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Reversible ferroelectric domain wall movements beyond the 10 nm range associated with Rayleigh behavior are usually restricted to specific defect-engineered systems. Here, we demonstrate that such long-range movements naturally occur in the improper ferroelectric ErMnO3 during electric-field-cycling. We study the electric-field-driven motion of domain walls, showing that they readily return to their initial position after having travelled distances exceeding 250 nm. By applying switching spectroscopy band-excitation piezoresponse force microscopy, we track the domain wall movement with nanometric spatial precision and analyze the local switching behavior. Phase field simulations show that the reversible long-range motion is intrinsic to the hexagonal manganites, linking it to their improper ferroelectricity and topologically protected structural vortex lines, which serve as anchor point for the ferroelectric domain walls. Our results give new insight into the local dynamics of domain walls in improper ferroelectrics and demonstrate the possibility to reversibly displace domain walls over much larger distances than commonly expected for ferroelectric systems in their pristine state, ensuring predictable device behavior for applications such as tunable capacitors or sensors

Background. In the last decades, several life science resources have structured data using the same framework and made these accessible using the same query language to facilitate interoperability. Knowledge graphs have seen increased adoption in bioinformatics due to their advantages for representing data in a generic graph format. For example, yummydata.org catalogs more than 60 knowledge graphs accessible through SPARQL, a technical query language. Although SPARQL allows powerful, expressive queries, even across physically distributed knowledge graphs, formulating such queries is a challenge for most users. Therefore, to guide users in retrieving the relevant data, many of these resources provide representative examples. These examples can also be an important source of information for machine learning, if a sufficiently large number of examples are provided and published in a common, machine-readable and standardized format across different resources. Findings. We introduce a large collection of human-written natural language questions and their corresponding SPARQL queries over federated bioinformatics knowledge graphs (KGs) collected for several years across different research groups at the SIB Swiss Institute of Bioinformatics. The collection comprises more than 1000 example questions and queries, including 65 federated queries. We propose a methodology to uniformly represent the examples with minimal metadata, based on existing standards. Furthermore, we introduce an extensive set of open-source applications, including query graph visualizations and smart query editors, easily reusable by KG maintainers who adopt the proposed methodology. Conclusions. We encourage the community to adopt and extend the proposed methodology, towards richer KG metadata and improved Semantic Web services.

This paper presents the fabrication and characterization of superconducting qubit components from titanium nitride (TiN) and aluminum nitride (AlN) layers to create Josephson junctions and superconducting resonators in an all-nitride architecture. Our methodology comprises a complete process flow for the fabrication of TiN/AlN/TiN junctions, characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), ellipsometry and DC electrical measurements. We evaluated the sputtering rates of AlN under varied conditions, the critical temperatures of TiN thin films for different sputtering environments, and the internal quality factors of TiN resonators in the few-GHz regime, fabricated from these films. Overall, this offered insights into the material properties critical to qubit performance. Measurements of the dependence of the critical current of the TiN / AlN / TiN junctions yielded values ranging from 150 ${\mu}$A to 2 ${\mu}$A, for AlN barrier thicknesses up to ca. 5 nm, respectively. Our findings demonstrate advances in the fabrication of nitride-based superconducting qubit components, which may find applications in quantum computing technologies based on novel materials.

Superconducting qubits are a promising platform for large-scale quantum computing. Besides the Josephson junction, most parts of a superconducting qubit are made of planar, patterned superconducting thin films. In the past, most qubit architectures have relied on niobium (Nb) as the material of choice for the superconducting layer. However, there is also a variety of alternative materials with potentially less losses, which may thereby result in increased qubit performance. One such material is tantalum (Ta), for which high-performance qubit components have already been demonstrated. In this study, we report the sputter-deposition of Ta thin films directly on heated and unheated silicon (Si) substrates as well as onto different, nanometer-thin seed layers from tantalum nitride (TaN), titanium nitride (TiN) or aluminum nitride (AlN) that were deposited first. The thin films are characterized in terms of surface morphology, crystal structure, phase composition, critical temperature, residual resistance ratio (RRR) and RF-performance. We obtain thin films indicative of pure alpha-Ta for high temperature (600{\deg}C) sputtering directly on silicon and for Ta deposited on TaN or TiN seed layers. Coplanar waveguide (CPW) resonator measurements show that the Ta deposited directly on the heated silicon substrate performs best with internal quality factors $Q_i$ reaching 1 x $10^6$ in the single-photon regime, measured at $T=100 {\space \rm mK}$.

