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Let $(R,m,k)$ be a Golod ring. We show a recurrent formula for high syzygies of $k$ interms of previous ones. In the case of embedding dimension at most $2$, we provided complete descriptions of all indecomposable summands of all syzygies of $k$.

We compute the corrections to the two-loop amplitudes for $gg \to h$, $gg \to hg$, $qg \to hq$, and $q \bar q \to hg$ due to a modified cubic Higgs self-coupling. The exact dependence on the Higgs and top-quark masses across the entire $2 \to 2$ phase space is determined by numerically solving a system of differential equations for the relevant master integrals. The calculated amplitudes are crucial for evaluating the impact of the considered corrections on exclusive $pp \to hj$ production at the hadronic event level. As an application, we calculate the non-universal, kinematic-dependent cubic Higgs self-coupling corrections to the Higgs-boson transverse momentum distribution in gluon-gluon fusion Higgs production for arbitrary values of the transverse momentum., Comment: 11 pages, 3 figures; v2: revised version of the manuscript as accepted for publication in JHEP

The transition matrix, frequently abbreviated as T-matrix, contains the complete information in a linear approximation of how a spatially localized object scatters an incident field. The T-matrix is used to study the scattering response of an isolated object and describes the optical response of complex photonic materials made from ensembles of individual objects. T-matrices of certain common structures, potentially, have been repeatedly calculated all over the world again and again. This is not necessary and constitutes a major challenge for various reasons. First, the resources spent on their computation represent an unsustainable financial and ecological burden. Second, with the onset of machine learning, data is the gold of our era, and it should be freely available to everybody to address novel scientific challenges. Finally, the possibility of reproducing simulations could tremendously improve if the considered T-matrices could be shared. To address these challenges, we found it important to agree on a common data format for T-matrices and to enable their collection from different sources and distribution. This document aims to develop the specifications for storing T-matrices and associated metadata. The specifications should allow maximum freedom to accommodate as many use cases as possible without introducing any ambiguity in the stored data. The common format will assist in setting up a public database of T-matrices., Comment: Submitted to the Journal of Quantitative Spectroscopy and Radiative Transfer

The long runtime associated with simulating multidisciplinary systems challenges the use of Bayesian optimization for multidisciplinary design optimization (MDO). This is particularly the case if the coupled system is modeled in a partitioned manner and feedback loops, known as strong coupling, are present. This work introduces a method for Bayesian optimization in MDO called "Multidisciplinary Design Optimization using Thompson Sampling", abbreviated as MDO-TS. Instead of replacing the whole system with a surrogate, we substitute each discipline with such a Gaussian process. Since an entire multidisciplinary analysis is no longer required for enrichment, evaluations can potentially be saved. However, the objective and associated uncertainty are no longer analytically estimated. Since most adaptive sampling strategies assume the availability of these estimates, they cannot be applied without modification. Thompson sampling does not require this explicit availability. Instead, Thompson sampling balances exploration and exploitation by selecting actions based on optimizing random samples from the objective. We combine Thompson sampling with an approximate sampling strategy that uses random Fourier features. This approach produces continuous functions that can be evaluated iteratively. We study the application of this infill criterion to both an analytical problem and the shape optimization of a simple fluid-structure interaction example.

This contribution combines a low-rank matrix approximation through Singular Value Decomposition (SVD) with second-order Krylov subspace-based Model Order Reduction (MOR), in order to efficiently propagate input uncertainties through a given vibroacoustic model. The vibroacoustic model consists of a plate coupled to a fluid into which the plate radiates sound due to a turbulent boundary layer excitation. This excitation is subject to uncertainties due to the stochastic nature of the turbulence and the computational cost of simulating the coupled problem with stochastic forcing is very high. The proposed method approximates the output uncertainties in an efficient way, by reducing the evaluation cost of the model in terms of DOFs and samples by using the factors of the SVD low-rank approximation directly as input for the MOR algorithm. Here, the covariance matrix of the vector of unknowns can efficiently be approximated with only a fraction of the original number of evaluations. Therefore, the approach is a promising step to further reducing the computational effort of large-scale vibroacoustic evaluations.

We implement and analyse a sparse / indirect-addressing data structure for the Lattice Boltzmann Method to support efficient compute kernels for fluid dynamics problems with a high number of non-fluid nodes in the domain, such as in porous media flows. The data structure is integrated into a code generation pipeline to enable sparse Lattice Boltzmann Methods with a variety of stencils and collision operators and to generate efficient code for kernels for CPU as well as for AMD and NVIDIA accelerator cards. We optimize these sparse kernels with an in-place streaming pattern to save memory accesses and memory consumption and we implement a communication hiding technique to prove scalability. We present single GPU performance results with up to 99% of maximal bandwidth utilization. We integrate the optimized generated kernels in the high performance framework WALBERLA and achieve a scaling efficiency of at least 82% on up to 1024 NVIDIA A100 GPUs and up to 4096 AMD MI250X GPUs on modern HPC systems. Further, we set up three different applications to test the sparse data structure for realistic demonstrator problems. We show performance results for flow through porous media, free flow over a particle bed, and blood flow in a coronary artery. We achieve a maximal performance speed-up of 2 and a significantly reduced memory consumption by up to 75% with the sparse / indirect-addressing data structure compared to the direct-addressing data structure for these applications., Comment: 16 pages, 19 figures

