Correlated Electronic Structure of La_{3}Ni_{2}O_{7} under Pressure.

Recently, superconductivity with a T_{c} up to 78 K has been reported in bulk samples of the bilayer nickelate La_{3}Ni_{2}O_{7} at pressures above 14 GPa. Important theoretical tasks are the formulation of relevant low-energy models and the clarification of the normal state properties. Here, we study the correlated electronic structure of the high-pressure phase in a four-orbital low-energy subspace using different many-body approaches: GW, dynamical mean field theory (DMFT), extended DMFT (EDMFT) and GW+EDMFT, with realistic frequency-dependent interaction parameters. The nonlocal correlation and screening effects captured by GW+EDMFT result in an instability toward the formation of charge stripes, with the 3d_{z^{2}} as the main active orbital. We also comment on the potential relevance of the rare-earth self-doping pocket, since hole doping suppresses the ordering tendency.

Quantum Optics Measurement Scheme for Quantum Geometry and Topological Invariants.

We show how a quantum optical measurement scheme based on heterodyne detection can be used to explore geometrical and topological properties of condensed matter systems. Considering a 2D material placed in a cavity with a coupling to the environment, we compute correlation functions of the photons exiting the cavity and relate them to the hybrid light-matter state within the cavity. Different polarizations of the intracavity field give access to all components of the quantum geometric tensor on contours in the Brillouin zone defined by the transition energy. Combining recent results based on the metric-curvature correspondence with the measured quantum metric allows us to characterize the topological phase of the material. Moreover, in systems where S_{z} is a good quantum number, the procedure also allows us to extract the spin Chern number. As an interesting application, we consider a minimal model for twisted bilayer graphene at the magic angle, and discuss the feasibility of extracting the Euler number.

Phosphorylated tau in Alzheimer's disease.

There is a need for blood biomarkers to detect individuals at different Alzheimer's disease (AD) stages because obtaining cerebrospinal fluid-based biomarkers is invasive and costly. Plasma phosphorylated tau proteins (p-tau) have shown potential as such biomarkers. This systematic review was conducted according to the PRISMA guidelines and aimed to determine whether quantification of plasma tau phosphorylated at threonine 181 (p-tau181), threonine 217 (p-tau217) and threonine 231 (p-tau231) is informative in the diagnosis of AD. All p-tau isoforms increase as a function of Aß-accumulation and discriminate healthy individuals from those at preclinical AD stages with high accuracy. P-tau231 increases earliest, followed by p-tau181 and p-tau217. In advanced stages, all p-tau isoforms are associated with the clinical classification of AD and increase with disease severity, with the greatest increase seen for p-tau217. This is also reflected by a better correlation of p-tau217 with Aß scans, whereas both, p-tau217 and p-tau181 correlated equally with tau scans. However, at the very advanced stages, p-tau181 begins to plateau, which may mirror the trajectory of the Aß pathology and indicate an association with a more intermediate risk of AD. Across the AD continuum, the incremental increase in all biomarkers is associated with structural changes in widespread brain regions and underlying cognitive decline. Furthermore, all isoforms differentiate AD from non-AD neurodegenerative disorders, making them specific for AD. Incorporating p-tau181, p-tau217 and p-tau231 in clinical use requires further studies to examine ideal cut-points and harmonize assays.

Automated Electrodermal Activity and Facial Expression Analysis for Continuous Pain Intensity Monitoring on the X-ITE Pain Database.

