We present a multi-modal stress dataset that uses digital job interviews to induce stress. The dataset provides multi-modal data of 40 participants including audio, video (motion capturing, facial recognition, eye tracking) as well as physiological information (photoplethysmography, electrodermal activity). In addition to that, the dataset contains time-continuous annotations for stress and occurred emotions (e.g. shame, anger, anxiety, surprise). In order to establish a baseline, five different machine learning classifiers (Support Vector Machine, K-Nearest Neighbors, Random Forest, Long-Short-Term Memory Network) have been trained and evaluated on the proposed dataset for a binary stress classification task. The best-performing classifier achieved an accuracy of 88.3% and an F1-score of 87.5%.

Understanding causality in event sequences where outcome labels such as diseases or system failures arise from preceding events like symptoms or error codes is critical. Yet remains an unsolved challenge across domains like healthcare or vehicle diagnostics. We introduce CARGO, a scalable multi-label causal discovery method for sparse, high-dimensional event sequences comprising of thousands of unique event types. Using two pretrained causal Transformers as domain-specific foundation models for event sequences. CARGO infers in parallel, per sequence one-shot causal graphs and aggregates them using an adaptive frequency fusion to reconstruct the global Markov boundaries of labels. This two-stage approach enables efficient probabilistic reasoning at scale while bypassing the intractable cost of full-dataset conditional independence testing. Our results on a challenging real-world automotive fault prediction dataset with over 29,100 unique event types and 474 imbalanced labels demonstrate CARGO's ability to perform structured reasoning.

The recently discovered altermagnets exhibit collinear magnetic order with zero net magnetization but with unconventional spin-polarized d/g/i-wave band structures, expanding the known paradigms of ferromagnets and antiferromagnets. In addition to novel current-driven electronic transport effects, the unconventional time-reversal symmetry breaking in these systems also makes it possible to obtain a spin response to linearly polarized fields in the optical frequency domain. We show through ab-initio calculations of the prototypical d-wave altermagnet RuO$_2$, with $[C_2\|C_{4z}]$ symmetry combining twofold spin rotation with fourfold lattice rotation, that there is an optical analogue of a spin splitter effect, as the coupling to a linearly polarized exciting laser field makes the d-wave character of the altermagnet directly visible. By magneto-optical measurements on RuO$_2$ films of a few nanometer thickness, we demonstrate the predicted connection between the polarization of an ultrashort pump pulse and the sign and magnitude of a persistent optically excited electronic spin polarization. Our results point to the possibility of exciting and controlling the electronic spin polarization in altermagnets by such ultrashort optical pulses. In addition, the possibility of exciting an electronic spin polarization by linearly polarized optical fields in a compensated system is a unique consequence of the altermagnetic material properties, and our experimental results therefore present an indication for the existence of an altermagnetic phase in ultrathin RuO$_2$ films.

The next step for intelligent dialog agents is to escape their role as silent bystanders and become proactive. Well-defined proactive behavior may improve human-machine cooperation, as the agent takes a more active role during interaction and takes off responsibility from the user. However, proactivity is a double-edged sword because poorly executed pre-emptive actions may have a devastating effect not only on the task outcome but also on the relationship with the user. For designing adequate proactive dialog strategies, we propose a novel approach including both social as well as task-relevant features in the dialog. Here, the primary goal is to optimize proactive behavior so that it is task-oriented - this implies high task success and efficiency - while also being socially effective by fostering user trust. Including both aspects in the reward function for training a proactive dialog agent using reinforcement learning showed the benefit of our approach for more successful human-machine cooperation.