Scalar leptoquarks (LQ) with masses between 2 TeV and 50 TeV are prime candidates to explain deviations between measurements and Standard-Model predictions in decay observables of $b$-flavored hadrons (``flavor anomalies''). Explanations of low-energy data often involve order-one LQ-quark-lepton Yukawa couplings, especially when collider bounds enforce a large LQ mass. This calls for the calculation of radiative corrections involving these couplings. Studying such corrections to LQ-mediated $b\to c\tau \nu$ and $b\to s\ell^+\ell^-$ amplitudes, we find that they can be absorbed into finite renormalizations of the LQ Yukawa couplings. If one wants to use Yukawa couplings extracted from low-energy data for the prediction of on-shell LQ decay rates, one must convert the low-energy couplings to their high-energy counterparts, which subsume the corrections to the on-shell LQ-quark-lepton vertex. We present compact formulae for these correction factors and find that in scenarios with $S_1$, $R_2$, or $S_3$ LQ the high-energy coupling is always smaller than the low-energy one, which weakens the impact of collider data on the determination of the allowed parameter spaces. For the $R_2$ scenario addressing $b\to c\tau \nu$, in which one of the two involved Yukawa coupling must be significantly larger than 1, we find this coupling reduced by 15\% at high energy. If both $S_1$ and $R_2$ are present, the high-energy coupling can also be larger and the size of the correction is unbounded, because tree contribution and vertex corrections involve different couplings. We further present the conversion formula to the $\overline{\rm MS}$ scheme for the Yukawa couplings of the $S_3$ scenario., Comment: 22 pages, 5 Figures

Pd$_3$Bi$_2$Se$_2$ is a rare realization of a superconducting metal with a non-zero $Z_2$ topological invariant. We report the growth of high-quality single crystals of layered Pd$_3$Bi$_2$Se$_2$ with a superconducting transition at $T_c$ ~ 0.80 K and upper critical fields of ~10 mT and ~5 mT for the in-plane and out-of-plane directions, respectively. Our density functional theory (DFT) calculations reveal three pairs of doubly degenerate bands crossing the Fermi level, all displaying clear three-dimensional dispersion consistent with the overall low electronic anisotropy (<2). The multiband electronic nature of Pd$_3$Bi$_2$Se$_2$ is evident in magneto-transport measurements, yielding a sign-changing Hall resistivity at low temperatures. The magnetoresistance is non-saturating and follows Kohler's scaling rule. We interpret the magneto-transport data in terms of open orbits that are revealed in the DFT-calculated Fermi surface. de Haas-van Alphen (dHvA) oscillation measurements using torque magnetometry on single crystals yield four frequencies for out-of-plane fields: $F_\alpha = (150 \pm 26)$T, $F_\beta = (293 \pm 10)$T, $F_\gamma = (375 \pm 20)$T, and $F_\eta = (1017 \pm 12)$T, with the low frequency dominating the spectrum. Through the measurement of angular dependent dHvA oscillations and DFT calculations, we identify the $F_\alpha$ frequency with an approximately ellipsoidal electron pocket centered on the $L_2$ point of the Brillouin zone. Lifshitz-Kosevich analysis of the dHvA oscillations reveals a small cyclotron effective mass: $m^* = (0.11 \pm 0.02) m_0$ and a nontrivial Berry phase for the dominant orbit. The presence of nontrivial topology in a bulk superconductor positions Pd$_3$Bi$_2$Se$_2$ as a potential candidate for exploring topological superconductivity., Comment: 30 pages, 5 figures

The existence of superconductivity (SC) in graphene appears to be established in both twisted and non-twisted multilayers. However, whether their building block, single-layer graphene (SLG), can also host SC remains an open question. Earlier theoretical works predicted that SLG could become a chiral d-wave superconductor driven by electronic interactions when doped to its van Hove singularity, but questions such as whether the d-wave SC survives the strong band renormalizations seen in experiments, its robustness against the source of doping, or if it will occur at any reasonable critical temperature (Tc) have remained difficult to answer, in part due to uncertainties in model parameters. In this study, we adopt a random-phase approximation framework based on a Kohn-Luttinger-like mechanism to investigate SC in heavily-doped SLG. We predict that robust d+id topological SC could arise in SLG doped by Tb, with a Tc up to 600 mK. We also investigate the possibility of realizing d-wave SC by employing other dopants, such as Li or Cs. The structural models have been derived from angle-resolved photoemission spectroscopy measurements on Tb-doped graphene and first-principles calculations for Cs and Li doping. We find that dopants that change the lattice symmetry of SLG are detrimental to the d-wave state. The stability of the d-wave SC predicted here in Tb-doped SLG could provide a valuable insight for guiding future experimental efforts aimed at exploring topological superconductivity in monolayer graphene., Comment: 14 pages, 6 figures. Comments are very welcome

We study the interactions and the conditions for the equilibrium of forces between generic non-rotating black holes of the Einstein-Maxwell-Dilaton (EMD) theory. We study known (and some new) solutions of the time-symmetric initial-data problem escribing an arbitrary number of those black holes, some of them with primary scalar hair. We show how one can distinguish between initial data corresponding to dynamical situations in which the black holes (one or many) are not in equilibrium and initial data which are just constant-time slices of a static solution of the full equations of motion describing static black holes using (self-)interaction energies. For a single black hole, non-vanishing self-interaction energy is always related to primary scalar hair and to a dynamical black hole. Removing the self-interaction energies in multi-center solutions we get interaction energies related to the attractive and repulsive forces acting on the black holes. As shown by Brill and Lindquist, for widely separated black holes, these take the standard Newtonian and Coulombian forms plus an additional interaction term associated with the scalar charges which is attractive for like charges., Comment: Latex2e file, 38 pages, no figures

Metal-assisted chemical etching of silicon is a promising method for fabricating nanostructures with a high aspect ratio. To define a pattern for the catalyst, lift-off processes are commonly used. The lift-off step however is often a process bottle neck due to low yield, especially for smaller structures. To bypass the lift-off process, other methods such as electroplating can be utilized. In this paper, we suggest an electroplated bi-layer catalyst for vapour phase metal-assisted chemical etching as an alternative to the commonly utilised lift-off process. Samples were successfully etched in vapour, and resulting structures had feature sizes down to 10 nm.