This study focuses on improving healthcare quality by introducing an automated system that continuously monitors patient pain intensity. The system analyzes the Electrodermal Activity (EDA) sensor modality modality, compares the results obtained from both EDA and facial expressions modalities, and late fuses EDA and facial expressions modalities. This work extends our previous studies of pain intensity monitoring via an expanded analysis of the two informative methods. The EDA sensor modality and facial expression analysis play a prominent role in pain recognition; the extracted features reflect the patient's responses to different pain levels. Three different approaches were applied: Random Forest (RF) baseline methods, Long-Short Term Memory Network (LSTM), and LSTM with the sample-weighting method (LSTM-SW). Evaluation metrics included Micro average F1-score for classification and Mean Squared Error (MSE) and intraclass correlation coefficient (ICC [3, 1]) for both classification and regression. The results highlight the effectiveness of late fusion for EDA and facial expressions, particularly in almost balanced datasets (Micro average F1-score around 61%, ICC about 0.35). EDA regression models, particularly LSTM and LSTM-SW, showed superiority in imbalanced datasets and outperformed guessing (where the majority of votes indicate no pain) and baseline methods (RF indicates Random Forest classifier (RFc) and Random Forest regression (RFr)). In conclusion, by integrating both modalities or utilizing EDA, they can provide medical centers with reliable and valuable insights into patients' pain experiences and responses.

Spin, Charge, and η-Spin Separation in One-Dimensional Photodoped Mott Insulators.

We show that effectively cold metastable states in one-dimensional photodoped Mott insulators described by the extended Hubbard model exhibit spin, charge, and η-spin separation. Their wave functions in the large on-site Coulomb interaction limit can be expressed as |Ψ⟩=|Ψ_{charge}⟩|Ψ_{spin}⟩|Ψ_{η-spin}⟩, which is analogous to the Ogata-Shiba states of the doped Hubbard model in equilibrium. Here, the η-spin represents the type of photo-generated pseudoparticles (doublon or holon). |Ψ_{charge}⟩ is determined by spinless free fermions, |Ψ_{spin}⟩ by the isotropic Heisenberg model in the squeezed spin space, and |Ψ_{η-spin}⟩ by the XXZ model in the squeezed η-spin space. In particular, the metastable η-pairing and charge-density-wave (CDW) states correspond to the gapless and gapful states of the XXZ model. The specific form of the wave function allows us to accurately determine the exponents of correlation functions. The form also suggests that the central charge of the η-pairing state is 3 and that of the CDW phase is 2, which we numerically confirm. Our study provides analytic and intuitive insights into the correlations between active degrees of freedom in photodoped strongly correlated systems.

Vertex-Based Diagrammatic Treatment of Light-Matter-Coupled Systems.

We propose a diagrammatic Monte Carlo approach for quantum impurity models, which can be regarded as a generalization of the strong-coupling expansion for fermionic impurity models. The algorithm is based on a self-consistently computed three-point vertex and a stochastically sampled four-point vertex, and it allows one to obtain numerically exact results in a wide parameter regime. The performance of the algorithm is demonstrated with applications to a spin-boson model representing an emitter in a waveguide. As a function of the coupling strength, the spin exhibits a delocalization-localization crossover at low temperatures, signaling a qualitative change in the real-time relaxation. In certain parameter regimes, the response functions of the emitter coupled to the electromagnetic continuum can be described by an effective Rabi model with appropriately defined parameters. We also discuss the spatial distribution of the photon density around the emitter.

Endovascular stroke therapy outside core working hours in a nationwide stroke system.

BACKGROUND: Endovascular therapy (EVT) has been established as a major component in the acute treatment of large vessel occlusion stroke. However, it is unclear whether outcome and other treatment-related factors differ if patients are treated within or outside core working hours. METHODS: We analyzed data from the prospective nationwide Austrian Stroke Unit Registry capturing all consecutive stroke patients treated with EVT between 2016 and 2020. Patients were trichotomized according to the time of groin puncture into treatment within regular working hours (08:00-13:59), afternoon/evening (14:00-21:59) and night-time (22:00-07:59). Additionally, we analyzed 12 EVT treatment windows with equal patient numbers. Main outcome variables included favorable outcome (modified Rankin Scale scores of 0-2) 3 months post-stroke as well as procedural time metrics, recanalization status and complications. RESULTS: We analyzed 2916 patients (median age 74 years, 50.7% female) who underwent EVT. Patients treated within core working hours more frequently had a favorable outcome (42.6% vs 36.1% treated in the afternoon/evening vs 35.8% treated at night-time; p=0.007). Similar results were found when analyzing 12 treatment windows. All these differences remained significant in multivariable analysis adjusting for outcome-relevant co-factors. Onset-to-recanalization time was considerably longer outside core working hours, which was mainly explained by longer door-to-groin time (p<0.001). There was no difference in the number of passes, recanalization status, groin-to-recanalization time and EVT-related complications. CONCLUSIONS: The findings of delayed intrahospital EVT workflows and worse functional outcomes outside core working hours in this nationwide registry are relevant for optimization of stroke care, and might be applicable to other countries with similar settings.