We present a structured additive regression approach to model conditional densities given scalar covariates, where only samples of the conditional distributions are observed. This links our approach to distributional regression models for scalar data. The model is formulated in a Bayes Hilbert space -- preserving nonnegativity and integration to one under summation and scalar multiplication -- with respect to an arbitrary finite measure. This allows to consider, amongst others, continuous, discrete and mixed densities. Our theoretical results include asymptotic existence, uniqueness, consistency, and asymptotic normality of the penalized maximum likelihood estimator, as well as confidence regions and inference for the (effect) densities. For estimation, we propose to maximize the penalized log-likelihood corresponding to an appropriate multinomial, or equivalently, Poisson regression model, which we show to approximate the original penalized maximum likelihood problem. We apply our framework to a motivating gender economic data set from the German Socio-Economic Panel Study (SOEP), analyzing the distribution of the woman's share in a couple's total labor income given covariate effects for year, place of residence and age of the youngest child. As the income share is a continuous variable having discrete point masses at zero and one for single-earner couples, the corresponding densities are of mixed type.

Layered 2D van der Waals materials, such as transition metal dichalcogenides, are promising for nanoscale spintronic and optoelectronic applications. Harnessing their full potential requires understanding both intrinsic transport and the dynamics of optically excited spin and charge carriers -- particularly the transition between excited spin polarization and the conduction band's intrinsic spin texture. Here, we investigate the spin polarization of the conduction bands of bulk WSe$_2$ using static and time-resolved spin-resolved photoemission spectroscopy, complemented by photocurrent calculations. Electron doping reveals the intrinsic spin polarization, while time-resolved measurements trace the evolution of excited spin carriers. We find that intervalley scattering is spin-conserving, with spin transport initially governed by photoexcited carriers and aligning with the intrinsic conduction band polarization after $\sim$150 fs.

The Schrieffer-Wolff transformation (SWT) is a foundational perturbative method for deriving effective Hamiltonians in quantum systems by systematically eliminating couplings between pairs of energy distant subspaces. Despite recent advancements, the implementation of SWTs for sufficiently complex systems remains computationally challenging and often requires extensive calculations that are prone to errors. In this work, we introduce an analytical software tool, SymPT (Symbolic Perturbation Theory), designed to automate the SWT and its extensions. Building on a universal framework developed in recent research, SymPT provides a systematic and generalizable solution for deriving the generator of the transformation, enabling accurate computation of effective Hamiltonians for arbitrary perturbative systems. The tool supports both time-independent and time-periodic Hamiltonians, extending beyond standard SWT to incorporate arbitrary coupling elimination, block-diagonalization and full-diagonalization routines, thus enabling precise handling of systems with intricate energy structures.

Job interviews are usually high-stakes social situations where professional and behavioral skills are required for a satisfactory outcome. Professional job interview trainers give educative feedback about the shown behavior according to common standards. This feedback can be helpful concerning the improvement of behavioral skills needed for job interviews. A technological approach for generating such feedback might be a playful and low-key starting point for job interview training. Therefore, we extended an interactive virtual job interview training system with a Generative Adversarial Network (GAN)-based approach that first detects behavioral weaknesses and subsequently generates personalized feedback. To evaluate the usefulness of the generated feedback, we conducted a mixed-methods pilot study using mock-ups from the job interview training system. The overall study results indicate that the GAN-based generated behavioral feedback is helpful. Moreover, participants assessed that the feedback would improve their job interview performance.

The main goal of this text is comprehensive study of time homogeneous Markov chains on the real line whose drift tends to zero at infinity, we call such processes Markov chains with asymptotically zero drift. Traditionally this topic is referred to as Lamperti's problem. Time homogeneous Markov chains with asymptotically zero drift may be viewed as a subclass of perturbed in space random walks. The latter are of basic importance in the study of various applied stochastic models, among them branching and risk processes, queueing systems etc. Random walks generated by sums of independent identically distributed random variables are well studied, see e.g. classical textbooks by W. Feller (1971), V.V. Petrov (1975), or F. Spitzer (1964); for the recent development of the theory of random walks we refer to A.A. Borovkov and K.A. Borovkov (2008). There are many monographs devoted to various applications where random walks play a crucial role, let us just mention books on ruin and queueing processes by S. Asmussen (2000, 2003); on insurance and finance by P. Embrechts, C. Kluppelberg, and T. Mikosch (1997), and T. Rolski, H. Schmidli, V. Schmidt, and J. Teugels (1998); and on stochastic difference equations by D. Buraczewski, E. Damek and T. Mikosch (2016). In the same applied stochastic models, if one allows the process considered to be dependent on the current state of the process, we often get a Markov chain which has asymptotically zero drift, we demonstrate that in the last chapter, where we particularly discuss branching and risk processes, stochastic difference equations and ALOHA network.