Charge-parity (CP) asymmetries in charm decays are extremely suppressed in the Standard Model and may well be dominated by new-physics contributions. The LHCb collaboration reported the results of direct CP asymmetry measurements in $D^0\to K^+ K^-$ and $D^0\to \pi^+\pi^-$ decays with unprecedented accuracy: $a_{\rm{CP}}(K^+ K^-)=(7.7\pm5.7)\times 10^{-4}$ and $a_{\rm{CP}}(\pi^+\pi^-)=(23.2\pm6.1)\times 10^{-4}$, with the latter quantity inferred from the precise measurement of $\Delta a_{\rm{CP}} =\, a_{\rm{CP}}(K^+ K^-) -a_{\rm{CP}}(\pi^+\pi^-) =\, (-15.7\pm2.9)\times 10^{-4}$. When interpreted within the Standard Model, these values indicate a breakdown of the approximate $U$-spin symmetry of QCD. If, however, this symmetry holds and the data stem from new physics, other CP asymmetries should be enhanced as well. We derive CP asymmetry sum rules based on $\rm{SU}(3)$ flavor symmetry for $D$ meson decays into a pair of pseudoscalar mesons as well as a pair of a pseudoscalar and a vector meson for two generic scenarios, with $\Delta U=0$ and $|\Delta U|=1$ interactions, respectively. The correlations implied by the sum rules can be used to check the consistency between different measurements and to discriminate between these scenarios with future data. For instance, we find $a_{\mathrm{CP}}(\pi^{+}K^{* 0}) + a_{\mathrm{CP}}(K^{+}\overline{K}^{* 0}) = 0$ for $\Delta U=0$ new physics and the opposite relative sign for the $|\Delta U|=1$ case. One sum rule, connecting four decay modes, holds in both scenarios. We further extend our sum rules to certain differences of CP asymmetries from which the $D$ production asymmetries drop out., Comment: 11 pages, 1 figure, 4 tables

A Lagrangian gravity-wave parameterization (MS-GWaM, Multi-Scale Gravity-Wave Model) that allows for fully transient wave-mean-flow interaction and horizontal propagation is applied to orographic gravity waves for the first time. Both linear and nonlinear mountain waves are modeled in idealized simulations within the pseudo-incompressible flow solver PincFlow. Two-dimensional flows over monochromatic orographies are considered, using MS-GWaM either in its fully transient implementation or in a steady-state implementation that represents classic mountain-wave parameterizations. Comparisons of wave-resolving simulations (not using MS-GWaM) and coarse-resolution simulations (using MS-GWaM) show that allowing for transience leads to a significantly more accurate forcing of the resolved mean flow. The model is able to reproduce the transient forcing of linearly generated mountain waves that slowly propagate upwards, in contrast to the instantaneous distribution of wave energy in classic parameterizations. At high altitudes, wave breaking induces a wind reversal that is captured by the transient model but inhibited in steady-state simulations, due to the assumption of critical level formation. This shows that transience can have a substantial impact in the interaction between mountain waves and mean flow., Comment: 42 pages, 12 figures. This work has been submitted to the Journal of the Atmospheric Sciences. Copyright in this work may be transferred without further notice

People tend to use language to mention surprising properties of events: for example, when a banana is blue, we are more likely to mention color than when it is yellow. This fact is taken to suggest that yellowness is somehow a typical feature of bananas, and blueness is exceptional. Similar to how a yellow color is typical of bananas, there may also be genders that are typical of occupations. In this work, we explore this question using information theoretic techniques coupled with corpus statistic analysis. In two distinct large corpora, we do not find strong evidence that occupations and gender display the same patterns of mentioning as do bananas and color. Instead, we find that gender mentioning is correlated with femaleness of occupation in particular, suggesting perhaps that woman-dominated occupations are seen as somehow ``more gendered'' than male-dominated ones, and thereby they encourage more gender mentioning overall.

The association between log-price increments of exchange-traded equities, as measured by their spot correlation estimated from high-frequency data, exhibits a pronounced upward-sloping and almost piecewise linear relationship at the intraday horizon. There is notably lower-on average less positive-correlation in the morning than in the afternoon. We develop a nonparametric testing procedure to detect such deterministic variation in a correlation process. The test statistic has a known distribution under the null hypothesis, whereas it diverges under the alternative. It is robust against stochastic correlation. We run a Monte Carlo simulation to discover the finite sample properties of the test statistic, which are close to the large sample predictions, even for small sample sizes and realistic levels of diurnal variation. In an application, we implement the test on a monthly basis for a high-frequency dataset covering the stock market over an extended period. The test leads to rejection of the null most of the time. This suggests diurnal variation in the correlation process is a nontrivial effect in practice.