The teaching of general relativity at the secondary school and lower undergraduate university levels is necessarily based on approaches with a restricted use of mathematics. An important aspect of teaching general relativity at this level are suitable learner activities. While a substantial number of such activities has been reported for studying the non-Euclidean geometry of curved surfaces, there are far fewer reports of activities that let learners actually study spacetimes and infer physical phenomena from their geometry. In this article we report on the digital learning environment V-SeMo that brings sector models (Zahn and Kraus 2014, arXiv:1405.0323) into an interactive web application. V-SeMo lets learners explore relativistic spacetimes by constructing geodesics and assessing curvature. We describe the didactic design and the user interface of V-SeMo, discuss the extended possibilities of virtual sector models compared to the paper models described previously, and present an activity on light deflection near neutron stars by way of example. We further report on an evaluation carried out with secondary school students. Results indicate a high learning effectiveness in both the V-SeMo-based and the paper-based versions of a teaching unit on relativistic light deflection. The teaching materials presented in this article are available online for teaching purposes at https://www.spacetimetravel.org ., Comment: 24 pages, 13 figures

We study monolayer WSe2 using ultrafast electron diffraction. We introduce an approach to quantitatively extract atomic-site-specific information, providing an element-specific view of incoherent atomic vibrations following femtosecond excitation. Via differences between W and Se vibrations, we identify stages in the nonthermal evolution of the lattice. Combined with a calculated phonon dispersion, this element specificity enables us to identify a long-lasting overpopulation of specific optical phonons, and to interpret the stages as energy transfer processes between specific phonon groups. These results demonstrate the appeal of resolving element-specific vibrational information in the ultrafast time domain., Comment: 13 pages, 3 figures

Ferroelectric domain walls (DWs) are promising structures for assembling future nano-electronic circuit elements on a larger scale, since reporting domain wall currents of up to 1 mA per single DW. One key requirement hereto is their reproducible manufacturing by gaining preparative control over domain size and domain wall conductivity (DWC). To date, most works on DWC have focused on exploring the fundamental electrical properties of individual DWs within single shot experiments, with emphasis on quantifying the origins for DWC. Very few reports exist when it comes to compare the DWC properties between two separate DWs, and literally nothing exists where issues of reproducibility in DWC devices have been addressed. To fill this gap while facing the challenge of finding guidelines achieving predictable DWC performance, we report on a procedure that allows us to reproducibly prepare single hexagonal domains of a predefined diameter into uniaxial ferroelectric (FE) lithium niobate (LN) single crystals of 200 and 300 micrometers thickness, respectively. We show that the domain diameter can be controlled with an error of a few percent. As-grown DWs are then subjected to a standard procedure of current-controlled high-voltage DWC enhancement, repetitively reaching a DWC increase of 6 orders of magnitude. While all resulting DWs show significantly enhanced DWC values, subtle features in their individual current-voltage (I-V) characteristics hint towards different 3D shapes into the bulk, with variations probably reflecting local heterogeneities by defects, DW pinning, and surface-near DW inclination, which seem to have a larger impact than expected., Comment: 12 pages, 7 figures, with appended supplement (9 pages, 7 figures)

