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Learning-based methods have demonstrated remarkable performance in solving inverse problems, particularly in image reconstruction tasks. Despite their success, these approaches often lack theoretical guarantees, which are crucial in sensitive applications such as medical imaging. Recent works by Arndt et al (2023 Inverse Problems 39 125018, 2024 Inverse Problems 40 045021) addressed this gap by analyzing a data-driven reconstruction method based on invertible residual networks (iResNets). They revealed that, under reasonable assumptions, this approach constitutes a convergent regularization scheme. However, the performance of the reconstruction method was only validated on academic toy problems and small-scale iResNet architectures. In this work, we address this gap by evaluating the performance of iResNets on two real-world imaging tasks: a linear blurring operator and a nonlinear diffusion operator. To do so, we extend some of the theoretical results from Arndt et al to encompass nonlinear inverse problems and offer insights for the design of large-scale performant iResNet architectures. Through numerical experiments, we compare the performance of our iResNet models against state-of-the-art neural networks, confirming their efficacy. Additionally, we numerically investigate the theoretical guarantees of this approach and demonstrate how the invertibility of the network enables a deeper analysis of the learned forward operator and its learned regularization.

Inferring treatment effects on a survival time outcome based on data from an observational study is challenging due to the presence of censoring and possible confounding. An additional challenge occurs when a unit's treatment affects the outcome of other units, i.e., there is interference. In some settings, units may be grouped into clusters such that it is reasonable to assume interference only occurs within clusters, i.e., there is clustered interference. In this paper, methods are developed which can accommodate confounding, censored outcomes, and clustered interference. The approach avoids parametric assumptions and permits inference about counterfactual scenarios corresponding to any stochastic policy which modifies the propensity score distribution, and thus may have application across diverse settings. The proposed nonparametric sample splitting estimators allow for flexible data-adaptive estimation of nuisance functions and are consistent and asymptotically normal with parametric convergence rates. Simulation studies demonstrate the finite sample performance of the proposed estimators, and the methods are applied to a cholera vaccine study in Bangladesh.

Phosphorene, a puckered two-dimensional allotrope of phosphorus, has sparked considerable interest in recent years due to its potential especially for optoelectronic applications with its layer-number-dependant direct band gap and strongly bound excitons. However, detailed experimental characterization of its intrinsic defects as well as its defect creation characteristics under electron irradiation are scarce. Here, we report on the creation and stability of a variety of defect configurations under 60 kV electron irradiation in mono- and bilayer phosphorene including the first experimental reports of stable adatom-vacancy-complexes. Displacement cross section measurements in bilayer phosphorene yield a value of 7.7 +- 1.4 barn with an estimated lifetime of adatom-vacancy-complexes of 19.9 +- 0.7 s, while some are stable for up to 68 s under continuous electron irradiation. Surprisingly, ab initio-based simulations indicate that the complexes should readily recombine, even in structures strained by up to 3 %. The presented results will help to improve the understanding of the wide variety of defects in phosphorene, their creation, and their stability, which may enable new pathways for defect engineered phosphorene devices., Comment: 11 pages, 7 figures

The application of machine learning to medical ultrasound videos of the heart, i.e., echocardiography, has recently gained traction with the availability of large public datasets. Traditional supervised tasks, such as ejection fraction regression, are now making way for approaches focusing more on the latent structure of data distributions, as well as generative methods. We propose a model trained exclusively by knowledge distillation, either on real or synthetical data, involving retrieving masks suggested by a teacher model. We achieve state-of-the-art (SOTA) values on the task of identifying end-diastolic and end-systolic frames. By training the model only on synthetic data, it reaches segmentation capabilities close to the performance when trained on real data with a significantly reduced number of weights. A comparison with the 5 main existing methods shows that our method outperforms the others in most cases. We also present a new evaluation method that does not require human annotation and instead relies on a large auxiliary model. We show that this method produces scores consistent with those obtained from human annotations. Relying on the integrated knowledge from a vast amount of records, this method overcomes certain inherent limitations of human annotator labeling. Code: https://github.com/GregoirePetit/EchoDFKD

Since the invention of the laser nonlinear effects such as filamentation, Rabi-cycling and collective emission have been explored in the optical regime leading to a wide range of scientific and industrial applications. X-ray free electron lasers (XFELs) have led to the extension of many optical techniques to X-rays for their advantages of angstrom scale spatial resolution and elemental specificity. One such example is XFEL driven population inversion of 1s core hole states resulting in inner-shell K${\alpha}$ (2p to 1s) X-ray lasing in elements ranging from neon to copper, which has been utilized for nonlinear spectroscopy and development of next generation X-ray laser sources. Here we show that strong lasing effects, similar to those observed in the optical regime, can occur at 1.5 to 2.1 angstrom wavelengths during high intensity (> ${10^{19}}$ W/cm${^{2}}$) XFEL driven inner-shell lasing and superfluorescence of copper and manganese. Depending on the temporal substructure of the XFEL pump pulses, the resulting inner-shell X-ray laser pulses can exhibit strong spatial inhomogeneities as well as spectral inhomogeneities and broadening. Through 3D Maxwell Bloch theory we show that the observed spatial inhomogeneities result from X-ray filamentation, and that the spectral broadening is driven by Rabi cycling with sub-femtosecond periods. These findings indicate that we have generated Angstrom-wavelength x-ray pulses (containing ${10^{6}}$ - ${10^{8}}$ photons) in the strong lasing regime, some of them with pulse lengths of less than 100 attoseconds.

