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Tracking of eye movements is an established measurement for many types of experimental paradigms. More complex and more prolonged visual stimuli have made algorithmic approaches to eye movement event classification the most pragmatic option. A recent analysis revealed that many current algorithms are lackluster when it comes to data from viewing dynamic stimuli such as video sequences. Here we present an event classification algorithm—built on an existing velocity-based approach—that is suitable for both static and dynamic stimulation, and is capable of classifying saccades, post-saccadic oscillations, fixations, and smooth pursuit events. We validated classification performance and robustness on three public datasets: 1) manually annotated, trial-based gaze trajectories for viewing static images, moving dots, and short video sequences, 2) lab-quality gaze recordings for a feature length movie, and 3) gaze recordings acquired under suboptimal lighting conditions inside the bore of a magnetic resonance imaging (MRI) scanner for the same full-length movie. We found that the proposed algorithm performs on par or better compared to state-of-the-art alternatives for static stimulation. Moreover, it yields eye movement events with biologically plausible characteristics on prolonged dynamic recordings. Lastly, algorithm performance is robust on data acquired under suboptimal conditions that exhibit a temporally varying noise level. These results indicate that the proposed algorithm is a robust tool with improved classification accuracy across a range of use cases. The algorithm is cross-platform compatible, implemented using the Python programming language, and readily available as free and open source software from public sources.

Combining graphene and the insulating hexagonal boron nitride (h-BN) into two-dimensional heterostructures is promising for novel, atomically thin electronic nanodevices. A heteroepitaxial growth, in which these materials are grown on top of each other, will be crucial for their scalable device integration. However, during this so-called van der Waals epitaxy, not only the atomically thin substrate itself must be considered but also the influences from the supporting substrate below it. Here, we report not only a substantial difference between the formation of h-BN on single- (SLG) and on bi-layer epitaxial graphene (BLG) on SiC, but also vice versa, that the van der Waals epitaxy of h-BN at growth temperatures well below 1000 °C affects the varying number of graphene layers differently. Our results clearly demonstrate that the additional graphene layer in BLG enhances the distance to the corrugated, carbon-rich interface of the supporting SiC substrate and thereby diminishes its influence on the van der Waals epitaxy, leading to a homogeneous formation of a smooth, atomically thin heterostructure, which will be required for a scalable device integration of 2D heterostructures.

For most non-financial events, risk-neutral outcome probabilities are identical across numeraire currencies. Some events, however, such as elections or referendums, may have an impact on exchange rates. This implies numeraire dependence in risk-neutral outcome probabilities, which leads to different state prices for affected currency pairs. If betting odds available to punters do not reflect these differences, this may give rise to (approximate) arbitrage opportunities. Despite the sizable risk this creates, odds quoted by bookmakers seem to ignore this effect. TOPICS:Currency, performance measurement, risk management

Increasing complexity and volume of research data pose increasing challenges for scientists to manage their data efficiently. At the same time, availability and reuse of research data are becoming more and more important in modern science. The German government has established an initiative to develop research data management (RDM) and to increase accessibility and reusability of research data at the national level, the Nationale Forschungsdateninfrastruktur (NFDI). The NFDI Neuroscience (NFDI-Neuro) consortium aims to represent the neuroscience community in this initiative. Here, we review the needs and challenges in RDM faced by researchers as well as existing and emerging solutions and benefits, and how the NFDI in general and NFDI-Neuro specifically can support a process for making these solutions better available to researchers. To ensure development of sustainable research data management practices, both technical solutions and engagement of the scientific community are essential. NFDI-Neuro is therefore focusing on community building just as much as on improving the accessibility of technical solutions.

In neuroimaging, voxel-wise encoding models are a popular tool to predict brain activity elicited by a stimulus. To evaluate the accuracy of these predictions across multiple voxels, one can choose between multiple quality metrics. However, each quality metric requires specifying auxiliary parameters such as the number and selection criteria of voxels, whose influence on model validation is unknown. In this study, we systematically vary these parameters and observe their effects on three common quality metrics of voxel-wise encoding models in two open datasets of 3- and 7-Tesla BOLD fMRI activity elicited by musical stimuli. We show that such auxiliary parameters not only exert substantial influence on model validation, but also differ in how they affect each quality metric. Finally, we give several recommendations for validating voxel-wise encoding models that may limit variability due to different numbers of voxels, voxel selection criteria, and magnetic field strengths.