Anomaly detection in distributed systems such as High-Performance Computing (HPC) clusters is vital for early fault detection, performance optimisation, security monitoring, reliability in general but also operational insights. Deep Neural Networks have seen successful use in detecting long-term anomalies in multidimensional data, originating for instance from industrial or medical systems, or weather prediction. A downside of such methods is that they require a static input size, or lose data through cropping, sampling, or other dimensionality reduction methods, making deployment on systems with variability on monitored data channels, such as computing clusters difficult. To address these problems, we present DeepHYDRA (Deep Hybrid DBSCAN/Reduction-Based Anomaly Detection) which combines DBSCAN and learning-based anomaly detection. DBSCAN clustering is used to find point anomalies in time-series data, mitigating the risk of missing outliers through loss of information when reducing input data to a fixed number of channels. A deep learning-based time-series anomaly detection method is then applied to the reduced data in order to identify long-term outliers. This hybrid approach reduces the chances of missing anomalies that might be made indistinguishable from normal data by the reduction process, and likewise enables the algorithm to be scalable and tolerate partial system failures while retaining its detection capabilities. Using a subset of the well-known SMD dataset family, a modified variant of the Eclipse dataset, as well as an in-house dataset with a large variability in active data channels, made publicly available with this work, we furthermore analyse computational intensity, memory footprint, and activation counts. DeepHYDRA is shown to reliably detect different types of anomalies in both large and complex datasets.

While recent advances in deep learning have demonstrated its transformative potential, its adoption for real-world manufacturing applications remains limited. We present an Explanation User Interface (XUI) for a state-of-the-art deep learning-based robot program optimizer which provides both naive and expert users with different user experiences depending on their skill level, as well as Explainable AI (XAI) features to facilitate the application of deep learning methods in real-world applications. To evaluate the impact of the XUI on task performance, user satisfaction and cognitive load, we present the results of a preliminary user survey and propose a study design for a large-scale follow-up study., Comment: 6 pages, 4 figures, accepted at the 2024 CIRP International Conference on Manufacturing Systems (CMS)

Optical manipulation of magnetism holds promise for future ultrafast spintronics, especially with lanthanides and their huge, localized 4f magnetic moments. These moments interact indirectly via the conduction electrons (RKKY exchange), influenced by interatomic orbital overlap, and the conduction electron susceptibility. Here, we study this influence in a series of 4f antiferromagnets, GdT2Si2 (T=Co, Rh, Ir), using ultrafast resonant X-ray diffraction. We observe a twofold increase in ultrafast angular momentum transfer between the materials, originating from modifications in the conduction electron susceptibility, as confirmed by first-principles calculations.

We present the development of multifunctional blue-emission organic light-emitting diodes (OLEDs) using TADF-exciplex materials. These OLEDs exhibit sensitivity to external stimuli and achieve a maximum external quantum efficiency (EQE) of 11.6 % through partly liquid processing. This technique allows for large-scale production on arbitrary geometries. The potential multifunctionality of the devices arises from their response to low external magnetic fields (up to 100 mT) with an efficiency up to 2.5 % for magnetoconductance, while maximum magneto-electroluminescence effects of 4.1 % were detected. We investigated novel aspects, including the utilization of two organic materials without further doping and the investigation of the impact of 2,2',2''-(1,3,5-Benzinetriyl)-tris(1phenyl-1-H-benzimidazole) (TPBi) processing in liquid and vapor form. The insights gained provide a fundamental understanding regarding the applicability of exciplex (EX) materials for fully solution-processed OLEDs through a deliberate omission of doping. Our work represents a significant advancement on the path towards multifunctional OLED technology, with potential applications in cost-efficient, scalable organic full-color displays and advanced sensing system, Comment: 25 pages manuscript, 3 pages supplementary information

We characterize the connectivity structure of feed-forward, deep neural networks (DNNs) using network motif theory. To address whether a particular motif distribution is characteristic of the training task, or function of the DNN, we compare the connectivity structure of 350 DNNs trained to simulate a bio-mechanical flight control system with different randomly initialized parameters. We develop and implement algorithms for counting second- and third-order motifs and calculate their significance using their Z-score. The DNNs are trained to solve the inverse problem of the flight dynamics model in Bustamante, et al. (2022) (i.e., predict the controls necessary for controlled flight from the initial and final state-space inputs) and are sparsified through an iterative pruning and retraining algorithm Zahn, et al. (2022). We show that, despite random initialization of network parameters, enforced sparsity causes DNNs to converge to similar connectivity patterns as characterized by their motif distributions. The results suggest how neural network function can be encoded in motif distributions, suggesting a variety of experiments for informing function and control.