In this work, we study the influence of domain-specific characteristics when defining a meaningful notion of predictive uncertainty on graph data. Previously, the so-called Graph Posterior Network (GPN) model has been proposed to quantify uncertainty in node classification tasks. Given a graph, it uses Normalizing Flows (NFs) to estimate class densities for each node independently and converts those densities into Dirichlet pseudo-counts, which are then dispersed through the graph using the personalized Page-Rank algorithm. The architecture of GPNs is motivated by a set of three axioms on the properties of its uncertainty estimates. We show that those axioms are not always satisfied in practice and therefore propose the family of Committe-based Uncertainty Quantification Graph Neural Networks (CUQ-GNNs), which combine standard Graph Neural Networks with the NF-based uncertainty estimation of Posterior Networks (PostNets). This approach adapts more flexibly to domain-specific demands on the properties of uncertainty estimates. We compare CUQ-GNN against GPN and other uncertainty quantification approaches on common node classification benchmarks and show that it is effective at producing useful uncertainty estimates., Comment: 17 pages, 4 figures, 1 table. Accepted at ECML PKDD 2024. arXiv admin note: substantial text overlap with arXiv:2406.04041

The evolution of charge and orbital ordering phenomena in optimally-doped \textit{RE}\textsubscript{5/8}Ca\textsubscript{3/8}MnO\textsubscript{3} (RECMO, \textit{RE} $=$ rare-earth) manganite perovskites has been investigated through average structure synchrotron x-ray and neutron powder diffraction techniques. We demonstrate the intricate relationship between the \textit{B}O\textsubscript{6} octahedral rotation magnitude and lattice strain distortions acting in this series and how they tune macroscopic signatures describing ordering behavior. Through careful symmetry-motivated crystallographic analysis, we show that for the range of RECMO compositions which famously contain maxima in the colossal magnetoresistance (CMR) response, their lattice strain states are in close proximity to that associated with a novel orbital order:charge disordered state we have recently unveiled in the quadruple manganite perovskites Na\textsubscript{1-\textit{x}}Ca\textsubscript{\textit{x}}Mn\textsubscript{7}O\textsubscript{12}. We establish that this order is the primary state which competes with the ferromagnetic metallic state which ultimately leads to phase coexistence and the emergence of CMR. Our results lend themselves to aiding a further understanding of how particular chemical complexities can control charge and orbital ordering phenomena, and also the general properties of manganite perovskites and other related systems \textit{via} strain effects., Comment: 10 pages, 5 figures

Nonabelian gauge theories are chaotic in the classical limit. We discuss new evidence from SU(2) lattice gauge theory that they are also chaotic at the quantum level. We also describe possible future studies aimed at discovering the consequences of this insight., Comment: Lecture presented at the Particles and Plasmas Symposium 2024, Budapest (Hungary)

The observable behavior of a system usually carries useful information about its internal state, properties, and potential future behaviors. In this paper, we introduce configuration monitoring to determine an unknown configuration of a running system based on observations of its behavior. We develop a modular and generic pipeline to synthesize automata-theoretic configuration monitors from a featured transition system model of the configurable system to be monitored. The pipeline further allows synthesis under partial observability and network-induced losses as well as predictive configuration monitors taking the potential future behavior of a system into account. Beyond the novel application of configuration monitoring, we show that our approach also generalizes and unifies existing work on runtime monitoring and fault diagnosis, which aim at detecting the satisfaction or violation of properties and the occurrence of faults, respectively. We empirically demonstrate the efficacy of our approach with a case study on configuration monitors synthesized from configurable systems community benchmarks., Comment: Accepted for ATVA2024

This review paper describes the energy-upgraded CEBAF accelerator. This superconducting linac has achieved 12 GeV beam energy by adding 11 new high-performance cryomodules containing eighty-eight superconducting cavities that have operated CW at an average accelerating gradient of 20 MV/m. After reviewing the attributes and performance of the previous 6 GeV CEBAF accelerator, we discuss the upgraded CEBAF accelerator system in detail with particular attention paid to the new beam acceleration systems. In addition to doubling the acceleration in each linac, the upgrade included improving the beam recirculation magnets, adding more helium cooling capacity to allow the newly installed modules to run cold, adding a new experimental hall, and improving numerous other accelerator components. We review several of the techniques deployed to operate and analyze the accelerator performance, and document system operating experience and performance. In the final portion of the document, we present much of the current planning regarding projects to improve accelerator performance and enhance operating margins, and our plans for ensuring CEBAF operates reliably into the future. For the benefit of potential users of CEBAF, the performance and quality measures for beam delivered to each of the experimental halls is summarized in the appendix., Comment: 66 pages, 73 figures, 21 tables

