Therefore, we propose FlexCloud for an automatic georeferencing of point cloud maps created from SLAM. Our approach is designed to work modularly with different SLAM methods, utilizing only the generated local point cloud map and its odometry. Using the corresponding GNSS positions enables direct georeferencing without additional control points. By leveraging a 3D rubber-sheet transformation, we can correct distortions within the map caused by long-term drift while maintaining its structure. Our approach enables the creation of consistent, globally referenced point cloud maps from data collected by a mobile mapping system (MMS). The source code of our work is available at https://github.com/TUMFTM/FlexCloud., Comment: Accepted for publication at VEHITS 2025, Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; 2025