Efficient orbital imaging based on ultrafast momentum microscopy and sparsity-driven phase retrieval

We present energy-resolved photoelectron momentum maps for orbital tomography that have been collected with a novel and efficient time-of-flight momentum microscopy setup. This setup is combined with a 0.5 MHz table-top femtosecond extreme-ultraviolet light source, which enables unprecedented speed in data collection and paves the way towards time-resolved orbital imaging experiments in the future. Moreover, we take a significant step forward in the data analysis procedure for orbital imaging, and present a sparsity-driven approach to the required phase retrieval problem, which uses only the number of non-zero pixels in the orbital. Here, no knowledge of the object support is required, and the sparsity number can easily be determined from the measured data. Used in the relaxed averaged alternating reflections algorithm, this sparsity constraint enables fast and reliable phase retrieval for our experimental as well as noise-free and noisy simulated photoelectron momentum map data

Attractor Metadynamics in Adapting Neural Networks

Slow adaption processes, like synaptic and intrinsic plasticity, abound in the brain and shape the landscape for the neural dynamics occurring on substantially faster timescales. At any given time the network is characterized by a set of internal parameters, which are adapting continuously, albeit slowly. This set of parameters defines the number and the location of the respective adiabatic attractors. The slow evolution of network parameters hence induces an evolving attractor landscape, a process which we term attractor metadynamics. We study the nature of the metadynamics of the attractor landscape for several continuous-time autonomous model networks. We find both first- and second-order changes in the location of adiabatic attractors and argue that the study of the continuously evolving attractor landscape constitutes a powerful tool for understanding the overall development of the neural dynamics

Effect of Catalyst Layer and Fuel Utilization on the Durability of Direct Methane SOFC

International audienceSolid oxide fuels cells with and without anodic catalytic layer and specific anodic current collectors were developed in order to be fueled by dry methane. Due to the cell architecture integrating a 0.1wt% Ir-CGO catalyst layer onto the anode, platinum, gold and cupper screen-printed meshes were designed and optimized to ensure efficient current collection between the anode surface and the catalyst membrane. Current density and ageing in H2 and in pure dry CH4 respectively were compared to conventional pressed grid collecting systems. Similar performances were achieved using bulk grids or gold, platinum and copper screen-printed meshes. Operation in pure dry methane is compared with and without the catalytic layer as a function of the fuel utilization. It is demonstrated that long term operation is possible provided that sufficient faradic efficiency is achieved

Multiorbital exciton formation in an organic semiconductor

Harnessing the optoelectronic response of organic semiconductors requires a thorough understanding of the fundamental light-matter interaction that is dominated by the excitation of correlated electron-hole pairs, i.e. excitons. The nature of these excitons would be fully captured by knowing the quantum-mechanical wavefunction, which, however, is difficult to access both theoretically and experimentally. Here, we use femtosecond photoemission orbital tomography in combination with many-body perturbation theory to gain access to exciton wavefunctions in organic semiconductors. We find that the coherent sum of multiple electron-hole pair contributions that typically make up a single exciton can be experimentally evidenced by photoelectron spectroscopy. For the prototypical organic semiconductor buckminsterfullerene (C$_{60}$), we show how to disentangle such multiorbital contributions and thereby access key properties of the exciton wavefunctions including localization, charge-transfer character, and ultrafast exciton formation and relaxation dynamics

Description and evaluation of GMXe: a new aerosol submodel for global simulations (v1)

We present a new aerosol microphysics and gas aerosol partitioning submodel (Global Modal-aerosol eXtension, GMXe) implemented within the ECHAM/MESSy Atmospheric Chemistry model (EMAC, version 1.8). The submodel is computationally efficient and is suitable for medium to long term simulations with global and regional models. The aerosol size distribution is treated using 7 log-normal modes and has the same microphysical core as the M7 submodel (Vignati et al., 2004). <br><br> The main developments in this work are: (i) the extension of the aerosol emission routines and the M7 microphysics, so that an increased (and variable) number of aerosol species can be treated (new species include sodium and chloride, and potentially magnesium, calcium, and potassium), (ii) the coupling of the aerosol microphysics to a choice of treatments of gas/aerosol partitioning to allow the treatment of semi-volatile aerosol, and, (iii) the implementation and evaluation of the developed submodel within the EMAC model of atmospheric chemistry. <br><br> Simulated concentrations of black carbon, particulate organic matter, dust, sea spray, sulfate and ammonium aerosol are shown to be in good agreement with observations (for all species at least 40% of modeled values are within a factor of 2 of the observations). The distribution of nitrate aerosol is compared to observations in both clean and polluted regions. Concentrations in polluted continental regions are simulated quite well, but there is a general tendency to overestimate nitrate, particularly in coastal regions (geometric mean of modelled values/geometric mean of observed data ≈2). In all regions considered more than 40% of nitrate concentrations are within a factor of two of the observations. Marine nitrate concentrations are well captured with 96% of modeled values within a factor of 2 of the observations