We introduce a variational first-order Sobolev calculus on metric measure spacetimes. The key object is the maximal weak subslope of an arbitrary causal function, which plays the role of the (Lorentzian) modulus of its differential. It is shown to satisfy certain chain and Leibniz rules, certify a locality property, and be compatible with its smooth analog. In this setup, we propose a quadraticity condition termed infinitesimal Minkowskianity, which singles out genuinely Lorentzian structures among Lorentz-Finsler spacetimes. Moreover, we establish a comparison theorem for a nonlinear yet elliptic $p$-d'Alembertian in a weak form under the timelike measure contraction property. As a particular case, this extends Eschenburg's classical estimate past the timelike cut locus., Comment: 132 pages. Comments welcome

We analyze the classical and quantized center-of-mass motion of a polarizable particle interacting with the fluctuations of the electromagnetic (EM) field in the presence of a medium. As a polarizable particle is immersed in a thermal environment, the momentum impulses imparted by the field fluctuations lead to momentum diffusion and drag for the particle's classical center of mass. When considering the quantized center-of-mass motion of the particle, these very fluctuations gain information about its position, leading to decoherence in the position basis. We derive a position localization master equation for the particle's quantized center of mass, and examine its classical center-of-mass momentum diffusion, elucidating correspondences between classical and quantum Brownian motion of polarizable particles near media.

This work develops an efficient real-time inverse formulation for inferring the aerodynamic surface pressures on a hypersonic vehicle from sparse measurements of the structural strain. The approach aims to provide real-time estimates of the aerodynamic loads acting on the vehicle for ground and flight testing, as well as guidance, navigation, and control applications. Specifically, the approach targets hypersonic flight conditions where direct measurement of the surface pressures is challenging due to the harsh aerothermal environment. For problems employing a linear elastic structural model, we show that the inference problem can be posed as a least-squares problem with a linear constraint arising from a finite element discretization of the governing elasticity partial differential equation. Due to the linearity of the problem, an explicit solution is given by the normal equations. Pre-computation of the resulting inverse map enables rapid evaluation of the surface pressure and corresponding integrated quantities, such as the force and moment coefficients. The inverse approach additionally allows for uncertainty quantification, providing insights for theoretical recoverability and robustness to sensor noise. Numerical studies demonstrate the estimator performance for reconstructing the surface pressure field, as well as the force and moment coefficients, for the Initial Concept 3.X (IC3X) conceptual hypersonic vehicle., Comment: 22 pages, 15 figures

All-optical helicity-independent switching (AO-HIS) is of interest for ultrafast and energy efficient magnetic switching in future magnetic data storage approaches. Yet, to achieve high bit density magnetic recording it is necessary to reduce the size of the magnetic bits addressed by laser pulses at well-controlled positions. Metallic nanostructures that support localized surface plasmons enable spatial electromagnetic confinement well below the diffraction limit and rare-earth transition metal alloys such as GdTbCo have demonstrated nanometre-sized stable domains. Here, we deposit plasmonic gold nanostructures on a GdTbCo film and probe the magnetic state using magnetic force microscopy. We observe localized AO-HIS down to a critical dimension of 240 nm after excitation of the gold nanostructures by a single 370 fs long laser pulse with a centre wavelength of 1030 nm. We demonstrate that the strong localization of optical fields through plasmonic nanostructures enables reproducible localized nanoscale AO-HIS at sub-wavelength length scales. We study the influence of the localized electromagnetic field enhancement by the plasmonic nanostructures on the required fluence to switch the magnetization., Comment: 10 pages, 4 figures

Low-energy-electron emission from resonant Auger final states via intermolecular Coulombic decay (RA-ICD) has been previously described as a promising scenario for controlling radiation damage for medical purposes, but has so far only been observed in prototypical atomic and molecular van der Waals dimers and clusters. Here, we report the experimental observation of RA-ICD in aqueous solution. We show that for solvated Ca$^{2+}$ ions, the emission can be very efficiently controlled by tuning the photon energy of exciting X-rays to inner-shell resonances of the ions. Our results provide the next step from proving RA-ICD in relatively simple prototype systems to understanding the relevance and potential applications of ICD in real-life scenarios.

The analysis of variance (ANOVA) decomposition offers a systematic method to understand the interaction effects that contribute to a specific decision output. In this paper we introduce Neural-ANOVA, an approach to decompose neural networks into glassbox models using the ANOVA decomposition. Our approach formulates a learning problem, which enables rapid and closed-form evaluation of integrals over subspaces that appear in the calculation of the ANOVA decomposition. Finally, we conduct numerical experiments to illustrate the advantages of enhanced interpretability and model validation by a decomposition of the learned interaction effects., Comment: 8 pages, 4 figures, 5 tables

We investigate Brill-Noether loci of two kinds of spectral curves: double covers whose branch locus is a canonical divisor and spectral covers of the projective line. Our techniques are based on the Hitchin correspondence and Higgs bundles. For the first kind our results determine the gonality sequence, when the rank of the linear system is much smaller than the genus. For this the curve and the branch divisor are assumed to be general. In the second case we compute dimensions of the finer splitting loci if these spaces are non-empty. We compare this result with the existing literature about general curves with a fixed gonality.