Imitation Learning from observation describes policy learning in a similar way to human learning. An agent's policy is trained by observing an expert performing a task. While many state-only imitation learning approaches are based on adversarial imitation learning, one main drawback is that adversarial training is often unstable and lacks a reliable convergence estimator. If the true environment reward is unknown and cannot be used to select the best-performing model, this can result in bad real-world policy performance. We propose a non-adversarial learning-from-observations approach, together with an interpretable convergence and performance metric. Our training objective minimizes the Kulback-Leibler divergence (KLD) between the policy and expert state transition trajectories which can be optimized in a non-adversarial fashion. Such methods demonstrate improved robustness when learned density models guide the optimization. We further improve the sample efficiency by rewriting the KLD minimization as the Soft Actor Critic objective based on a modified reward using additional density models that estimate the environment's forward and backward dynamics. Finally, we evaluate the effectiveness of our approach on well-known continuous control environments and show state-of-the-art performance while having a reliable performance estimator compared to several recent learning-from-observation methods.

Traditional approaches to automatic emotion recognition are relying on the application of handcrafted features. More recently however the advent of deep learning enabled algorithms to learn meaningful representations of input data automatically. In this paper, we investigate the applicability of transferring knowledge learned from large text and audio corpora to the task of automatic emotion recognition. To evaluate the practicability of our approach, we are taking part in this year's Interspeech ComParE Elderly Emotion Sub-Challenge, where the goal is to classify spoken narratives of elderly people with respect to the emotion of the speaker. Our results show that the learned feature representations can be effectively applied for classifying emotions from spoken language. We found the performance of the features extracted from the audio signal to be not as consistent as those that have been extracted from the transcripts. While the acoustic features achieved best in class results on the development set, when compared to the baseline systems, their performance dropped considerably on the test set of the challenge. The features extracted from the text form, however, are showing promising results on both sets and are outperforming the official baseline by 5.7 percentage points unweighted average recall.

Perovskite SrRuO$_3$ is a prototypical itinerant ferromagnet which allows interface engineering of its electronic and magnetic properties. We report synthesis and investigation of atomically flat artificial multilayers of SrRuO$_3$ with the spin-orbit semimetal SrIrO$_3$ in combination with band-structure calculations with a Hubbard $U$ term and topological analysis. They reveal an electronic reconstruction and emergence of flat Ru-4d$_{xz}$ bands near the interface, ferromagnetic interlayer coupling and negative Berry-curvature contribution to the anomalous Hall effect. We analyze the Hall effect and magnetoresistance measurements as a function of the field angle from out of plane towards in-plane orientation (either parallel or perpendicular to the current direction) by a two-channel model. The magnetic easy direction is tilted by about $20^\circ$ from the sample normal for low magnetic fields, rotating towards the out-of-plane direction by increasing fields. Fully strained epitaxial growth enables a strong anisotropy of magnetoresistance. An additional Hall effect contribution, not accounted for by the two-channel model is compatible with stable skyrmions only up to a critical angle of roughly $45^\circ$ from the sample normal. Within about $20^\circ$ from the thin film plane an additional peak-like contribution to the Hall effect suggests the formation of a non-trivial spin structure.