The amount of turbulent pressure in galaxy clusters is still debated, especially as for the impact of the dynamical state and the hydro-method used for simulations. We study the turbulent pressure fraction in the intra cluster medium of massive galaxy clusters. We aim to understand the impact of the hydrodynamical scheme, analysis method, and dynamical state on the final properties of galaxy clusters from cosmological simulations. We perform non-radiative simulations of a set of zoom-in regions of seven galaxy clusters with Meshless Finite Mass (MFM) and Smoothed Particle Hydrodynamics (SPH). We use three different analysis methods based on: $(i)$ the deviation from hydrostatic equilibrium, $(ii)$ the solenoidal velocity component obtained by a Helmholtz-Hodge decomposition, and $(iii)$ the small-scale velocity obtained through a multi-scale filtering approach. We split the sample of simulated clusters into active and relaxed clusters. Our simulations predict an increased turbulent pressure fraction for active compared to relaxed clusters. This is especially visible for the velocity-based methods. For these, we also find increased turbulence for the MFM simulations compared to SPH, consistent with findings from more idealized simulations. The predicted non-thermal pressure fraction varies between a few percent for relaxed clusters and $\approx13\%$ for active ones within the cluster center and increases towards the outskirts. No clear trend with redshift is visible. Our analysis quantitatively assesses the importance played by the hydrodynamical scheme and the analysis method to determine the non-thermal/turbulent pressure fraction. While our setup is relatively simple (non-radiative runs), our simulations show agreement with previous, more idealized simulations, and make a step further toward the understanding of turbulence., Comment: 15 pages, 10 figures, submitted to A&A

In this paper we present commutativity results for a general class of Szasz-Mirakjan-Durrmeyer type operators and associated differential operators and investigate their eigenfunctions.

We collect papers forecasting wind turbine failures at least two days in advance. We examine the prediction time, methods, failed components, and dataset size. We investigate the effect of using standard SCADA data and data from additional sensors, such as those measuring vibration. We observe a positive correlation between dataset size and prediction time. In the considered cases, one may roughly expect a forecast for at least two days using a dataset of one turbine year and a forecast for two hundred days from a dataset of a hundred turbine years.

We present results of our study devoted to the development of a time correction algorithm needed to precisely synchronize a free-running Rubidium atomic clock with the Coordinated Universal Time (UTC). This R&D is performed in view of the Hyper-Kamiokande (HK) experiment currently under construction in Japan, which requires a synchronization with UTC and between its different experimental sites with a precision better than $100$ ns. We use a Global Navigation Satellite System (GNSS) receiver to compare a PPS and a $10$ MHz signal, generated by a free-running Rubidium clock, to the Global Positioning System (GPS) Time signal. We use these comparisons to correct the time series (time stamps) provided by the Rubidium clock signal. We fit the difference between Rubidium and GPS Time with polynomial functions of time over a certain integration time window to extract a correction of the Rubidium time stamps in offline or online mode. In online mode, the latest fit results are used for the correction until a new comparison to GPS Time becomes available. We show that with an integration time window of around $10^4$ seconds, we can correct the time stamps drift, caused by the frequency random walk noise and the deterministic frequency drift of the free running Rubidium clock, so that the time difference with respect to GPS Time stays within a $\pm5$ ns range in both offline or online correction mode. Presented results could be of interest for other experiments in the field of neutrino physics and multi-messenger astrophysics., Comment: 32 pages, 15 figures, manuscript prepared for submission to NIM A

Graphene flake-based dispersions are attractive materials for various applications in microelectronics because of their ease of fabrication and the potential to deposit them on diverse substrates. The integration of these materials into conductive networks and microdevices requires thorough knowledge of their mechanical material properties, including adhesion. This paper presents quantitative adhesion measurements of graphene flake networks on silicon dioxide (SiO${_2}$) via button shear testing (BST). In this method, shear forces are applied to prefabricated micrometric buttons until they delaminate, providing information about the shear strength of the underlying graphene. We applied BST to graphene flake networks with different flake structures and defect densities. Flat flakes, a flat network structure, and a high flake defect density improve adhesion. We further demonstrate that graphene flake networks have stronger adhesion than chemical vapor deposited monolayer graphene grown on copper and transferred to SiO${_2}$. Hexamethyldisilazane (HMDS) increases the total adhesion force by improving flake network formation. Finally, we provide flake-type-specific delamination patterns by combining BST, optical microscopy, and Raman spectroscopy. We establish BST as a quantitative technique for measuring the adhesion of graphene dispersions and show the crucial role of interflake junctions in the overall adhesion of graphene flake networks., Comment: 34 pages

Purpose: The purpose of this study is to address the lack of uncertainty quantification in numerical hemolysis models, which are critical for medical device evaluations. Specifically, we aim to incorporate experimental variability into these models using the Markov Chain Monte Carlo (MCMC) method to enhance predictive accuracy and robustness. Methods: We applied the MCMC method to an experimental hemolysis dataset to derive detailed stochastic distributions for the hemolysis Power Law model parameters $C$, $\alpha$ and $\beta$. These distributions were then propagated through a reduced order model of the FDA benchmark pump to quantify the experimental uncertainty in hemolysis measurements with respect to the predicted pump hemolysis. Results: The MCMC analysis revealed multiple local minima in the sum of squared errors, highlighting the non-uniqueness of traditional Power Law model fitting. The MCMC results showed a constant optimal $C=3.515x10-5$ and log normal distributions of $\alpha$ and $\beta$ with means of 0.614 and 1.795, respectively. The MCMC model closely matched the mean and variance of experimental data. In comparison, conventional deterministic models are not able to describe experimental variation. Conclusion: Incorporating Uncertainty quantification through MCMC enhances the robustness and predictive accuracy of hemolysis models. This method allows for better comparison of simulated hemolysis outcomes with in-vivo experiments and can integrate additional datasets, potentially setting a new standard in hemolysis modeling.