Semiconductor-superconductor hybrid materials are used as a platform to realise Andreev bound states, which hold great promise for quantum applications. These states require transparent interfaces between the semiconductor and superconductor, which are typically realised by in-situ deposition of an Al superconducting layer. Here we present a hybrid material based on an InAs two-dimensional electron gas (2DEG) combined with in-situ deposited Nb and NbTi superconductors, which offer a larger operating range in temperature and magnetic field due to their larger superconducting gap. We overcome the inherent difficulty associated with the formation of an amorphous interface between III-V semiconductors and Nb-based superconductors by introducing a 7 nm Al interlayer. The Al interlayer provides an epitaxial connection between an in-situ magnetron sputtered Nb or NbTi thin film and a shallow InAs 2DEG. This metal-to-metal epitaxy is achieved by optimization of the material stack and results in an induced superconducting gap of approximately 1 meV, determined from transport measurements of superconductor-semiconductor Josephson junctions. This induced gap is approximately five times larger than the values reported for Al-based hybrid materials and indicates the formation of highly-transparent interfaces that are required in high-quality hybrid material platforms.

We consider a biased version of Maker-Breaker domination games, which were recently introduced by Gledel, Ir{\v{s}}i{\v{c}}, and Klav{\v{z}}ar. Two players, Dominator and Staller, alternatingly claim vertices of a graph $G$ where Dominator is allowed to claim up to $b$ vertices in every round and she wins if and only if she occupies all vertices of a dominating set of $G$. For this game, we prove a full characterization of all trees on which Dominator has a winning strategy. For the number of rounds which Dominator needs to win, we give exact results when played on powers of paths or cycles, and for all trees we provide bounds which are optimal up to a constant factor not depending on $b$. Furthermore, we discuss general minimum degree conditions and study how many vertices can still be dominated by Dominator even when Staller has a winning strategy.

Swarm robots offer fascinating opportunities to perform complex tasks beyond the capabilities of individual machines. Just as a swarm of ants collectively moves a large object, similar functions can emerge within a group of robots through individual strategies based on local sensing. However, realizing collective functions with individually controlled microrobots is particularly challenging due to their micrometer size, large number of degrees of freedom, strong thermal noise relative to the propulsion speed, complex physical coupling between neighboring microrobots, and surface collisions. Here, we implement Multi-Agent Reinforcement Learning (MARL) to generate a control strategy for up to 200 microrobots whose motions are individually controlled by laser spots. During the learning process, we employ so-called counterfactual rewards that automatically assign credit to the individual microrobots, which allows for fast and unbiased training. With the help of this efficient reward scheme, swarm microrobots learn to collectively transport a large cargo object to an arbitrary position and orientation, similar to ant swarms. We demonstrate that this flexible and versatile swarm robotic system is robust to variations in group size, the presence of malfunctioning units, and environmental noise. Such control strategies can potentially enable complex and automated assembly of mobile micromachines, programmable drug delivery capsules, and other advanced lab-on-a-chip applications., Comment: 40 pages, 15 figures

$\Omega$-automata and Wilke algebras are formalisms for characterising $\omega$-regular languages via their ultimately periodic words. $\Omega$-automata read finite representations of ultimately periodic words, called lassos, and they are a subclass of lasso automata. We introduce lasso semigroups as a generalisation of Wilke algebras that mirrors how lasso automata generalise $\Omega$-automata, and we show that finite lasso semigroups characterise regular lasso languages. We then show a dual adjunction between lasso automata and quotients of the free lasso semigroup with a recognising set, and as our main result we show that this dual adjunction restricts to one between $\Omega$-automata and quotients of the free Wilke algebra with a recognising set.

The paper proposes a new testing approach for Deep Neural Networks (DNN) using gradient-free optimization to find perturbation chains that successfully falsify the tested DNN, going beyond existing grid-based or combinatorial testing. Applying it to an image segmentation task of detecting railway tracks in images, we demonstrate that the approach can successfully identify weaknesses of the tested DNN regarding particular combinations of common perturbations (e.g., rain, fog, blur, noise) on specific clusters of test images.

eXplainable Artificial Intelligence (XAI) aims at providing understandable explanations of black box models. In this paper, we evaluate current XAI methods by scoring them based on ground truth simulations and sensitivity analysis. To this end, we used an Electric Arc Furnace (EAF) model to better understand the limits and robustness characteristics of XAI methods such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), as well as Averaged Local Effects (ALE) or Smooth Gradients (SG) in a highly topical setting. These XAI methods were applied to various types of black-box models and then scored based on their correctness compared to the ground-truth sensitivity of the data-generating processes using a novel scoring evaluation methodology over a range of simulated additive noise. The resulting evaluation shows that the capability of the Machine Learning (ML) models to capture the process accurately is, indeed, coupled with the correctness of the explainability of the underlying data-generating process. We furthermore show the differences between XAI methods in their ability to correctly predict the true sensitivity of the modeled industrial process., Comment: 11 pages, 3 figures, accepted at the ICML'24 Workshop ML4MS

Purpose: Federated training is often hindered by heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging multi-modal learning paradigms, where dataset harmonization including a uniform data representation and filtering options are of paramount importance. Methods: DICOM structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom. Building on this, we developed an open platform for data integration and interactive filtering capabilities that simplifies the process of assembling multi-modal datasets. Results: In this study, we extend our prior work by showing its applicability to more and divergent data types, as well as streamlining datasets for federated training within an established consortium of eight university hospitals in Germany. We prove its concurrent filtering ability by creating harmonized multi-modal datasets across all locations for predicting the outcome after minimally invasive heart valve replacement. The data includes DICOM data (i.e. computed tomography images, electrocardiography scans) as well as annotations (i.e. calcification segmentations, pointsets and pacemaker dependency), and metadata (i.e. prosthesis and diagnoses). Conclusion: Structured reports bridge the traditional gap between imaging systems and information systems. Utilizing the inherent DICOM reference system arbitrary data types can be queried concurrently to create meaningful cohorts for clinical studies. The graphical interface as well as example structured report templates will be made publicly available.