Layered 2D van der Waals materials, such as transition metal dichalcogenides, are promising for nanoscale spintronic and optoelectronic applications. Harnessing their full potential requires understanding both intrinsic transport and the dynamics of optically excited spin and charge carriers -- particularly the transition between excited spin polarization and the conduction band's intrinsic spin texture. Here, we investigate the spin polarization of the conduction bands of bulk WSe$_2$ using static and time-resolved spin-resolved photoemission spectroscopy, complemented by photocurrent calculations. Electron doping reveals the intrinsic spin polarization, while time-resolved measurements trace the evolution of excited spin carriers. We find that intervalley scattering is spin-conserving, with spin transport initially governed by photoexcited carriers and aligning with the intrinsic conduction band polarization after $\sim$150 fs.

The unexpected discovery of intrinsic ferromagnetism in layered van der Waals materials has sparked interest in both their fundamental properties and their potential for novel applications. Recent studies suggest near room-temperature ferromagnetism in the pressurized van der Waals crystal CrGeTe$_3$. We perform a comprehensive experimental and theoretical investigation of magnetism and electronic correlations in CrGeTe$_3$, combining broad-frequency reflectivity measurements with density functional theory and dynamical mean-field theory calculations. Our experimental optical conductivity spectra trace the signatures of developing ferromagnetic order and of the insulator-to-metal transition (IMT) as a function of temperature and hydrostatic pressure. With increasing pressure, we observe the emergence of a mid-infrared feature in the optical conductivity, indicating the development of strong orbital-selective correlations in the high-pressure ferromagnetic phase. We find a distinct relationship between the plasma frequency and Curie temperature of CrGeTe$_3$, which strongly suggests that a double-exchange mechanism is responsible for the observed near room-temperature ferromagnetism. Our results clearly demonstrate the existence of a charge-transfer gap in the metallic phase, ruling out its previously conjectured collapse under pressure.

Polarization-dependent reflectivity measurements were carried out over a broad frequency range on single-crystalline ZrGeSe and ZrGeS compounds, which are closely related to the prototype nodal-line semimetal ZrSiS. These measurements revealed the strongly anisotropic character of both ZrGeSe and ZrGeS, with a reduced plasma frequency for the out-of-plane direction {\bf E}$\| c$ as compared to the in-plane direction {\bf E}$\| ab$. For {\bf E}$\| ab$ the optical conductivity spectrum consists of two Drude terms followed by a shoulder or plateau-like behavior and a distinct U shape at higher energies, while for {\bf E}$\| c$ one Drude term is followed by a peak-like behavior and the U shape of the profile is less developed. Under external pressure, two prominent excitations appear in the out-of-plane optical conductivity spectrum of ZrGeSe, whose frequency position and oscillator strength show a weak anomaly at $\sim$3~GPa. Overall, the pressure-induced changes in the profile of the {\bf E}$\| c$ conductivity spectrum are much enhanced above $\sim$3~GPa. We compare our results to those recently reported for ZrSiS in a quantitative manner.

Unconventional superconductivity and magnetism are intertwined on a microscopic level in a wide class of materials. A new approach to this most fundamental and hotly debated issue focuses on the role of interactions between superconducting electrons and bosonic fluctuations at the interface between adjacent layers in heterostructures. Here we fabricate hybrid superlattices consisting of alternating atomic layers of heavy-fermion superconductor CeCoIn$_5$ and antiferromagnetic (AFM) metal CeRhIn$_5$, in which the AFM order can be suppressed by applying pressure. We find that the superconducting and AFM states coexist in spatially separated layers, but their mutual coupling via the interface significantly modifies the superconducting properties. An analysis of upper critical fields reveals that near the critical pressure where AFM order vanishes, the force binding superconducting electron-pairs acquires an extremely strong-coupling nature. This demonstrates that superconducting pairing can be tuned non-trivially by magnetic fluctuations (paramagnons) injected through the interface, leading to maximization of the pairing interaction.