Probing magnetic orbitals and Berry curvature with circular dichroism in resonant inelastic X-ray scattering.

Resonant inelastic X-ray scattering (RIXS) can probe localized excitations at selected atoms in materials, including particle-hole transitions between the valence and conduction bands. These transitions are governed by fundamental properties of the corresponding Bloch wave functions, including orbital and magnetic degrees of freedom, and quantum geometric properties such as the Berry curvature. In particular, orbital angular momentum (OAM), which is closely linked to the Berry curvature, can exhibit a nontrivial momentum dependence. We demonstrate how information on such OAM textures can be extracted from the circular dichroism in RIXS. Based on accurate modeling with a first-principles treatment of the key ingredient-the light-matter interaction-we simulate dichroic RIXS spectra for the prototypical transition-metal dichalcogenide MoSe2 and the two-dimensional topological insulator 1T'-MoS2. Guided by an intuitive picture of the optical selection rules, we discuss how the momentum-dependent OAM manifests itself in the dichroic RIXS signal if one controls the momentum transfer. Our calculations are performed for typical experimental geometries and parameter regimes, and demonstrate the possibility of observing the predicted circular dichroism in forthcoming experiments. Thus, our work establishes a new avenue for observing Berry curvature and topological states in quantum materials.

Anomalous Temperature Dependence of High-Harmonic Generation in Mott Insulators.

We reveal the crucial effect of strong spin-charge coupling on high-harmonic generation (HHG) in Mott insulators. In a system with antiferromagnetic correlations, the HHG signal is drastically enhanced with decreasing temperature, even though the gap increases and the production of charge carriers is suppressed. This anomalous behavior, which has also been observed in recent HHG experiments on Ca_{2}RuO_{4}, originates from a cooperative effect between the spin-charge coupling and the thermal ensemble, as well as the strongly temperature-dependent coherence between charge carriers. We argue that the peculiar temperature dependence of HHG is a generic feature of Mott insulators, which can be controlled via the Coulomb interaction and dimensionality of the system. Our results demonstrate that correlations between different degrees of freedom, which are a characteristic feature of strongly correlated solids, have significant and nontrivial effects on nonlinear optical responses.

Field-induced ultrafast modulation of Rashba coupling at room temperature in ferroelectric α-GeTe(111).

Rashba materials have appeared as an ideal playground for spin-to-charge conversion in prototype spintronics devices. Among them, α-GeTe(111) is a non-centrosymmetric ferroelectric semiconductor for which a strong spin-orbit interaction gives rise to giant Rashba coupling. Its room temperature ferroelectricity was recently demonstrated as a route towards a new type of highly energy-efficient non-volatile memory device based on switchable polarization. Currently based on the application of an electric field, the writing and reading processes could be outperformed by the use of femtosecond light pulses requiring exploration of the possible control of ferroelectricity on this timescale. Here, we probe the room temperature transient dynamics of the electronic band structure of α-GeTe(111) using time and angle-resolved photoemission spectroscopy. Our experiments reveal an ultrafast modulation of the Rashba coupling mediated on the fs timescale by a surface photovoltage, namely an increase corresponding to a 13% enhancement of the lattice distortion. This opens the route for the control of the ferroelectric polarization in α-GeTe(111) and ferroelectric semiconducting materials in quantum heterostructures.

Doping Asymmetry and Layer-Selective Metal-Insulator Transition in Trilayer K_{3+x}C_{60}.