Federated learning (FL) is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often involve inherent challenges such as partially labeled datasets, where not all clients possess expert annotations of all labels of interest, leaving large portions of unlabeled data unused. In this study, we conduct the largest federated cardiac CT imaging analysis to date, focusing on partially labeled datasets ($n=8,124$) of Transcatheter Aortic Valve Implantation (TAVI) patients over eight hospital clients. Transformer architectures, which are the major building blocks of current foundation models, have shown superior performance when trained on larger cohorts than traditional CNNs. However, when trained on small task-specific labeled sample sizes, it is currently not feasible to exploit their underlying attention mechanism for improved performance. Therefore, we developed a two-stage semi-supervised learning strategy that distills knowledge from several task-specific CNNs (landmark detection and segmentation of calcification) into a single transformer model by utilizing large amounts of unlabeled data typically residing unused in hospitals to mitigate these issues. This method not only improves the predictive accuracy and generalizability of transformer-based architectures but also facilitates the simultaneous learning of all partial labels within a single transformer model across the federation. Additionally, we show that our transformer-based model extracts more meaningful features for further downstream tasks than the UNet-based one by only training the last layer to also solve segmentation of coronary arteries. We make the code and weights of the final model openly available, which can serve as a foundation model for further research in cardiac CT imaging.

This paper is concerned with the fundamental limits of nonlinear dynamical system learning from input-output traces. Specifically, we show that recurrent neural networks (RNNs) are capable of learning nonlinear systems that satisfy a Lipschitz property and forget past inputs fast enough in a metric-entropy optimal manner. As the sets of sequence-to-sequence maps realized by the dynamical systems we consider are significantly more massive than function classes generally considered in deep neural network approximation theory, a refined metric-entropy characterization is needed, namely in terms of order, type, and generalized dimension. We compute these quantities for the classes of exponentially-decaying and polynomially-decaying Lipschitz fading-memory systems and show that RNNs can achieve them.

This paper concerns diffraction-tomographic reconstruction of an object characterized by its scattering potential. We establish a rigorous generalization of the Fourier diffraction theorem in arbitrary dimension, giving a precise relation in the Fourier domain between measurements of the scattered wave and reconstructions of the scattering potential. With this theorem at hand, Fourier coverages for different experimental setups are investigated taking into account parameters such as object orientation, direction of incidence and frequency of illumination. Allowing for simultaneous and discontinuous variation of these parameters, a general filtered backpropagation formula is derived resulting in an explicit approximation of the scattering potential for a large class of experimental setups.

Purpose: To employ optimal control for the numerical design of CEST saturation pulses to maximize contrast and stability against $B_0$ inhomogeneities. Theory and Methods: We applied an optimal control framework for the design pulse shapes for CEST saturation pulse trains. The cost functional minimized both the pulse energy and the discrepancy between the corresponding CEST spectrum and the target spectrum based on a continuous RF pulse. The optimization is subject to hardware limitations. In measurements on a 7 T preclinical scanner, the optimal control pulses were compared to continuous-wave and Gaussian saturation methods. We conducted a comparison of the optimal control pulses were compared to with Gaussian, block pulse trains, and adiabatic spin-lock pulses. Results: The optimal control pulse train demonstrated saturation levels comparable to continuous-wave saturation and surpassed Gaussian saturation by up to 50 \% in phantom measurements. In phantom measurements at 3 T the optimized pulses not only showcased the highest CEST contrast, but also the highest stability against field inhomogeneities. In contrast, block pulse saturation resulted in severe artifacts. Dynamic Bloch-McConnell simulations were employed to identify the source of these artifacts, and underscore the $B_0$ robustness of the optimized pulses. Conclusion: In this work, it was shown that a substantial improvement in pulsed saturation CEST imaging can be achieved by using Optimal Control design principles. It is possible to overcome the sensitivity of saturation to B0 inhomogeneities while achieving CEST contrast close to continuous wave saturation., Comment: submitted to MRM (Magnetic Resonance in Medicine)

We propose a multi-objective global pattern search algorithm for the task of locating and quantifying damage in flexible mechanical structures. This is achieved by identifying eigenfrequencies and eigenmodes from measurements and matching them against the results of a finite element simulation model, which leads to a nonsmooth nonlinear bi-objective parameter estimation problem. A derivative-free optimization algorithm is required since the problem is nonsmooth and also because complex mechanical simulation models are often solved using commercial black-box software. Moreover, the entire set of non-dominated solutions is of interest to practitioners. Most solution approaches published to date are based on meta-heuristics such as genetic algorithms. The proposed multi-objective pattern-search algorithm provides a mathematically well-founded alternative. It features a novel sorting procedure that reduces the complexity in our context. Test runs on two experimental structures with multiple damage scenarios are used to validate the approach. The results demonstrate that the proposed algorithm yields accurate damage locations and requires moderate computational resources. From the engineer's perspective it represents a promising tool for structural health monitoring.