Surface albedo is an important parameter in radiative transfer simulations of the Earth's system, as it is fundamental to correctly calculate the energy budget of the planet. The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on NASA's Terra and Aqua satellites continuously monitor daily and yearly changes in reflection at the planetary surface. The MODIS Surface Reflectance black-sky albedo dataset (MCD43D, version 6.1) gives detailed albedo maps in seven spectral bands in the visible and near-infrared range. These albedo maps allow us to classify different Lambertian surface types and their seasonal and yearly variability and change, albeit only in seven spectral bands. However, a complete set of albedo maps covering the entire wavelength range is required to simulate radiance spectra, and to correctly retrieve atmospheric and cloud properties from Earth's remote sensing. We use a Principal Component Analysis (PCA) regression algorithm to generate hyperspectral albedo maps of Earth. Combining different datasets of hyperspectral reflectance laboratory measurements for various dry soils, vegetation surfaces, and mixtures of both, we reconstruct the albedo maps in the entire wavelength range from 400 to 2500~nm. The PCA method is trained with a 10-years average of MODIS data for each day of the year. We obtain hyperspectral albedo maps with a spatial resolution of 0.05{\deg} in latitude and longitude, a spectral resolution of 10~nm, and a temporal resolution of 1~day. Using the hyperspectral albedo maps, we estimate the spectral profiles of different land surfaces, such as forests, deserts, cities and icy surfaces, and study their seasonal variability. These albedo maps shall enable to refine calculations of Earth's energy budget, its seasonal variability, and improve climate simulations., Comment: 32 pages, 19 figures. Accepted for publication on Atmospheric Measurement Techniques (AMT, Copernicus)

Sapphire is a technologically highly relevant material, but it poses many challenges to performing epitaxial thin-film deposition. We have identified and applied the conditions for adsorption-controlled homoepitaxial growth of c-plane sapphire. The films thus grown are atomically smooth, have a controlled termination, and are of outstanding crystallinity. Their chemical purity exceeds that of the substrates. The films exhibit exceptional optical properties such as a single-crystal-like bandgap and a low density of F+ centers.

In this paper we introduce a general family of Sz\'asz--Mirakjan--Durrmeyer type operators depending on an integer parameter $j \in \mathbb{Z}$. They can be viewed as a generalization of the Sz\'asz--Mirakjan--Durrmeyer operators [9], Phillips operators [11] and corresponding Kantorovich modifications of higher order. For $j\in {\mathbb{N}}$, these operators possess the exceptional property to preserve constants and the monomial $x^{j}$. It turns out, that an extension of this family covers certain well-known operators studied before, so that the outcoming results could be unified. We present the complete asymptotic expansion for the sequence of these operators. All its coefficients are given in a concise form. In order to prove the expansions for the class of locally integrable functions of exponential growth on the positive half-axis, we derive a localization result which is interesting in itself.

The transport of DNA polymers through nanoscale pores is central to many biological processes, from bacterial gene exchange to viral infection. In single-molecule nanopore sensing, the detection of nucleic acid and protein analytes relies on the passage of a long biopolymer through a nanoscale aperture. Understanding the dynamics of polymer translocation through nanopores, especially the relation between ionic current signal and polymer conformations is thus essential for the successful identification of targets. Here, by analyzing ionic current traces of dsDNA translocation, we reveal that features up to now uniquely associated with knots are instead different structural motifs: plectonemes. By combining experiments and simulations, we demonstrate that such plectonemes form because of the solvent flow that induces rotation of the helical DNA fragment in the nanopore, causing torsion propagation outwards from the pore. Molecular dynamic simulations reveal that plectoneme initialization is dominated by the applied torque while the translocation time and size of the plectonemes depend on the coupling of torque and pulling force, a mechanism that might also be relevant for in vivo DNA organization. Experiments with nicked DNA constructs show that the number of plectonemes depends on the rotational constraints of the translocating molecules. Thus, our work introduces plectonemes as essential structural features that must be considered for accurate analysis of polymer transport in the nanopore., Comment: 20 pages, 4 figures

Most numerical simulations of galaxy formation and evolution are unable to properly resolve the turbulent cascade at or below the resolution scale and turbulence models are required to capture the motion of eddies on those unresolved scales. In this study, we investigate the impact of turbulent metal diffusion models on multiphase outflows originating from dwarf galaxies ($M_{\rm halo} \sim 10^{10} - 10^{11}$ M$_\odot$). We use our state-of-the-art numerical model for the formation of single stars and non-equilibrium cooling and hydrogen chemistry. Our simulations are carried out at a mass resolution of $\sim$1 M$_{\odot}$, where the individual supernova explosions are resolved in terms of hot-phase generation and momentum input. We find that mass, energy, and metal loading factors are only weakly affected by the inclusion of a metal diffusion model. The metal enrichment factor at low altitude above the galactic disk is higher by around 20 per cent when the metal diffusion model is included. Specifically, we find more efficient cooling in the cold interstellar medium, as higher amounts of metals are kept in the cold dense phase. The most striking effect of the metal diffusion model is that, without metal diffusion, there is more rapid cooling in the hot phase and a reduced sound speed by a factor of two. Specifically, we find that the hot phase is more metal enriched in the case without metal diffusion leading to more rapid (over) cooling of that phase which is consistent with the higher sound speed we find in the runs with metal diffusion., Comment: 19 pages, 11 figures, submitted to ApJ, comments welcome!

Grain boundary (GB) segregation in magnesium (Mg) substantially influences its mechanical properties and performance. Atomic-scale modelling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at highly symmetric GBs in Mg alloys, often failing to capture the diversity of local atomic environments and segregation energies, resulting in inaccurate structure-property predictions. This study employs atomistic simulations and machine learning models to systematically investigate the segregation behavior of common solute elements in polycrystalline Mg at both ground state and finite temperatures. The machine learning models accurately predict segregation thermodynamics by incorporating energetic and structural descriptors. We found that segregation energy and vibrational free energy follow skew-normal distributions, with hydrostatic stress, an indicator of excess free volume, emerging as an important factor influencing segregation tendency. The local atomic environment's flexibility, quantified by flexibility volume, is also crucial in predicting GB segregation. Comparing the grain boundary solute concentrations calculated via the Langmuir-McLean isotherm with experimental data, we identified a pronounced segregation tendency for Nd, highlighting its potential for GB engineering in Mg alloys. This work demonstrates the powerful synergy of atomistic simulations and machine learning, paving the way for designing advanced lightweight Mg alloys with tailored properties.