We develop a simulation algorithm that generates multivariate samples with exact means, covariances, and multivariate skewness. If required for financial applications, absence of arbitrage can be ensured. Potential applications include the simulation of risk factors for the risk management of financial institutions. We use the Kollo measure of multivariate skewness, which is more informative for these applications than the Mardia skewness previously used in this context.

Multimatch-gaze is a Python package for computing the similarity of eye-movement sequences, so called scan paths. Scan paths are the trace of eye-movements in space and time, usually captured with eye tracking devices. Scan path similarity is a measure that is used in a variety of disciplines ranging from cognitive psychology, medicine, and marketing to human-machine interfaces. In addition to quantifying position and order of a series of eye-movements, comparing their temporo-spatial sequence adds an insightful dimension to the traditional analysis of eye tracking data. It reveals commonalities and differences of viewing behavior within and between observers, and is used to study how people explore visual information. For example, scan path comparisons are used to study analogy-making (French, Glady, & Thibaut, 2017), visual exploration and imagery (Johansson, Holsanova, & Holmqvist, 2006), habituation in repetitive visual search (Burmester & Mast, 2010), or spatial attention allocation in dynamic scenes (Mital, Smith, Hill, & Henderson, 2011). The method is applied within individuals as a measure of change (Burmester & Mast, 2010), or across samples to study group differences (French et al., 2017).Therefore, in recent years, interest in the study of eye movement sequences has sparked the development of novel methodologies and algorithms to perform scan path comparisons. However, many of the contemporary scan path comparison algorithms are implemented inclosed-source, non-free software such as Matlab.Multimatch-gaze is a Python-based reimplementation of the MultiMatch toolbox for scanpath comparison, originally developed by Jarodzka, Holmqvist, & Nyström (2010) and implemented by Dewhurst et al. (2012) in Matlab. This algorithm represents scan paths asgeometrical vectors in a two-dimensional space: Any scan path is built up of a coordinate vector sequence in which the start and end position of vectors represent fixations, and the vectors represent saccades. Two such vector sequences are, after optional simplification based on angular relations and amplitudes of saccades, compared on the five dimensions “vector shape”, “vector length (amplitude)”, “vector position”, “vector direction”, and “fixation duration” for a multidimensional similarity evaluation.This reimplementation in Python aims at providing an accessible, documented, and tested open source alternative to the existing MultiMatch toolbox. The algorithm is an established tool for scan path comparison (N. C. Anderson, Anderson, Kingstone, & Bischof, 2015),and improved availability aids adoption in a broader research community. multimatch-gaze is available from its Github repository and as the Python package multimatch-gaze via pip install multimatch-gaze. The module contains the same functionality as the original Matlab toolbox, that is, scan path comparison with optional simplification according to userdefined thresholds, and it provides this functionality via a command line interface or a PythonAPI. Data for scan path comparison can be supplied as nx3 fixation vectors with columns corresponding to x-coordinates, y-coordinates, and duration of the fixation in seconds (as for the original Matlab toolbox). Alternatively, multimatch-gaze can natively read in event detection output produced by REMoDNaV (Dar, Wagner, & Hanke, 2019), a velocity-based eye movement classification algorithm written in Python. For REMoDNaV-based input, users can additionally specify whether smooth pursuit events in the data should be kept in the scan path or discarded.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

Previously published results indicate that the accuracy of decoding visual orientation from 7 Tesla fMRI data of V1 peaks at spatial acquisition resolutions that are routinely accessible with more conventional 3 Tesla scanners. This study directly compares the decoding performance between a 3 Tesla and a 7 Tesla dataset that were acquired using the same stimulation paradigm by applying an identical analysis procedure. The results indicate that decoding models built on 3 Tesla data are comparatively impaired. Moreover, we found no evidence for a strong coupling of BOLD signal change magnitude or temporal signal to noise ratio (tSNR) with decoding performance. Direct enhancement of tSNR via multiband fMRI acquisition at the same resolution did not translate into improved decoding performance. Additional voxel selection can boost 3 Tesla decoding performance to the 7 Tesla level only at a 3 mm acquisition resolution. In both datasets the BOLD signal available for orientation decoding is spatially broadband, but, consistent with the size of the BOLD point-spread-function, decoding models at 3 Tesla utilize spatially coarser image components.