Characteristic shock effects in silica serve as a key indicator of historical impacts at geological sites. Despite this geological significance, atomistic details of structural transformations under high pressure and shock compression remain poorly understood. This ambiguity is evidenced by conflicting experimental observations of both amorphization and crystallization transitions. Utilizing a newly developed machine-learning interatomic potential, we examine the response of $\alpha$-quartz to shock compression with a peak pressure of 60 GPa over nano-second timescales. We initially observe amorphization before recrystallization into a d-NiAs-structured silica with disorder on the silicon sublattice, accompanied by the formation of domains with partial order of silicon. Investigating a variety of strain conditions enables us to identify the non-hydrostatic stress and strain states that allow the direct diffusionless formation of rosiaite-structured silica.

This second international workshop on explainable AI for the Arts (XAIxArts) brought together a community of researchers in HCI, Interaction Design, AI, explainable AI (XAI), and digital arts to explore the role of XAI for the Arts. Workshop held at the 16th ACM Conference on Creativity and Cognition (C&C 2024), Chicago, USA., Comment: Proceedings of The second international workshop on eXplainable AI for the Arts (XAIxArts)

We investigate the transport of intensity equation (TIE) and the transport of phase equation (TPE) for solving the phase retrieval problem. Both the TIE and the TPE are derived from the paraxial Helmholtz equation and relate phase information to the intensity. The TIE is usually favored since the TPE is nonlinear. The main contribution of this paper is a discussion of preferential use of either one of the two equations or potential benefits of a hybrid use. Moreover, we discuss the solution of the TPE with the method of characteristics and with viscosity methods. Both the TIE and the viscosity method are numerically implemented with finite element methods.

The discovery of 80 K superconductivity in bilayer La$_3$Ni$_2$O$_7$ at pressures greater than 14 GPa presents a unique opportunity to study a novel class of high-temperature superconductors. Therefore, other bilayer nickelate following the classical ($T^{4+}$) Ruddlesden-Popper (RP) series of Sr$_3$Ni$_2$O$_7$ would present an interesting new candidate. In this work, we study the stabilization of RP $n=2$ phase in Sr$_3$Ni$_{2-x}$Al$_{x}$O$_7$, via floating zone growth of crystals. With powder and single crystal XRD, we study the stability range of the RP-type phase. Our Thermogravimetric Analysis (TGA), X-ray photoelectron spectroscopy (XPS) and gas extraction studies reveal a remarkably high oxidation state of Ni$^{4+}$. The obtained black crystals are insulating in transport and show a magnetic transition around 12 K.

In recent years, interplanetary exploration has gained significant momentum, leading to a focus on the development of launch vehicles. However, the critical technology of edl mechanisms has not received the same level of attention and remains less mature and capable. To address this gap, we took advantage of the REXUS program to develop a pioneering edl mechanism. We propose an alternative to conventional, parachute based landing vehicles by utilizing autorotation. Our approach enables future additions such as steerability, controllability, and the possibility of a soft landing. To validate the technique and our specific implementation, we conducted a sounding rocket experiment on REXUS29. The systems design is outlined with relevant design decisions and constraints, covering software, mechanics, electronics and control systems. Furthermore, an emphasis will also be the organization and setup of the team entirely made up and executed by students. The flight results on REXUS itself are presented, including the most important outcomes and possible reasons for mission failure. We have not archived an autorotation based landing, but provide a reliable way of building and operating such vehicles. Ultimately, future works and possibilities for improvements are outlined. The research presented in this paper highlights the need for continued exploration and development of edl mechanisms for future interplanetary missions. By discussing our results, we hope to inspire further research in this area and contribute to the advancement of space exploration technology., Comment: 12 pages, 9 figures

Generative AI models are capable of performing a wide range of tasks that traditionally require creativity and human understanding. They learn patterns from existing data during training and can subsequently generate new content such as texts, images, and music that follow these patterns. Due to their versatility and generally high-quality results, they, on the one hand, represent an opportunity for digitalization. On the other hand, the use of generative AI models introduces novel IT security risks that need to be considered for a comprehensive analysis of the threat landscape in relation to IT security. In response to this risk potential, companies or authorities using them should conduct an individual risk analysis before integrating generative AI into their workflows. The same applies to developers and operators, as many risks in the context of generative AI have to be taken into account at the time of development or can only be influenced by the operating company. Based on this, existing security measures can be adjusted, and additional measures can be taken., Comment: 33 pages, 3 figures

PolySAT is a word-level decision procedure supporting bit-precise SMT reasoning over polynomial arithmetic with large bit-vector operations. The PolySAT calculus extends conflict-driven clause learning modulo theories with two key components: (i) a bit-vector plugin to the equality graph, and (ii) a theory solver for bit-vector arithmetic with non-linear polynomials. PolySAT implements dedicated procedures to extract bit-vector intervals from polynomial inequalities. For the purpose of conflict analysis and resolution, PolySAT comes with on-demand lemma generation over non-linear bit-vector arithmetic. PolySAT is integrated into the SMT solver Z3 and has potential applications in model checking and smart contract verification where bit-blasting techniques on multipliers/divisions do not scale., Comment: Submitted to FMCAD 2024, https://fmcad.org/FMCAD24/