Knowledge about the maximum tire-road friction potential is an important factor to ensure the driving stability and traffic safety of the vehicle. Many authors proposed systems that either measure friction related parameters or estimate the friction coefficient directly via a mathematical model. However these systems can be negatively impacted by environmental factors or require a sufficient level of excitation in the form of tire slip, which is often too low under practical conditions. Therefore, this work investigates, if a more robust estimation can be achieved by fusing the information of multiple systems using a Bayesian network, which models the statistical relationship between the sensors and the maximum friction coefficient. First, the Bayesian network is evaluated over its entire domain to compare the inference process to all possible road conditions. After that, the algorithm is applied to data from a test vehicle to demonstrate the performance under real conditions.

We consider a large family of theories of equivalence relations, each with finitely many classes, and assuming the existence of an $\omega$-Erdos cardinal, we determine which of these theories are Borel complete. We develop machinery, including {\em forbidding nested sequences} which implies a tight upper bound on Borel complexity, and {\em admitting cross-cutting absolutely indiscernible sets} which in our context implies Borel completeness. In the Appendix we classify the reducts of theories of refining equivalence relations, possibly with infinite splitting.

We propose a method, a model, and a form of presenting model results for condition monitoring of a small set of wind turbines with rare failures. The main new ingredient of the method is to sample failure thresholds according to the profit they give to an operating company. The model is a multiple linear regression with seasonal components and external regressors, representing all sensor components except for the considered one. To overcome the scarcity of the training data, we use the median sensor values from all available turbines in their healthy state. The cumulated deviation from the normal behavior model obtained for this median turbine is calibrated for each turbine at the beginning of the test period and after known failures. The proposed form of presenting results is to set a scale for possible costs, control for random maintenance, and show a whole distribution of costs depending on the free model parameters. We make a case study on an open dataset with SCADA data from multiple sensors and show that considering the influence of turbine components is more critical than seasonality. The distribution, the average, and the standard deviation of maintenance costs can be very different for similar minimal costs. Random maintenance can be more profitable than reactive maintenance and other approaches. Our predictive maintenance model outperforms random maintenance and competitors for the whole set of considered turbines, giving substantial savings.

Combined extracorporeal membrane oxygenation (ECMO) and continuous renal replacement therapy (CRRT) pose complex hemodynamic challenges in intensive care. In this study, a comprehensive lumped parameter model (LPM) is developed to simulate the cardiovascular system, incorporating ECMO and CRRT circuit dynamics. The model is used to analyze nine CRRT-ECMO connection schemes under varying flow conditions. Using a robust parameter identification framework based on global sensitivity analysis (GSA) and multi-start gradient-based optimization, we calibrated the model on 30 clinical data points from eight veno-arterial ECMO patients. Our results indicate that CRRT has a significant impact on the cardiovascular system, with changes in pulmonary artery pressure of up to 202.5 %, highly dependent on ECMO flow. The GSA proved to be a powerful tool to improve the parameter estimation process. The established parameter estimation framework is fast and robust without the need for hyperparameter tuning and improves the parameter estimation process with an R^2>0.98 between simulation and experimental data. It uses modeling methods that could pave the way for real-time applications in intensive care. This open-source framework provides a valuable tool for the systematic evaluation of combined ECMO and CRRT, which can be used to develop standardized treatment protocols and improve patient outcomes in critical care. In addition, as a digital twin, this model also provides a good basis for addressing research questions related to mechanical circulatory and respiratory support., Comment: Model available on GitHub: https://github.com/nikithiel/ECLIPSE

In this article, we propose a novel standalone hybrid Spiking-Convolutional Neural Network (SC-NN) model and test on using image inpainting tasks. Our approach uses the unique capabilities of SNNs, such as event-based computation and temporal processing, along with the strong representation learning abilities of CNNs, to generate high-quality inpainted images. The model is trained on a custom dataset specifically designed for image inpainting, where missing regions are created using masks. The hybrid model consists of SNNConv2d layers and traditional CNN layers. The SNNConv2d layers implement the leaky integrate-and-fire (LIF) neuron model, capturing spiking behavior, while the CNN layers capture spatial features. In this study, a mean squared error (MSE) loss function demonstrates the training process, where a training loss value of 0.015, indicates accurate performance on the training set and the model achieved a validation loss value as low as 0.0017 on the testing set. Furthermore, extensive experimental results demonstrate state-of-the-art performance, showcasing the potential of integrating temporal dynamics and feature extraction in a single network for image inpainting., Comment: 7 Pages, 3 figures, and 2 tables

When silica-filled rubber compounds are deformed, structural modifications in the material's bulk lead to irreversible damage, the most significant of which is cavitation appearing within the interfaces of interconnected polymer and filler networks. This work introduces a new method to analyze cavitation in industrial-grade rubbers based on Ultra Small-Angle X-ray Scattering. This method employs a specially designed multi-sample stretching device for high-throughput measurements with statistical relevance. The proposed data reduction approach allows for early detection and quantification of cavitation while providing at the same time information on the hierarchical filler structures at length scales ranging from the primary particle size to large silica agglomerates over four orders of magnitude. To validate the method, the scattering of SSBR rubber compounds filled with highly dispersible silica at different ratios was measured under quasi-static strain. The strain was applied in incremental steps up to a maximum achievable elongation or breakage of the sample. From the measurements performed in multiple repetitions, it was found that the minimum strain necessary for cavity formation and the size evolution of the cavities with increasing strain are comparable between these samples. The sample with the highest polymer content showed the lowest rate of cavity formation and higher durability of silica structures. The structural stability of the compounds was determined by the evolution of the filler hierarchical structures, obtained by fitting data across the available strain range., Comment: 17 pages with 2 tables and 13 figures