Thin films provide a versatile platform to tune electron correlations and explore new physics in strongly correlated materials. Epitaxially grown thin films of the alkali-doped fulleride K_{3+x}C_{60}, for example, exhibit intriguing phenomena, including Mott transitions and superconductivity, depending on dimensionality and doping. Surprisingly, in the trilayer case, a strong electron-hole doping asymmetry has been observed in the superconducting phase, which is absent in the three-dimensional bulk limit. Using density-functional theory plus dynamical mean-field theory, we show that this doping asymmetry results from a substantial charge reshuffling from the top layer to the middle layer. While the nominal filling per fullerene is close to n=3, the top layer rapidly switches to an n=2 insulating state upon hole doping, which implies a doping asymmetry of the superconducting gap. The interlayer charge transfer and layer-selective metal-insulator transition result from the interplay between crystal field splittings, strong Coulomb interactions, and an effectively negative Hund coupling. This peculiar charge reshuffling is absent in the monolayer system, which is an n=3 Mott insulator, as expected from the nominal filling.

An Automatic System for Continuous Pain Intensity Monitoring Based on Analyzing Data from Uni-, Bi-, and Multi-Modality.

Pain is a reliable indicator of health issues; it affects patients' quality of life when not well managed. The current methods in the clinical application undergo biases and errors; moreover, such methods do not facilitate continuous pain monitoring. For this purpose, the recent methodologies in automatic pain assessment were introduced, which demonstrated the possibility for objectively and robustly measuring and monitoring pain when using behavioral cues and physiological signals. This paper focuses on introducing a reliable automatic system for continuous monitoring of pain intensity by analyzing behavioral cues, such as facial expressions and audio, and physiological signals, such as electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA) from the X-ITE Pain Dataset. Several experiments were conducted with 11 datasets regarding classification and regression; these datasets were obtained from the database to reduce the impact of the imbalanced database problem. With each single modality (Uni-modality) experiment, we used a Random Forest [RF] baseline method, a Long Short-Term Memory (LSTM) method, and a LSTM using a sample weighting method (called LSTM-SW). Further, LSTM and LSTM-SW were used with fused modalities (two modalities = Bi-modality and all modalities = Multi-modality) experiments. Sample weighting was used to downweight misclassified samples during training to improve the performance. The experiments' results confirmed that regression is better than classification with imbalanced datasets, EDA is the best single modality, and fused modalities improved the performance significantly over the single modality in 10 out of 11 datasets.

Mott versus Hybridization Gap in the Low-Temperature Phase of 1T-TaS_{2}.

We address the long-standing problem of the ground state of 1T-TaS_{2} by computing the correlated electronic structure of stacked bilayers using the GW+EDMFT method. Depending on the surface termination, the semi-infinite uncorrelated system is either band insulating or exhibits a metallic surface state. For realistic values of the on-site and inter-site interactions, a Mott gap opens in the surface state, but it is smaller than the gap originating from the bilayer structure. Our results are consistent with recent scanning tunneling spectroscopy measurements for different terminating layers, and with our own photoemission measurements, which indicate the coexistence of spatial regions with different gaps in the electronic spectrum. By comparison to exact diagonalization data, we clarify the interplay between Mott insulating and band insulating behavior in this archetypal layered system.

P-tau217 in Alzheimer's disease.

BACKGROUND AND AIMS: The potential of disease-modifying therapies for Alzheimer's disease has greatly stimulated interest in the development of minimally invasive testing for early identification of at-risk individuals. Accordingly, identification of blood-based biomarkers is paramount. The recent discovery of plasma phosphorylated at threonine217 (p-tau217) may provide a turning point in Alzheimer's disease detection. This systematic review aims to evaluate the available evidence on the use of plasma p-tau217 as a marker to predict Alzheimer's disease and to monitor disease progression. MATERIAL AND METHODS: This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Study quality was assessed using the QUADAS-2 tool. In total, 676 publications were identified, of which 16 were in accordance with the pre-defined eligibility criteria. RESULTS: Current evidence shows that plasma p-tau217 is a sensitive maker of the clinical manifestation and progression of Alzheimer's disease and of pathological changes associated with this condition, including amyloid accumulation, tau burden, brain atrophy and physical degradation. Moreover, given that plasma p-tau217 does not predict such changes in patients with other neurodegenerative disorders, plasma p-tau217 is also specific to Alzheimer's disease. CONCLUSION: More large, diverse community studies are needed to harmonize plasma p-tau217 measurements and to determine widely applicable diagnostic cut-off values.