Conventional and recently developed approaches for estimating turbulent scalar fluxes under stable conditions are evaluated. The focus is on methods that do not require fast scalar sensors such as the relaxed eddy accumulation (REA) approach, the disjunct eddy-covariance (DEC) approach, and a novel mixing length parametrization labelled as A22. Using high-frequency measurements collected from two contrasting sites (Utqiagvik, Alaska and Wendell, Idaho "during winter"), it is shown that the REA and A22 models outperform the conventional Monin-Obukhov Similarity Theory (MOST) utilized in Earth System Models. With slow trace gas sensors used in disjunct eddy-covariance (DEC) approaches and the more complex signal filtering associated with REA devices (here simulated using filtered signals from fast-response sensors), A22 outperforms REA and DEC in predicting the observed unfiltered (total) eddy-covariance (EC) fluxes. However, REA and DEC can still capture the observed filtered EC fluxes computed with the filtered scalar signal. This finding motivates the development of a correction, blending the REA and DEC methods, for the underestimated net averaged fluxes to incorporate the effect of sensor filtering. The only needed parameter for this correction is the mean velocity at the instrument height, a surrogate of the advective timescale., Comment: 14 pages, 8 figures, 2 tables

Because of their natural one-dimensional (1D) structure combined with intricate chiral variations, carbon nanotubes (CNTs) exhibit various exceptional physical properties, such as ultrahigh electrical and thermal conductivity, exceptional mechanical strength, and chirality-dependent metallicity. These properties make CNTs highly promising for diverse applications, including field-effect transistors, sensors, photodetectors, and thermoelectric devices. While CNTs excel individually at the nanoscale, their 1D and chiral nature can be lost on a macroscopic scale when they are randomly assembled. Therefore, the alignment and organization of CNTs in macroscopic structures is crucial for harnessing their full potential. In this review, we explore recent advancements in understanding CNT alignment mechanisms, improving CNT aligning methods, and demonstrating macroscopically 1D properties of ordered CNT assemblies. We also focus on a newly discovered class of CNT architectures, combining CNT alignment and twisting mechanisms to create artificial radial and chiral CNT films at wafer scales. Finally, we summarize recent developments related to aligned and chiral CNT films in optoelectronics, highlighting their unique roles in solar cells, thermal emitters, and optical modulators.

We show that the stress tensor of a real scalar quantum field on Reissner-Nordstr{\"o}m-de Sitter spacetime exhibits correlations over macroscopic distances near the Cauchy horizon. These diverge as the Cauchy horizon is approached and are universal, i.e., state-independent. This signals a breakdown of the semi-classical approximation near the Cauchy horizon. We also investigate the effect of turning on a charge of the scalar field and consider the correlation of the stress tensor between the two poles of the Cauchy horizon of Kerr-de Sitter spacetime., Comment: 5 pages, 4 figures, version as published in PRD

The purpose of this study was to examine the accuracy of function-based decisions made in consideration of scores from the Intervention Selection Profile (ISP)-Function, a tool founded upon direct behavior rating methodology. The ISP-Function is designed to be a brief measure, given the need for efficient and low resource assessments in schools. Data from a previous investigation were used to create data reports for each of 34 elementary students with a history of exhibiting disruptive behavior in the classroom. The first report summarized ISP-Function data that the student's classroom teacher collected. The second report was representative of more typical functional behavior assessment (FBA), summarizing data collected via a functional assessment interview with the teacher, as well as systematic direct observation data. Nine school psychologists conducted blind reviews of these reports and derived decisions regarding the function of each student's behavior (e.g., adult attention or escape/avoidance). Gwet's agreement coefficients were statistically significant and suggested Fair to Almost Perfect correspondence between ISP-Function and FBA reports. Limitations and implications for practice are discussed herein. [This paper was published in "School Psychology."]