The mechanics of animal cells is strongly determined by stress fibers, which are contractile filament bundles that form dynamically in response to extracellular cues. Stress fibers allow the cell to adapt its mechanics to environmental conditions and to protect it from structural damage. While the physical description of single stress fibers is well-developed, much less is known about their spatial distribution on the level of whole cells. Here, we combine a finite element method for one-dimensional fibers embedded in an elastic bulk medium with dynamical rules for stress fiber formation based on genetic algorithms. We postulate that their main goal is to achieve minimal mechanical stress in the bulk material with as few fibers as possible. The fiber positions and configurations resulting from this optimization task alone are in good agreement with those found in experiments where cells in 3D-scaffolds were mechanically strained at one attachment point. For optimized configurations, we find that stress fibers typically run through the cell in a diagonal fashion, similar to reinforcement strategies used for composite material., Comment: 37 pages, 11 figures, 72 references

Autonomous trucking is a promising technology that can greatly impact modern logistics and the environment. Ensuring its safety on public roads is one of the main duties that requires an accurate perception of the environment. To achieve this, machine learning methods rely on large datasets, but to this day, no such datasets are available for autonomous trucks. In this work, we present MAN TruckScenes, the first multimodal dataset for autonomous trucking. MAN TruckScenes allows the research community to come into contact with truck-specific challenges, such as trailer occlusions, novel sensor perspectives, and terminal environments for the first time. It comprises more than 740 scenes of 20s each within a multitude of different environmental conditions. The sensor set includes 4 cameras, 6 lidar, 6 radar sensors, 2 IMUs, and a high-precision GNSS. The dataset's 3D bounding boxes were manually annotated and carefully reviewed to achieve a high quality standard. Bounding boxes are available for 27 object classes, 15 attributes, and a range of more than 230m. The scenes are tagged according to 34 distinct scene tags, and all objects are tracked throughout the scene to promote a wide range of applications. Additionally, MAN TruckScenes is the first dataset to provide 4D radar data with 360{\deg} coverage and is thereby the largest radar dataset with annotated 3D bounding boxes. Finally, we provide extensive dataset analysis and baseline results. The dataset, development kit, and more are available online., Comment: Accepted to NeurIPS 2024 Datasets and Benchmarks Track

The robustness of topological properties, such as quantized currents, generally depends on the existence of gaps surrounding the relevant energy levels or on symmetry-forbidden transitions. Here, we observe quantized currents that survive the addition of bounded local disorder beyond the closing of the relevant instantaneous energy gaps in a driven Aubry-Andr\'e-Harper chain, a prototypical model of quasiperiodic systems. We explain the robustness using a local picture in \textit{configuration-space} based on Landau-Zener transitions, which rests on the Anderson localisation of the eigenstates. Moreover, we propose a protocol, directly realizable in for instance cold atoms or photonic experiments, which leverages this stability to prepare topological many-body states with high Chern numbers and opens new experimental avenues for the study of both the integer and fractional quantum Hall effects., Comment: 12 pages, 8 figures. Updated fig5 with a finer study of the influence of the pumping rate on the quantization of the current. Additional minor edits

Medical doctors rely on images of the human anatomy, such as magnetic resonance imaging (MRI), to localize regions of interest in the patient during diagnosis and treatment. Despite advances in medical imaging technology, the information conveyance remains unimodal. This visual representation fails to capture the complexity of the real, multisensory interaction with human tissue. However, perceiving multimodal information about the patient's anatomy and disease in real-time is critical for the success of medical procedures and patient outcome. We introduce a Multimodal Medical Image Interaction (MMII) framework to allow medical experts a dynamic, audiovisual interaction with human tissue in three-dimensional space. In a virtual reality environment, the user receives physically informed audiovisual feedback to improve the spatial perception of anatomical structures. MMII uses a model-based sonification approach to generate sounds derived from the geometry and physical properties of tissue, thereby eliminating the need for hand-crafted sound design. Two user studies involving 34 general and nine clinical experts were conducted to evaluate the proposed interaction framework's learnability, usability, and accuracy. Our results showed excellent learnability of audiovisual correspondence as the rate of correct associations significantly improved (p < 0.001) over the course of the study. MMII resulted in superior brain tumor localization accuracy (p < 0.05) compared to conventional medical image interaction. Our findings substantiate the potential of this novel framework to enhance interaction with medical images, for example, during surgical procedures where immediate and precise feedback is needed., Comment: Accepted for publication in IEEE TVCG; presentation at IEEE ISMAR 2024

The $^{246}$Cm(n,$\gamma$) and $^{248}$Cm(n,$\gamma$) cross-sections have been measured at the Experimental Area 2 (EAR2) of the n_TOF facility at CERN with three C$_6$D$_6$ detectors. This measurement is part of a collective effort to improve the capture cross-section data for Minor Actinides (MAs), which are required to estimate the production and transmutation rates of these isotopes in light water reactors and innovative reactor systems. In particular, the neutron capture in $^{246}$Cm and $^{248}$Cm open the path for the formation of other Cm isotopes and heavier elements such as Bk and Cf and the knowledge of (n,$\gamma$) cross-sections of these Cm isotopes plays an important role in the transport, transmutation and storage of the spent nuclear fuel. The reactions $^{246}$Cm(n,$\gamma$) and $^{248}$Cm(n,$\gamma$) have been the two first capture measurements analyzed at n_TOF EAR2. Until this experiment and two recent measurements performed at J-PARC, there was only one set of data of the capture cross-sections of $^{246}$Cm and $^{248}$Cm, that was obtained in 1969 in an underground nuclear explosion experiment. In the measurement at n_TOF a total of 13 resonances of $^{246}$Cm between 4 and 400 eV and 5 of $^{248}$Cm between 7 and 100 eV have been identified and fitted. The radiative kernels obtained for $^{246}$Cm are compatible with JENDL-5, but some of them are not with JENDL-4, which has been adopted by JEFF-3.3 and ENDF/B-VIII.0. The radiative kernels obtained for the first three $^{248}$Cm resonances are compatible with JENDL-5, however, the other two are not compatible with any other evaluation and are 20% and 60% larger than JENDL-5.