SFPD: Simultaneous Face and Person Detection in Real-Time for Human-Robot Interaction.

Face and person detection are important tasks in computer vision, as they represent the first component in many recognition systems, such as face recognition, facial expression analysis, body pose estimation, face attribute detection, or human action recognition. Thereby, their detection rate and runtime are crucial for the performance of the overall system. In this paper, we combine both face and person detection in one framework with the goal of reaching a detection performance that is competitive to the state of the art of lightweight object-specific networks while maintaining real-time processing speed for both detection tasks together. In order to combine face and person detection in one network, we applied multi-task learning. The difficulty lies in the fact that no datasets are available that contain both face as well as person annotations. Since we did not have the resources to manually annotate the datasets, as it is very time-consuming and automatic generation of ground truths results in annotations of poor quality, we solve this issue algorithmically by applying a special training procedure and network architecture without the need of creating new labels. Our newly developed method called Simultaneous Face and Person Detection (SFPD) is able to detect persons and faces with 40 frames per second. Because of this good trade-off between detection performance and inference time, SFPD represents a useful and valuable real-time framework especially for a multitude of real-world applications such as, e.g., human-robot interaction.

Imaging the coherent propagation of collective modes in the excitonic insulator Ta2NiSe5 at room temperature.

Excitonic insulators host a condensate of electron-hole pairs at equilibrium, giving rise to collective many-body effects. Although several materials have emerged as excitonic insulator candidates, evidence of long-range coherence is lacking and the origin of the ordered phase in these systems remains controversial. Here, using ultrafast pump-probe microscopy, we investigate the possible excitonic insulator Ta2NiSe5 Below 328 K, we observe the anomalous micrometer-scale propagation of coherent modes at velocities of ~105 m/s, which we attribute to the hybridization between phonon modes and the phase mode of the condensate. We develop a theoretical framework to support this explanation and propose that electronic interactions provide a substantial contribution to the ordered phase in Ta2NiSe5 These results allow us to understand how the condensate's collective modes transport energy and interact with other degrees of freedom. Our study provides a unique paradigm for the investigation and manipulation of these properties in strongly correlated materials.

Automatic vs. Human Recognition of Pain Intensity from Facial Expression on the X-ITE Pain Database.

Prior work on automated methods demonstrated that it is possible to recognize pain intensity from frontal faces in videos, while there is an assumption that humans are very adept at this task compared to machines. In this paper, we investigate whether such an assumption is correct by comparing the results achieved by two human observers with the results achieved by a Random Forest classifier (RFc) baseline model (called RFc-BL) and by three proposed automated models. The first proposed model is a Random Forest classifying descriptors of Action Unit (AU) time series; the second is a modified MobileNetV2 CNN classifying face images that combine three points in time; and the third is a custom deep network combining two CNN branches using the same input as for MobileNetV2 plus knowledge of the RFc. We conduct experiments with X-ITE phasic pain database, which comprises videotaped responses to heat and electrical pain stimuli, each of three intensities. Distinguishing these six stimulation types plus no stimulation was the main 7-class classification task for the human observers and automated approaches. Further, we conducted reduced 5-class and 3-class classification experiments, applied Multi-task learning, and a newly suggested sample weighting method. Experimental results show that the pain assessments of the human observers are significantly better than guessing and perform better than the automatic baseline approach (RFc-BL) by about 1%; however, the human performance is quite poor due to the challenge that pain that is ethically allowed to be induced in experimental studies often does not show up in facial reaction. We discovered that downweighting those samples during training improves the performance for all samples. The proposed RFc and two-CNNs models (using the proposed sample weighting) significantly outperformed the human observer by about 6% and 7%, respectively.

