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Spin noise spectroscopy is a technique to measure magnetization fluctuations, a subject of increasing relevance in ultrafast spintronics. We investigate numerically the equilibrium spin noise of ferro- and antiferromagnets within an atomistic spin model. The aim is to predict the possible outcomes of ultrafast spin-noise spectroscopy measurements and demonstrate what relevant information can be extracted. Specifically, we show how this method can be used to determine phase transitions, frequencies of magnon modes and correlation times.

Catalytic microswimmers typically swim close to walls due to hydrodynamic and/or phoretic effects. The walls in turn are known to affect their propulsion, making it difficult to single out the contributions that stem from particle-based catalytic propulsion only, thereby preventing an understanding of the propulsion mechanism. Here, we use acoustic tweezers to lift catalytically active Janus spheres away from the wall to study their motion in bulk and when approaching a wall. Mean-squared displacement analysis shows that diffusion constants at different heights match with Fax\'en's prediction for the near-wall hydrodynamic mobility. Both particles close to a substrate and in bulk show a decrease in velocity with increasing salt concentration, suggesting that the dominant factor for the decrease in speed is a reduction of the swimmer-based propulsion. The velocity-height profile follows a hydrodynamic scaling relation as well, implying a coupling between the wall and the swimming speed. The observed speed reduction upon addition of salt matches expectations from a electrokinetic theory, except for experiments in 0.1 wt% hydrogen peroxide in bulk, which could indicate contributions from a different propulsion mechanism. Our results help with the understanding of ionic effects on microswimmers in 3D and point to a coupling between the wall and the particle that affects its self-propulsion speed.

Rainbow Deep Q-Network (DQN) demonstrated combining multiple independent enhancements could significantly boost a reinforcement learning (RL) agent's performance. In this paper, we present "Beyond The Rainbow" (BTR), a novel algorithm that integrates six improvements from across the RL literature to Rainbow DQN, establishing a new state-of-the-art for RL using a desktop PC, with a human-normalized interquartile mean (IQM) of 7.4 on atari-60. Beyond Atari, we demonstrate BTR's capability to handle complex 3D games, successfully training agents to play Super Mario Galaxy, Mario Kart, and Mortal Kombat with minimal algorithmic changes. Designing BTR with computational efficiency in mind, agents can be trained using a desktop PC on 200 million Atari frames within 12 hours. Additionally, we conduct detailed ablation studies of each component, analzying the performance and impact using numerous measures., Comment: 9 main pages, 26 total. Currently under review at ICLR

Single-molecule junctions - nanoscale systems where a molecule is connected to metallic electrodes - offer a unique platform for studying charge, spin and energy transport in non-equilibrium many-body quantum systems, with few parallels in other areas of condensed matter physics. Over the past decades, these systems have revealed a wide range of remarkable quantum phenomena, including quantum interference, non-equilibrium spin-crossover, diode-like behavior, or chiral-induced spin selectivity, among many others. To develop a detailed understanding, it turned out essential to have available ab initio-based tools for accurately describing quantum transport in such systems. They need to be capable of capturing the intricate electronic structure of molecules, sometimes in the presence of electron-electron or electron-phonon interactions, in out-of-equilibrium environments. Such tools are indispensable also for experimentally observed phenomena explained in terms of parametrized tight-binding models for the quantum transport problem. While FHI-aims also offers specialized transport routines, e.g. for chemically functionalized nanotubes or nanotube networks, our focus in this section is on the AITRANSS package designed for simulations of single-molecule transport. AITRANSS is an independent post-processing tool that combined with FHI-aims enables the calculation of electronic transport properties, as well as atom-projected density of states, spin properties and the simulation of scanning tunneling microscope images in molecular junctions. Pilot versions of the code extend some of these capabilities to non-linear transport in the applied bias, with plans to include these features in future releases of the package., Comment: 7 pages, 2 figures. This is a contribution/chapter to the upcoming "Roadmap on Advancements of the FHI-aims Software Package"

Progressive dimensionality reduction algorithms allow for visually investigating intermediate results, especially for large data sets. While different algorithms exist that progressively increase the number of data points, we propose an algorithm that allows for increasing the number of dimensions. Especially in spatio-temporal data, where each spatial location can be seen as one data point and each time step as one dimension, the data is often stored in a format that supports quick access to the individual dimensions of all points. Therefore, we propose Progressive Glimmer, a progressive multidimensional scaling (MDS) algorithm. We adapt the Glimmer algorithm to support progressive updates for changes in the data's dimensionality. We evaluate Progressive Glimmer's embedding quality and runtime. We observe that the algorithm provides more stable results, leading to visually consistent results for progressive rendering and making the approach applicable to streaming data. We show the applicability of our approach to spatio-temporal simulation ensemble data where we add the individual ensemble members progressively., Comment: 6 pages, 5 figures, presented at 2024 IEEE VIS Workshop on Progressive Data Analysis and Visualization (PDAV)