For a positive and invertible linear operator $T$ acting on a $C^*$-algebra, we give necessary and sufficient criteria for the inverse operator $T^{-1}$ to be positive, too. Moreover, a simple counterexample shows that $T^{-1}$ need not be positive even if $T$ is unital and its spectrum is contained in the unit circle., Comment: 16 pages

In this work, we propose an efficient algorithm for the calculation of the Betti matching, which can be used as a loss function to train topology aware segmentation networks. Betti matching loss builds on techniques from topological data analysis, specifically persistent homology. A major challenge is the computational cost of computing persistence barcodes. In response to this challenge, we propose a new, highly optimized implementation of Betti matching, implemented in C++ together with a python interface, which achieves significant speedups compared to the state-of-the-art implementation Cubical Ripser. We use Betti matching 3D to train segmentation networks with the Betti matching loss and demonstrate improved topological correctness of predicted segmentations across several datasets. The source code is available at https://github.com/nstucki/Betti-Matching-3D.

Predictive modeling involving simulation and sensor data at the same time, is a growing challenge in computational science. Even with large-scale finite element models, a mismatch to the sensor data often remains, which can be attributed to different sources of uncertainty. For such a scenario, the statistical finite element method (statFEM) can be used to condition a simulated field on given sensor data. This yields a posterior solution which resembles the data much better and additionally provides consistent estimates of uncertainty, including model misspecification. For frequency or parameter dependent problems, occurring, e.g. in acoustics or electromagnetism, solving the full order model at the frequency grid and conditioning it on data quickly results in a prohibitive computational cost. In this case, the introduction of a surrogate in form of a reduced order model yields much smaller systems of equations. In this paper, we propose a reduced order statFEM framework relying on Krylov-based moment matching. We introduce a data model which explicitly includes the bias induced by the reduced approximation, which is estimated by an inexpensive error indicator. The results of the new statistical reduced order method are compared to the standard statFEM procedure applied to a ROM prior, i.e. without explicitly accounting for the reduced order bias. The proposed method yields better accuracy and faster convergence throughout a given frequency range for different numerical examples., Comment: 32 pages, 12 figures, associated code available at https://github.com/herluc/statROM

A specific challenge with deep learning approaches for molecule generation is generating both syntactically valid and chemically plausible molecular string representations. To address this, we propose a novel generative latent-variable transformer model for small molecules that leverages a recently proposed molecular string representation called SAFE. We introduce a modification to SAFE to reduce the number of invalid fragmented molecules generated during training and use this to train our model. Our experiments show that our model can generate novel molecules with a validity rate > 90% and a fragmentation rate < 1% by sampling from a latent space. By fine-tuning the model using reinforcement learning to improve molecular docking, we significantly increase the number of hit candidates for five specific protein targets compared to the pre-trained model, nearly doubling this number for certain targets. Additionally, our top 5% mean docking scores are comparable to the current state-of-the-art (SOTA), and we marginally outperform SOTA on three of the five targets., Comment: 12 pages, 6 figures, Proceedings of the ICML 2024 Workshop on Accessible and Effi- cient Foundation Models for Biological Discovery, Vienna, Austria. 2024

Traditional nanophotonic sensing schemes utilize evanescent fields in dielectric or metallic nanoparticles, which confine far-field radiation in dispersive and lossy media. Apart from the lack of a well-defined sensing volume that can be accompanied by moderate sensitivities, these structures suffer from the generally limited access to the modal field, which is key for sensing performance. Recently, a novel strategy for dielectric nanophotonics has been demonstrated, namely, the resonant confinement of light in air. So-called Mie voids created in high-index dielectric host materials support localized resonant modes with exceptional properties. In particular, due to the confinement in air, these structures benefit from the full access to the modal field inside the void. We utilize these Mie voids for refractive index sensing in single voids with volumes down to 100 attoliters and sensitivities on the order of 400 nm per refractive index unit. Taking the signal-to-noise ratio of our measurements into account, we demonstrate detection of refractive index changes as small as 6.9 x 10-4 in a defined volume of just 850 attoliters. The combination of our Mie void sensor platform with appropriate surface functionalization will even enable specificity to biological or other analytes of interest, as the sensing volumes are on the order of cellular signaling chemicals of single vesicles in cellular synapses.

The vitrification of aqueous solutions through rapid cooling is a remarkable achievement that launched the field of cryo-electron microscopy (cryo-EM) and has enabled the cryopreservation of biological specimens. For judging the feasibility of a vitrification experiment, the critical cooling rate of pure water is a frequently cited reference quantity. However, an accurate determination has remained elusive, with estimates varying by several orders of magnitude. Here, we employ in situ and time-resolved electron microscopy to obtain a precise measurement. We use shaped microsecond laser pulses to briefly melt an amorphous ice sample before flash freezing it with a variable, well-defined cooling rate. This allows us to directly measure the critical cooling rate of pure water, which we determine to be $6.4\cdot10^{6}$ K/s. Our experimental approach also expands the toolkit of microsecond time-resolved cryo-EM, an emerging technique, in which a cryo sample is similarly flash melted and revitrified with a laser pulse.

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