A Baseline for Cross-Database 3D Human Pose Estimation.

Vision-based 3D human pose estimation approaches are typically evaluated on datasets that are limited in diversity regarding many factors, e.g., subjects, poses, cameras, and lighting. However, for real-life applications, it would be desirable to create systems that work under arbitrary conditions ("in-the-wild"). To advance towards this goal, we investigated the commonly used datasets HumanEva-I, Human3.6M, and Panoptic Studio, discussed their biases (that is, their limitations in diversity), and illustrated them in cross-database experiments (for which we used a surrogate for roughly estimating in-the-wild performance). For this purpose, we first harmonized the differing skeleton joint definitions of the datasets, reducing the biases and systematic test errors in cross-database experiments. We further proposed a scale normalization method that significantly improved generalization across camera viewpoints, subjects, and datasets. In additional experiments, we investigated the effect of using more or less cameras, training with multiple datasets, applying a proposed anatomy-based pose validation step, and using OpenPose as the basis for the 3D pose estimation. The experimental results showed the usefulness of the joint harmonization, of the scale normalization, and of augmenting virtual cameras to significantly improve cross-database and in-database generalization. At the same time, the experiments showed that there were dataset biases that could not be compensated and call for new datasets covering more diversity. We discussed our results and promising directions for future work.

Ultrafast coupled charge and spin dynamics in strongly correlated NiO.

Charge excitations across an electronic band gap play an important role in opto-electronics and light harvesting. In contrast to conventional semiconductors, studies of above-band-gap photoexcitations in strongly correlated materials are still in their infancy. Here we reveal the ultrafast dynamics controlled by Hund's physics in strongly correlated photoexcited NiO. By combining time-resolved two-photon photoemission experiments with state-of-the-art numerical calculations, an ultrafast (≲10 fs) relaxation due to Hund excitations and related photo-induced in-gap states are identified. Remarkably, the weight of these in-gap states displays long-lived coherent THz oscillations up to 2 ps at low temperature. The frequency of these oscillations corresponds to the strength of the antiferromagnetic superexchange interaction in NiO and their lifetime vanishes slightly above the Néel temperature. Numerical simulations of a two-band t-J model reveal that the THz oscillations originate from the interplay between local many-body excitations and antiferromagnetic spin correlations.

Electron Transfer Dynamics and Structural Effects in Benzonitrile Monolayers with Tuned Dipole Moments by Differently Positioned Fluorine Atoms.

To understand the influence of the molecular dipole moment on the electron transfer (ET) dynamics across the molecular framework, two series of differently fluorinated, benzonitrile-based self-assembled monolayers (SAMs) bound to Au(111) by either thiolate or selenolate anchoring groups were investigated. Within each series, the molecular structures were the same with the exception of the positions of two fluorine atoms affecting the dipole moment of the SAM-forming molecules. The SAMs exhibited a homogeneous anchoring to the substrate, nearly upright molecular orientations, and the outer interface comprised of the terminal nitrile groups. The ET dynamics was studied by resonant Auger electron spectroscopy in the framework of the core-hole clock method. Resonance excitation of the nitrile group unequivocally ensured an ET pathway from the tail group to the substrate. As only one of the π* orbitals of this group is hybridized with the π* system of the adjacent phenyl ring, two different ET times could be determined depending on the primary excited orbital being either localized at the nitrile group or delocalized over the entire benzonitrile moiety. The latter pathway turned out to be much more efficient, with the characteristic ET times being a factor 2.5-3 shorter than those for the localized orbital. The dynamic ET properties of the analogous thiolate- and selenolate-based adsorbates were found to be nearly identical. Finally and most importantly, these properties were found to be unaffected by the different patterns of the fluorine substitution used in the present study, thus showing no influence of the molecular dipole moment.