We address the problem of uncertainty quantification for graph-structured data, or, more specifically, the problem to quantify the predictive uncertainty in (semi-supervised) node classification. Key questions in this regard concern the distinction between two different types of uncertainty, aleatoric and epistemic, and how to support uncertainty quantification by leveraging the structural information provided by the graph topology. Challenging assumptions and postulates of state-of-the-art methods, we propose a novel approach that represents (epistemic) uncertainty in terms of mixtures of Dirichlet distributions and refers to the established principle of linear opinion pooling for propagating information between neighbored nodes in the graph. The effectiveness of this approach is demonstrated in a series of experiments on a variety of graph-structured datasets., Comment: Accepted for the 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024). Implementation available at https://github.com/Cortys/gpn-extensions

We investigate the geometry of the magnetic field towards the Radcliffe Wave, a coherent 3-kpc-long part of the nearby Local Arm recently discovered via three-dimensional dust mapping. We use archival stellar polarization in the optical and new measurements in the near-infrared to trace the magnetic field as projected on the plane of the sky. Our new observations cover the portion of the structure that is closest to the Sun, between Galactic longitudes of 122$^\circ$ and 188$^\circ$. The polarization angles of stars immediately background to the Radcliffe Wave appear to be aligned with the structure as projected on the plane of the sky. The observed magnetic field configuration is inclined with respect to the Galactic disk at an angle of 18$^\circ$. This departure from a geometry parallel to the plane of the Galaxy is contrary to previous constraints from more distant stars and polarized dust emission. We confirm that the polarization angle of stars at larger distances shows a mean orientation parallel to the Galactic disk. We discuss implications of the observed morphology of the magnetic field for models of the large-scale Galactic magnetic field, as well as formation scenarios for the Radcliffe Wave itself., Comment: Submitted to A&A

A major hurdle for building a large-scale quantum computer is to scale up the number of qubits while maintaining connectivity between them. In trapped-ion devices, this connectivity can be provided by physically moving subregisters consisting of a few ions across the processor. The topology of the connectivity is given by the layout of the ion trap where one-dimensional and two-dimensional arrangements are possible. Here, we focus on an architecture based on a rectangular two-dimensional lattice, where each lattice site contains a subregister with a linear string of ions. We refer to this architecture as the Quantum Spring Array (QSA). Subregisters placed in neighboring lattice sites can be coupled by bringing the respective ion strings close to each other while avoiding merging them into a single trapping potential. Control of the separation of subregisters along one axis of the lattice, known as the axial direction, uses quasi-static voltages, while the second axis, the radial, requires control of radio frequency signals. In this work, we investigate key elements of the 2D lattice quantum computation architecture along both axes: We show that the coupling rate between neighboring lattice sites increases with the number of ions per site and the motion of the coupled system can be resilient to noise. The coherence of the coupling is assessed, and an entangled state of qubits in separate trapping regions along the radial axis is demonstrated. Moreover, we demonstrate control over radio frequency signals to adjust radial separation between strings, and thus tune their coupling rate. We further map the 2D lattice architecture to code primitives for fault-tolerant quantum error correction, providing a step towards a quantum processor architecture that is optimized for large-scale fault-tolerant operation., Comment: 23 pages, 19 figures (15 in main text, 4 in appendices)

This paper analyzes Large Language Models (LLMs) with regard to their programming exercise generation capabilities. Through a survey study, we defined the state of the art, extracted their strengths and weaknesses and finally proposed an evaluation matrix, helping researchers and educators to decide which LLM is the best fitting for the programming exercise generation use case. We also found that multiple LLMs are capable of producing useful programming exercises. Nevertheless, there exist challenges like the ease with which LLMs might solve exercises generated by LLMs. This paper contributes to the ongoing discourse on the integration of LLMs in education., Comment: 5 pages, 0 figures, CSEE&T 2024

Image quality assessment (IQA) is standard practice in the development stage of novel machine learning algorithms that operate on images. The most commonly used IQA measures have been developed and tested for natural images, but not in the medical setting. Reported inconsistencies arising in medical images are not surprising, as they have different properties than natural images. In this study, we test the applicability of common IQA measures for medical image data by comparing their assessment to manually rated chest X-ray (5 experts) and photoacoustic image data (2 experts). Moreover, we include supplementary studies on grayscale natural images and accelerated brain MRI data. The results of all experiments show a similar outcome in line with previous findings for medical imaging: PSNR and SSIM in the default setting are in the lower range of the result list and HaarPSI outperforms the other tested measures in the overall performance. Also among the top performers in our medical experiments are the full reference measures FSIM, GMSD, LPIPS and MS-SSIM. Generally, the results on natural images yield considerably higher correlations, suggesting that the additional employment of tailored IQA measures for medical imaging algorithms is needed.