The Newton line and the associated theorems by Newton and Gauss for tetragons and quadrilaterals are closely linked to some other theorems of Euclidean geometry: a theorem by Bocher on the existence of a nine-point conic of a quadrangle, a theorem by Shatunov and Tokarev, and a theorem by Anne. This paper examines to which extent all these theorems can be transferred to other metric planes, in particular the elliptic and hyperbolic planes., Comment: 22 pages / 16 figures

Current research provides methods to communicate uncertainty and adapts classical algorithms of the visualization pipeline to take the uncertainty into account. Various existing visualization frameworks include methods to present uncertain data but do not offer transformation techniques tailored to uncertain data. Therefore, we propose a software package for uncertainty-aware data analysis in Python (UADAPy) offering methods for uncertain data along the visualization pipeline. We aim to provide a platform that is the foundation for further integration of uncertainty algorithms and visualizations. It provides common utility functionality to support research in uncertainty-aware visualization algorithms and makes state-of-the-art research results accessible to the end user. The project is available at https://github.com/UniStuttgart-VISUS/uadapy.

Sensitivity analyses of simulation ensembles determine how simulation parameters influence the simulation's outcome. Commonly, one global numerical sensitivity value is computed per simulation parameter. However, when considering 3D spatial simulations, the analysis of localized sensitivities in different spatial regions is of importance in many applications. For analyzing the spatial variation of parameter sensitivity, one needs to compute a spatial sensitivity scalar field per simulation parameter. Given $n$ simulation parameters, we obtain multi-field data consisting of $n$ scalar fields when considering all simulation parameters. We propose an interactive visual analytics solution to analyze the multi-field sensitivity data. It supports the investigation of how strongly and in what way individual parameters influence the simulation outcome, in which spatial regions this is happening, and what the interplay of the simulation parameters is. Its central component is an overview visualization of all sensitivity fields that avoids 3D occlusions by linearizing the data using an adapted scheme of data-driven space-filling curves. The spatial sensitivity values are visualized in a combination of a Horizon Graph and a line chart. We validate our approach by applying it to synthetic and real-world ensemble data., Comment: 15 pages paper, 2 pages appendix, 14 figures paper, 1 figure appendix, accepted at IEEE Transactions on Visualization and Computer Graphics (TVCG)

Partitionings (or segmentations) divide a given domain into disjoint connected regions whose union forms again the entire domain. Multi-dimensional partitionings occur, for example, when analyzing parameter spaces of simulation models, where each segment of the partitioning represents a region of similar model behavior. Having computed a partitioning, one is commonly interested in understanding how large the segments are and which segments lie next to each other. While visual representations of 2D domain partitionings that reveal sizes and neighborhoods are straightforward, this is no longer the case when considering multi-dimensional domains of three or more dimensions. We propose an algorithm for computing 2D embeddings of multi-dimensional partitionings. The embedding shall have the following properties: It shall maintain the topology of the partitioning and optimize the area sizes and joint boundary lengths of the embedded segments to match the respective sizes and lengths in the multi-dimensional domain. We demonstrate the effectiveness of our approach by applying it to different use cases, including the visual exploration of 3D spatial domain segmentations and multi-dimensional parameter space partitionings of simulation ensembles. We numerically evaluate our algorithm with respect to how well sizes and lengths are preserved depending on the dimensionality of the domain and the number of segments., Comment: 9 pages paper, 2 pages references, 4 pages appendix, 13 figures in the paper, 5 figures in the appendix, IEEE VIS 2024 paper

We propose a computational procedure for identifying convection in heat transfer dynamics. The procedure is based on a Gaussian process latent force model, consisting of a white-box component (i.e., known physics) for the conduction and linear convection effects and a Gaussian process that acts as a black-box component for the nonlinear convection effects. States are inferred through Bayesian smoothing and we obtain approximate posterior distributions for the kernel covariance function's hyperparameters using Laplace's method. The nonlinear convection function is recovered from the Gaussian process states using a Bayesian regression model. We validate the procedure by simulation error using the identified nonlinear convection function, on both data from a simulated system and measurements from a physical assembly., Comment: 6 pages, 4 figures. Published in the proceedings of the IEEE Conference on Control Technology and Applications 2024