Extraordinary physical properties arise at polar interfaces in oxide materials, including the emergence of two-dimensional electron gases, sheet-superconductivity, and multiferroicity. A special type of polar interface are ferroelectric domain walls, where electronic reconstruction phenomena can be driven by bound charges. Great progress has been achieved in the characterization of such domain walls and, over the last decade, their potential for next-generation nanotechnology has become clear. Established tomography techniques, however, are either destructive or offer insufficient spatial resolution, creating a pressing demand for 3D imaging compatible with future fabrication processes. Here, we demonstrate non-destructive tomographic imaging of ferroelectric domain walls using secondary electrons. Utilizing conventional scanning electron microscopy (SEM), we reconstruct the position, orientation, and charge state of hidden domain walls at distances up to several hundreds of nanometers away from the surface. A mathematical model is derived that links the SEM intensity variations at the surface to the local domain wall properties, enabling non-destructive tomography with good noise tolerance on the timescale of seconds. Our SEM-based approach facilitates high-throughput screening of materials with functional domain walls and domain-wall-based devices, which is essential for monitoring during the production of device architectures and quality control in real-time.

Ferroelectric domain wall (DW) conductivity (DWC) can be attributed to two separate mechanisms: (a) the injection/ejection of charge carriers across the Schottky barrier formed at the (metal-) electrode-DW junction and (b) the transport of those charge carriers along the DW. Current-voltage (IU) characteristics, recorded at variable temperatures from LiNbO$_3$ (LNO) DWs, are clearly able to differentiate between these two contributions. Practically, they allow us here to directly quantify the physical parameters relevant for the two mechanisms (a) and (b) mentioned above. These are, e.g., the resistance of the DW, the saturation current, the ideality factor, and the Schottky barrier height of the electrode/DW junction. Furthermore, the activation energies needed to initiate the thermally-activated electronic transport along the DWs, can be extracted. In addition, we show that electronic transport along LiNbO$_3$ DWs can be elegantly viewed and interpreted in an adapted semiconductor picture based on a double-diode/double-resistor equivalent circuit model, the R2D2 model. Finally, our R2D2 model was checked for its universality by fitting the DWC data not only to z-cut LNO bulk DWs, but equally to z-cut thin-film LNO DWs, and DWC from x-cut DWs as reported in literature., Comment: 10 pages, 7 figures; with included supporting information (6 pages, 4 figures)

Electromagnetic and gravitational perturbations on Kerr spacetime can be reconstructed from solutions to the Teukolsky equations. We study the canonical quantization of solutions to these equations for any integer spin. Our quantization scheme involves the analysis of the Hertz potential and one of the Newman-Penrose scalars, which must be related via the Teukolsky-Starobinsky identities. We show that the canonical commutation relations between the fields can be implemented if and only if the Teukolsky-Starobinsky constants are positive, which is the case both for gravitational perturbations and Maxwell fields. We also obtain the Hadamard parametrix of the Teukolsky equation, which is the basic ingredient for a local and covariant renormalization scheme for non-linear observables. We also discuss the relation of the canonical energy of Teukolsky fields to that of gravitational perturbations.

Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the nanoscale. In electronic and quantum materials, this new capability gives access to, for example, emergent chiral structures and symmetry-breaking distortions that underpin functional properties. Quantifying nanoscale structural features with statistical significance, however, is complicated by the subtleties of dynamic diffraction and coexisting contrast mechanisms, which often results in low signal-to-noise and the superposition of multiple signals that are challenging to deconvolute. Here we apply scanning electron diffraction to explore local polar distortions in the uniaxial ferroelectric Er(Mn,Ti)O$_3$. Using a custom-designed convolutional autoencoder with bespoke regularization, we demonstrate that subtle variations in the scattering signatures of ferroelectric domains, domain walls, and vortex textures can readily be disentangled with statistical significance and separated from extrinsic contributions due to, e.g., variations in specimen thickness or bending. The work demonstrates a pathway to quantitatively measure symmetry-breaking distortions across large areas, mapping structural changes at interfaces and topological structures with nanoscale spatial resolution.