Image quality assessment (IQA) is not just indispensable in clinical practice to ensure high standards, but also in the development stage of novel algorithms that operate on medical images with reference data. This paper provides a structured and comprehensive collection of examples where the two most common full reference (FR) image quality measures prove to be unsuitable for the assessment of novel algorithms using different kinds of medical images, including real-world MRI, CT, OCT, X-Ray, digital pathology and photoacoustic imaging data. In particular, the FR-IQA measures PSNR and SSIM are known and tested for working successfully in many natural imaging tasks, but discrepancies in medical scenarios have been noted in the literature. Inconsistencies arising in medical images are not surprising, as they have very different properties than natural images which have not been targeted nor tested in the development of the mentioned measures, and therefore might imply wrong judgement of novel methods for medical images. Therefore, improvement is urgently needed in particular in this era of AI to increase explainability, reproducibility and generalizability in machine learning for medical imaging and beyond. On top of the pitfalls we will provide ideas for future research as well as suggesting guidelines for the usage of FR-IQA measures applied to medical images.

Interfacial engineering has fueled recent development of p-i-n perovskite solar cells (PSCs), with self-assembled monolayer-based hole-transport layers (SAM-HTLs) enabling almost lossless contacts for solution-processed PSCs, resulting in the highest achieved power conversion efficiency (PCE) to date. Substrate interfaces are particularly crucial for the growth and quality of co-evaporated PSCs. However, adoption of SAM-HTLs for co-evaporated perovskite absorbers is complicated by the underexplored interaction of such perovskites with phosphonic acid functional groups. In this work, we highlight how exposed phosphonic acid functional groups impact the initial phase and final bulk crystal structures of co-evaporated perovskites and their resultant PCE. The explored surface interaction is mediated by hydrogen bonding with interfacial iodine, leading to increased formamidinium iodide adsorption, persistent changes in perovskite structure, and stabilization of bulk {\alpha}-FAPbI3, hypothesized as being due to kinetic trapping. Our results highlight the potential of exploiting substrates to increase control of co-evaporated perovskite growth.

Artificial Intelligence (AI) approaches have been incorporated into modern learning environments and software engineering (SE) courses and curricula for several years. However, with the significant rise in popularity of large language models (LLMs) in general, and OpenAI's LLM-powered chatbot ChatGPT in particular in the last year, educators are faced with rapidly changing classroom environments and disrupted teaching principles. Examples range from programming assignment solutions that are fully generated via ChatGPT, to various forms of cheating during exams. However, despite these negative aspects and emerging challenges, AI tools in general, and LLM applications in particular, can also provide significant opportunities in a wide variety of SE courses, supporting both students and educators in meaningful ways. In this early research paper, we present preliminary results of a systematic analysis of current trends in the area of AI, and how they can be integrated into university-level SE curricula, guidelines, and approaches to support both instructors and learners. We collected both teaching and research papers and analyzed their potential usage in SE education, using the ACM Computer Science Curriculum Guidelines CS2023. As an initial outcome, we discuss a series of opportunities for AI applications and further research areas., Comment: Accepted for publication at the 36th Conference on Software Engineering Education and Training (CSEE&T)

The average revenue, or market value, of wind and solar energy tends to fall with increasing market shares, as is now evident across European electricity markets. At the same time, these markets have become more interconnected. In this paper, we empirically study the multiple cross-border effects on the value of renewable energy: on one hand, interconnection is a flexibility resource that allows to export energy when it is locally abundant, benefitting renewables. On the other hand, wind and solar radiation are correlated across space, so neighboring supply adds to the local one to depress domestic prices. We estimate both effects, using spatial panel regression on electricity market data from 2015 to 2023 from 30 European bidding zones. We find that domestic wind and solar value is not only depressed by domestic, but also by neighboring renewables expansion. The better interconnected a market is, the smaller the effect of domestic but the larger the effect of neighboring renewables. While wind value is stabilized by interconnection, solar value is not. If wind market share increases both at home and in neighboring markets by one percentage point, the value factor of wind energy is reduced by just above 1 percentage points. For solar, this number is almost 4 percentage points.

The study of 5G base station antenna array performance for self-interference reduction is derived. The line of sight signal channel model and Rayleigh channel model are developed. The relevant calculations for channel capacities are shown. This is the pre-material for this study. More results and conclusions will be presented soon., Comment: 4 pages short paper

This paper introduces a novel approach to experimentally characterize effective human skin permittivity at sub-Terahertz (sub-THz) frequencies, specifically from $140$~to $210$~GHz, utilizing a quasi-optical measurement system. To ensure accurate measurement of the reflection coefficients of human skin, a planar, rigid, and thick reference plate with a low-loss dielectric is utilized to flatten the human skin surface. A permittivity characterization method is proposed to reduce permittivity estimation deviations resulting from the pressure effects on the phase displacements of skins under the measurements but also to ensure repeatability of the measurement. In practical permittivity characterizations, the complex permittivities of the finger, palm, and arm of seven volunteers show small standard deviations for the repeated measurements, respectively, while those show significant variations across different regions of the skins and for different persons. The proposed measurement system holds significant potential for future skin permittivity estimation in sub-THz bands, facilitating further studies on human-electromagnetic-wave interactions based on the measured permittivity values., Comment: 13 pages for Journal paper