Driving steady-state visual evoked potentials at arbitrary frequencies using temporal interpolation of stimulus presentation

Date of Acceptance: 29/10/2015 We thank Renate Zahn for help with data collection. This work was supported by Deutsche Forschungsgemeinschaft (AN 841/1-1, MU 972/20-1). We would like to thank A. Trujillo-Ortiz, R. Hernandez-Walls, A. Castro-Perez and K. BarbaRojo (Universidad Autonoma de Baja California) for making Matlab code for non-sphericity corrections freely available.Peer reviewedPublisher PD

Quality and diagnostic perspectives in laboratory diagnostics

Laboratory diagnostics is a medical discipline playing an important part in patient management. In laboratory medicine meaningful, accurate and precise routine measurements are essential for diagnosis, risk assessment, treatment and follow-up of patients. The contribution of the diagnostic laboratory in the overall diagnostic process is app. 40-60%, depending on the kind of disease status investigated. The diagnostic laboratory uses nowadays more than 1.000 different tests mostly provided by the in vitro diagnostic industry

Quality and diagnostic perspectives in laboratory diagnostics

Laboratory diagnostics is a medical discipline playing an important part in patient management. In laboratory medicine meaningful, accurate and precise routine measurements are essential for diagnosis, risk assessment, treatment and follow-up of patients. The contribution of the diagnostic laboratory in the overall diagnostic process is app. 40-60%, depending on the kind of disease status investigated. The diagnostic laboratory uses nowadays more than 1.000 different tests mostly provided by the in vitro diagnostic industry

Electron-magnon scattering in elementary ferromagnets from first principles: lifetime broadening and band anomalies

We study the electron-magnon scattering in bulk Fe, Co, and Ni within the framework of many-body perturbation theory implemented in the full-potential linearized augmented-plane-wave method. To this end, a $\mathbf{k}$-dependent self-energy ($GT$ self-energy) describing the scattering of electrons and magnons is constructed from the solution of a Bethe-Salpeter equation for the two-particle (electron-hole) Green function, in which single-particle Stoner and collective spin-wave excitations (magnons) are treated on the same footing. Partial self-consistency is achieved by the alignment of the chemical potentials. The resulting renormalized electronic band structures exhibit strong spin-dependent lifetime effects close to the Fermi energy, which are strongest in Fe. The renormalization can give rise to a loss of quasiparticle character close to the Fermi energy, which we attribute to electron scattering with spatially extended spin waves. This scattering is also responsible for dispersion anomalies in conduction bands of iron and for the formation of satellite bands in nickel. Furthermore, we find a band anomaly at a binding energy of 1.5~eV in iron, which results from a coupling of the quasihole with single-particle excitations that form a peak in the Stoner continuum. This band anomaly was recently observed in photoemission experiments. On the theory side, we show that the contribution of the Goldstone mode to the $GT$ self-energy is expected to (nearly) vanish in the long-wavelength limit. We also present an in-depth discussion about the possible violation of causality when an incomplete subset of self-energy diagrams is chosen

Numerical simulation of fog and radiation in complex terrain : results from COST-722

Two high resolution numerical 1D models, namely COBEL and PAFOG, have been adapted to compute a probabilistic fog forecast. Major modifications were made to the COBEL model. It was coupled to the NOAH land surface model to take into account the effects of soil and vegetation and furthermore a parameterization of precipitation was added. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification reveals that the probabilistic forecast can significantly improve the current methods used at Z¨rich u Unique airport. The complex topography in Switzerland further complicates fog forecasting. In order to simulate processes like advection, cold air drainage flows and cold air pooling, the NMM 3D model of NOAA/NCEP is modified and extended with detailed fog microphysics. The resulting 3D fog model runs at a horizontal resolution ofkm and a vertical resolution comparable to the 1D models. First results look very promising and are able to reproduce the spatial distribution of fog as it is seen by satellite. With increasing horizontal resolution of numerical weather prediction models, topographical effects on radiation gain importance. With a newly developed parameterization it is possible to consider slope angle, aspect angle, shadows and restricted sky view on the subgrid scale and with negligible computational costs. Verification reveals that RMS and mean error ofm temperature forecasts are generally improved by 0.5 toK

Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation

Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy noise in training data or domain shift. Recent work has tied these shortcomings to beam search – the de facto standard inference algorithm in NMT – and Eikema & Aziz (2020) propose to use Minimum Bayes Risk (MBR) decoding on unbiased samples instead. In this paper, we empirically investigate the properties of MBR decoding on a number of previously reported biases and failure cases of beam search. We find that MBR still exhibits a length and token frequency bias, owing to the MT metrics used as utility functions, but that MBR also increases robustness against copy noise in the training data and domain shift

Impact of chemical and meteorological boundary and initial conditions on air quality modeling: WRF-Chem sensitivity evaluation for a European domain

This study evaluates the impact of different chemical and meteorological boundary and initial conditions on the state-of-the-art Weather Research and Forecasting (WRF) model with its chemistry extension (WRF-Chem). The evaluation is done for July 2005 with 50km horizontal resolution. The effect of monthly mean chemical boundary conditions derived from the chemical transport model LMDZ-INCA on WRF-Chem is evaluated against the effect of the preset idealized profiles. Likewise, the impact of different meteorological initial and boundary conditions (GFS and Reanalysis II) on the model is evaluated. Pearson correlation coefficient between these different runs range from 0.96 to 1.00. Exceptions exists for chemical boundary conditions on ozone and for meteorological boundary conditions on PM10, where coefficients of 0.90 were obtained. Best results were achieved with boundary and initial conditions from LMDZ-INCA and GFS. Overall, the European simulations show encouraging results for observed air pollutant, with ozone being the most and PM10 being the least satisfyin

Subword Segmentation and a Single Bridge Language Affect Zero-Shot Neural Machine Translation

Zero-shot neural machine translation is an attractive goal because of the high cost of obtaining data and building translation systems for new translation directions. However, previous papers have reported mixed success in zero-shot translation. It is hard to predict in which settings it will be effective, and what limits performance compared to a fully supervised system. In this paper, we investigate zero-shot performance of a multilingual EN$\leftrightarrow${FR,CS,DE,FI} system trained on WMT data. We find that zero-shot performance is highly unstable and can vary by more than 6 BLEU between training runs, making it difficult to reliably track improvements. We observe a bias towards copying the source in zero-shot translation, and investigate how the choice of subword segmentation affects this bias. We find that language-specific subword segmentation results in less subword copying at training time, and leads to better zero-shot performance compared to jointly trained segmentation. A recent trend in multilingual models is to not train on parallel data between all language pairs, but have a single bridge language, e.g. English. We find that this negatively affects zero-shot translation and leads to a failure mode where the model ignores the language tag and instead produces English output in zero-shot directions. We show that this bias towards English can be effectively reduced with even a small amount of parallel data in some of the non-English pairs.Comment: Accepted at WMT 202