We investigate the dynamics of a pair of rigid rotating helices in a viscous fluid, as a model for bacterial flagellar bundle and a prototype of microfluidic pumps. Combining experiments with hydrodynamic modeling, we examine how spacing and phase difference between the two helices affect their torque, flow field and fluid transport capacity at low Reynolds numbers. Hydrodynamic coupling reduces the torque when the helices rotate in phase at constant angular speed, but increases the torque when they rotate out of phase. We identify a critical phase difference, at which the hydrodynamic coupling vanishes despite the close spacing between the helices. A simple model, based on the flow characteristics and positioning of a single helix, is constructed, which quantitatively predicts the torque of the helical pair in both unbounded and confined systems. Lastly, we show the influence of spacing and phase difference on the axial flux and the pump efficiency of the helices. Our findings shed light on the function of bacterial flagella and provide design principles for efficient low-Reynolds-number pumps.

Purpose. To present SPINEPS, an open-source deep learning approach for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole body T2w MRI. Methods. During this HIPPA-compliant, retrospective study, we utilized the public SPIDER dataset (218 subjects, 63% female) and a subset of the German National Cohort (1423 subjects, mean age 53, 49% female) for training and evaluation. We combined CT and T2w segmentations to train models that segment 14 spinal structures in T2w sagittal scans both semantically and instance-wise. Performance evaluation metrics included Dice similarity coefficient, average symmetrical surface distance, panoptic quality, segmentation quality, and recognition quality. Statistical significance was assessed using the Wilcoxon signed-rank test. An in-house dataset was used to qualitatively evaluate out-of-distribution samples. Results. On the public dataset, our approach outperformed the baseline (instance-wise vertebra dice score 0.929 vs. 0.907, p-value<0.001). Training on auto-generated annotations and evaluating on manually corrected test data from the GNC yielded global dice scores of 0.900 for vertebrae, 0.960 for intervertebral discs, and 0.947 for the spinal canal. Incorporating the SPIDER dataset during training increased these scores to 0.920, 0.967, 0.958, respectively. Conclusions. The proposed segmentation approach offers robust segmentation of 14 spinal structures in T2w sagittal images, including the spinal cord, spinal canal, intervertebral discs, endplate, sacrum, and vertebrae. The approach yields both a semantic and instance mask as output, thus being easy to utilize. This marks the first publicly available algorithm for whole spine segmentation in sagittal T2w MR imaging.

CeRuPO is a rare example of a ferromagnetic (FM) Kondo-lattice system. External pressure suppresses the ordering temperature to zero at about $p_c\approx3$ GPa. Our ac-susceptibility and electrical-resistivity investigations evidence that the type of magnetic ordering changes from FM to antiferromagnetic (AFM) at about $p^*\approx0.87$ GPa. Studies in applied magnetic fields suggest that ferromagnetic and antiferromagnetic correlations compete for the ground state at $p>p^*$, but finally the AFM correlations win. The change in the magnetic ground-state properties is closely related to the pressure evolution of the crystalline-electric-field level (CEF) scheme and the magnetic Ruderman-Kittel-Kasuya-Yosida (RKKY) exchange interaction. The N\'{e}el temperature disappears abruptly in a first-order-like fashion at $p_c$, hinting at the absence of a quantum critical point. This is consistent with the low-temperature transport properties exhibiting Landau-Fermi-liquid (LFL) behavior in the whole investigated pressure range up to 7.5 GPa.

We review theoretical and experimental highlights in transport in two-dimensional materials focussing on key developments over the last five years. Topological insulators are finding applications in magnetic devices, while Hall transport in doped samples and the general issue of topological protection remain controversial. In transition metal dichalcogenides valley-dependent electrical and optical phenomena continue to stimulate state-of-the-art experiments. In Weyl semimetals the properties of Fermi arcs are being actively investigated. A new field, expected to grow in the near future, focuses on the non-linear electrical and optical responses of topological materials, where fundamental questions are once more being asked about the intertwining roles of the Berry curvature and disorder scattering. In topological superconductors the quest for chiral superconductivity, Majorana fermions and topological quantum computing is continuing apace.