Formation of moir\ue9 interlayer excitons in space and time

Moir\ue9 superlattices in atomically thin van der Waals heterostructures hold great promise for extended control of electronic and valleytronic lifetimes1-7, the confinement of excitons in artificial moir\ue9 lattices8-13 and the formation of exotic quantum phases14-18. Such moir\ue9-induced emergent phenomena are particularly strong for interlayer excitons, where the hole and the electron are localized in different layers of the heterostructure19,20. To exploit the full potential of correlated moir\ue9 and exciton physics, a thorough understanding of the ultrafast interlayer exciton formation process and the real-space wavefunction confinement is indispensable. Here we show that femtosecond photoemission momentum microscopy provides quantitative access to these key properties of the moir\ue9 interlayer excitons. First, we elucidate that interlayer excitons are dominantly formed through femtosecond exciton-phonon scattering and subsequent charge transfer\ua0at the interlayer-hybridized Σ valleys. Second, we show that interlayer excitons exhibit a momentum fingerprint that is a direct hallmark of the superlattice moir\ue9 modification. Third, we reconstruct the wavefunction distribution of the electronic part of the exciton and compare the size with the real-space moir\ue9 superlattice. Our work provides direct access to interlayer exciton formation dynamics in space and time and reveals opportunities to study correlated moir\ue9 and exciton physics for the future realization of exotic quantum phases of matter

Tight Glycemic Control After Pediatric Cardiac Surgery in High-Risk Patient Populations

Background—Our previous randomized, clinical trial showed that postoperative tight glycemic control (TGC) for children undergoing cardiac surgery did not reduce the rate of health care–associated infections compared with standard care (STD). Heterogeneity of treatment effect may exist within this population. Methods and Results—We performed a post hoc exploratory analysis of 980 children from birth to 36 months of age at the time of cardiac surgery who were randomized to postoperative TGC or STD in the intensive care unit. Significant interactions were observed between treatment group and both neonate (age ≤30 days; P=0.03) and intraoperative glucocorticoid exposure (P=0.03) on the risk of infection. The rate and incidence of infections in subjects ≤60 days old were significantly increased in the TGC compared with the STD group (rate: 13.5 versus 3.7 infections per 1000 cardiac intensive care unit days, P=0.01; incidence: 13% versus 4%, P=0.02), whereas infections among those \u3e60 days of age were significantly reduced in the TGC compared with the STD group (rate: 5.0 versus 14.1 infections per 1000 cardiac intensive care unit days, P=0.02; incidence: 2% versus 5%, P=0.03); the interaction of treatment group by age subgroup was highly significant (P=0.001). Multivariable logistic regression controlling for the main effects revealed that previous cardiac surgery, chromosomal anomaly, and delayed sternal closure were independently associated with increased risk of infection. Conclusions—This exploratory analysis demonstrated that TGC may lower the risk of infection in children \u3e60 days of age at the time of cardiac surgery compared with children receiving STD. Meta-analyses of past and ongoing clinical trials are necessary to confirm these findings before clinical practice is altered

Nanoscale magnetic imaging using circularly polarized high-harmonic radiation

This work demonstrates nanoscale magnetic imaging using bright circularly polarized high-harmonic radiation. We utilize the magneto-optical contrast of worm-like magnetic domains in a Co/Pd multilayer structure, obtaining quantitative amplitude and phase maps by lensless imaging. A diffraction-limited spatial resolution of 49 nm is achieved with iterative phase reconstruction enhanced by a holographic mask. Harnessing the exceptional coherence of high harmonics, this approach will facilitate quantitative, element-specific, and spatially resolved studies of ultrafast magnetization dynamics, advancing both fundamental and applied aspects of nanoscale magnetism

Recurrent Kernel Machines: Computing with Infinite Echo State Networks

Echo state networks (ESNs) are large, random recurrent neural networks with a single trained linear readout layer. Despite the untrained nature of the recurrent weights, they are capable of performing universal computations on temporal input data, which makes them interesting for both theoretical research and practical applications. The key to their success lies in the fact that the network computes a broad set of nonlinear, spatiotemporal mappings of the input data, on which linear regression or classification can easily be performed. One could consider the reservoir as a spatiotemporal kernel, in which the mapping to a high-dimensional space is computed explicitly. In this letter, we build on this idea and extend the concept of ESNs to infinite-sized recurrent neural networks, which can be considered recursive kernels that subsequently can be used to create recursive support vector machines. We present the theoretical framework, provide several practical examples of recursive kernels, and apply them to typical temporal tasks