As is well known, magnetic impurities adsorbed on superconductors, e.g. of the s-wave type, can introduce a bound gap-state (Yu-Shiba-Rusinov resonance). We here investigate within a minimal model how the impurity moment arranges with respect to a weak homogeneous external magnetic field employing a fully self-consistent mean-field treatment. Our investigation reveals a critical window of impurity-to-substrate coupling constants, $J$. The width of the critical region, $\delta J$, scales like $\delta J \sim B/\Delta$, where $B$ is the magnitude of the external field and $\Delta$ is the bulk order parameter. While tuning $J$ through the window, the energy of the Yu-Shiba-Rusinov (YSR) resonance is pinned to the Fermi energy $\varepsilon_\text{F}$ and the impurity moment rotates in a continuous fashion. We emphasize the pivotal role of self-consistency: In treatments ignoring self-consistency, the critical window adopts zero width, $\delta J=0$; consequently, there is no pinning of the YSR-resonance to $\varepsilon_\text{F}$, the impurity orientation jumps and therefore this orientation cannot be controlled continuously by fine-tuning the coupling $J$. In this sense, our study highlights the significance of self-consistency for understanding intricate magnetic interactions between superconductive materials and Shiba chains.

A method to compute the excitation of narrow transitions at hard x-ray energies by short focused x-ray pulses is developed. In particular, the effect of thin-film cavities on the pulse propagation is incorporated via a semi-analytical algorithm requiring the numerical evaluation of only one two-dimensional Fourier transform. We investigate various limiting cases to confirm the reliability of the algorithm. As an application, we show how a focused x-ray pulse propagates in cavity structures utilized in previous theoretical studies and experiments with collimated beams., Comment: 17 pages, 9 figures

Strong excitation of nuclear resonances, particularly of M\"ossbauer nuclei, has been a longstanding goal and the advance of novel x-ray sources is promising new options in this regard. Here we map out the necessary experimental conditions for the more general goal of realizing nonlinear optics with nuclei and compare with available technology. In particular, we present a comprehensive theory of nonlinear nuclear excitation in thin-film x-ray cavities by focused x-ray pulses. We thereby identify cavity geometries with broad resonances which allow one to boost the nuclear excitation even at moderately tight focusing and offer the possibility to mitigate radiation damage., Comment: 8 pages, 4 figures

In a recently developed methodology termed photon induced near-field electron microscopy (PINEM), the inelastic scattering of electrons off illuminated nanostructures provides direct experimental access to the structure of optical near-field modes and their population. Whereas the inelastic scattering probability can be quantitatively linked to the near field distribution, analytical results for simple light scattering geometries are scarce. Here we derive a fully analytical expression for the coupling strength between free electrons and optical near-fields in planar geometries representing dielectric thin films. Contributions to the overall coupling from the electric field above, below and within the sample are analyzed in detail. By carefully choosing the relative angles between electron beam, light and thin film and by accounting for a broad spectrum of photon energies, we demonstrate that one can imprint optical material properties like the reflectivity onto the electron energy distribution., Comment: 17 pages, 4 figures, 32 references

We here analyse the question of developing artificial consciousness from an evolutionary perspective, taking the evolution of the human brain and its relation with consciousness as a reference model. This kind of analysis reveals several structural and functional features of the human brain that appear to be key for reaching human-like complex conscious experience and that current research on Artificial Intelligence (AI) should take into account in its attempt to develop systems capable of conscious processing. We argue that, even if AI is limited in its ability to emulate human consciousness for both intrinsic (structural and architectural) and extrinsic (related to the current stage of scientific and technological knowledge) reasons, taking inspiration from those characteristics of the brain that make conscious processing possible and/or modulate it, is a potentially promising strategy towards developing conscious AI. Also, it is theoretically possible that AI research can develop partial or potentially alternative forms of consciousness that is qualitatively different from the human, and that may be either more or less sophisticated depending on the perspectives. Therefore, we recommend neuroscience-inspired caution in talking about artificial consciousness: since the use of the same word consciousness for humans and AI becomes ambiguous and potentially misleading, we propose to clearly specify what is common and what differs in AI conscious processing from full human conscious experience.

We present the formalism and implementation of quasi-particle self-consistent GW (qsGW) and eigenvalue only quasi-particle self-consistent GW (evGW) adapted to standard quantum chemistry packages. Our implementation is benchmarked against high-level quantum chemistry computations (coupled-cluster theory) and experimental results using a representative set of molecules. Furthermore, we compare the qsGW approach for five molecules relevant for organic photovoltaics to self-consistent GW results (scGW) and analyze the effects of the self-consistency on the ground state density by comparing calculated dipole moments to their experimental values. We show that qsGW makes a significant improvement over conventional G0W0 and that partially self-consistent flavors (in particular evGW) can be excellent alternatives.

Motivated by experimental reports on chirality induced spin selectivity, we investigate a minimal model that allows us to calculate the charge and spin conductances through helical molecules analytically. The spin-orbit interaction is assumed to be non-vanishing on the molecule and negligible in the reservoirs (leads). The band-structure of the molecule features four helical modes with spin-momentum locking that are analogous of edge-currents in the quantum spin Hall effect. While charge is conserved and therefore the charge current is independent of where it is measured, - reservoirs or molecule, - our detailed calculations reveal that the spin currents in the left and right lead are equal in magnitudes but with opposite signs (in linear response). We predict that transport currents flowing through helical molecules are accompanied by a spin accumulation in the contact region with the same magnetization direction for source and drain. Further, we predict that the spin-conductance can be extracted directly from measuring the (quasi-static) spin accumulation - rather than the spin current itself, which is very challenging to obtain experimentally.

Optical orientation of carrier spins by circularly polarized light is the basic concept and tool of spin physics in semiconductors. We study the optical orientation of electrons and holes in a crystal of the FA$_{0.9}$Cs$_{0.1}$PbI$_{2.8}$Br$_{0.2}$ lead halide perovskite by means of polarized photoluminescence, time-resolved differential reflectivity, and time-resolved Kerr rotation. At the cryogenic temperature of 1.6 K the optical orientation degree measured for continuous-wave excitaton reaches 6% for localized electrons and 2\% for localized holes. Their contributions are distinguished from each other and from exciton optical orientation through the pronounced Hanle effect in transverse magnetic fields and the polarization recovery effect in longitudinal magnetic fields. The optical orientation degree is highly stable against detuning of the laser photon energy from the band gap by up to 0.25 eV, showing then a gradual decrease for detunings up to 0.9 eV. This evidences the inefficiency of spin relaxation mechanisms for free carriers during their energy relaxation. Spin relaxation for localized electrons and holes is provided by the hyperfine interaction with the nuclear spins. Dynamic polarization of nuclear spins is demonstrated by the Overhauser field reaching 4 mT acting on the electrons and $-76$ mT acting on the holes. This confirms the specifics of lead halide perovskite semiconductors, where the hole hyperfine interaction with the nuclei considerably exceeds that of the electron.

Is artificial consciousness theoretically possible? Is it plausible? If so, is it technically feasible? To make progress on these questions, it is necessary to lay some groundwork clarifying the logical and empirical conditions for artificial consciousness to arise and the meaning of relevant terms involved. Consciousness is a polysemic word: researchers from different fields, including neuroscience, Artificial Intelligence, robotics, and philosophy, among others, sometimes use different terms in order to refer to the same phenomena or the same terms to refer to different phenomena. In fact, if we want to pursue artificial consciousness, a proper definition of the key concepts is required. Here, after some logical and conceptual preliminaries, we argue for the necessity of using dimensions and profiles of consciousness for a balanced discussion about their possible instantiation or realisation in artificial systems. Our primary goal in this paper is to review the main theoretical questions that arise in the domain of artificial consciousness. On the basis of this review, we propose to assess the issue of artificial consciousness within a multidimensional account. The theoretical possibility of artificial consciousness is already presumed within some theoretical frameworks; however, empirical possibility cannot simply be deduced from these frameworks but needs independent empirical validation. We break down the complexity of consciousness by identifying constituents, components, and dimensions, and reflect pragmatically about the general challenges confronting the creation of artificial consciousness. Despite these challenges, we outline a research strategy for showing how "awareness" as we propose to understand it could plausibly be realised in artificial systems.

The use of electric fields applied across magnetic heterojunctions that lack spatial inversion symmetry has been previously proposed as a non-magnetic mean of controlling localized magnetic moments through spin-orbit torques (SOT). The implementation of this concept at the single-molecule level has remained a challenge, however. Here, we present first-principle calculations of SOT in a single-molecule junction under bias and beyond linear response. Employing a self-consistency scheme invoking density functional theory and non-equilibrium Green's function theory, we compute the current-induced SOT. Responding to this torque, a localized magnetic moment can tilt. Within the linear regime our quantitative estimates for the SOT in single-molecule junctions yield values similar to those known for magnetic interfaces. Our findings contribute to an improved microscopic understanding of SOT in single molecules., Comment: 7 pages + 5 figures; Supporting Information (16 pages + 3 figures)

Experimental indications have been reported suggesting that laser pulses shining on materials with relativistic dispersion can produce currents that survive long after the illumination has died out. Such residual currents ('remnants') have applications in petahertz logical gates. The remnants' strength strongly depends on the pulse-shape. We develop an analytical formula that allows to optimize the pulse-shape for remnant production; we predict remnants exceeding the values observed so far by orders of magnitude. In fact, remnants can be almost as strong as the peak current under irradiation.