We investigated the neural basis of conjoined processing of color and spatial frequency with functional magnetic resonance imaging (fMRI). A multivariate classification algorithm was trained to differentiate between either isolated color or spatial frequency differences, or between conjoint differences in both feature dimensions. All displays were presented in a singleton search task, avoiding confounds between conjunctive feature processing and search difficulty that arose in previous studies contrasting single feature and conjunction search tasks. Based on patient studies, we expected the right temporo-parietal junction (TPJ) to be involved in conjunctive feature processing. This hypothesis was confirmed in that only conjoined color and spatial frequency differences, but not isolated feature differences could be classified above chance level in this area. Furthermore, we could show that the accuracy of a classification of differences in both feature dimensions was superadditive compared to the classification accuracies of isolated color or spatial frequency differences within the right TPJ. These data provide evidence for the processing of feature conjunctions, here color and spatial frequency, in the right TPJ.

The fabrication of nanowires with axial multiquantum wells or disks presenting a homogeneous size and shape distribution along the whole stack is still an unresolved challenge, despite being essential for narrowing their light emission bandwidth. In this work we demonstrate that the commonly observed change in the shape of the disks along the stacking direction proceeds in a systematic, predictable way. High- resolution transmission electron microscopy of stacked (In,Ga)N quantum discs embedded in GaN nanowires with diameters of ∼40 nm and lengths of ∼700 nm and finite element method calculations show that, contrary to what is normally assumed, this change is not related to the radial growth of the nanowires, which is shown to be negligible, but to the strain relaxation of the whole active region. A simple model is proposed to account for the experimental observations. The model assumes that each disk reaches an equilibrium shape that minimizes the overall energy of the system, given by the sum of the surface and strain energies of the disk itself and the barrier below. The strain state of the barrier is affected by the presence of the disk buried directly below in a way that depends on its shape. This gives rise to a cumulative process, which makes the aspect ratio of each quantum disk to be smaller compared to the disk grown just before, in qualitative agreement with the experimental observations. The obtained results imply that strain relaxation is an important factor to bear in mind for the design of multiquantum disks with controlled shape along the stacking direction in any lattice mismatched nanowire system.

A decade after it was shown that the orientation of visual grating stimuli can be decoded from human visual cortex activity by means of multivariate pattern classification of BOLD fMRI data, numerous studies have investigated which aspects of neuronal activity are reflected in BOLD response patterns and are accessible for decoding. However, it remains inconclusive what the effect of acquisition resolution on BOLD fMRI decoding analyses is. The present study is the first to provide empirical ultra high-field fMRI data recorded at four spatial resolutions (0.8mm, 1.4mm, 2mm, and 3mm isotropic voxel size) on this topic - in order to test hypotheses on the strength and spatial scale of orientation discriminating signals. We present detailed analysis, in line with predictions from previous simulation studies, about how the performance of orientation decoding varies with different acquisition resolutions. Moreover, we also examine different spatial filtering procedures and its effects on orientation decoding. Here we show that higher-resolution scans with subsequent down-sampling or low-pass filtering yield no benefit over scans natively recorded in the corresponding lower resolution regarding decoding accuracy. The orientation-related signal in the BOLD fMRI data is spatially broadband in nature, includes both high spatial frequency components, as well as large-scale biases previously proposed in the literature. Moreover, we found above chance-level contribution from large draining veins to orientation decoding. Acquired raw data were publicly released to facilitate further investigation.

Van der Waals heterostructures combining hexagonal boron nitride (h-BN) and graphene offer many potential advantages, but remain difficult to produce as continuous films over large areas. In particular, the growth of h-BN on graphene has proven to be challenging due to the inertness of the graphene surface. Here we exploit a scalable molecular beam epitaxy based method to allow both the h-BN and graphene to form in a stacked heterostructure in the favorable growth environment provided by a Ni(111) substrate. This involves first saturating a Ni film on MgO(111) with C, growing h-BN on the exposed metal surface, and precipitating the C back to the h-BN/Ni interface to form graphene. The resulting laterally continuous heterostructure is composed of a top layer of few-layer thick h-BN on an intermediate few-layer thick graphene, lying on top of Ni/MgO(111). Examinations by synchrotronbased grazing incidence diffraction, X-ray photoemission spectroscopy, and UV-Raman spectroscopy reveal that while the h-BN is relaxed, the lattice constant of graphene is significantly reduced, likely due to nitrogen doping. These results illustrate a different pathway for the production of h-BN/graphene heterostructures, and open a new perspective for the large-area preparation of heterosystems combining graphene and other 2D or 3D materials. Funding Agencies|Leibniz Association; Alexander von Humboldt Foundation

Multivariate pattern classification methods have been successfully applied to decode orientation of visual grating stimuli from BOLD fMRI activity recorded in human visual cortex (Kamitani and Tong, 2005; Haynes and Rees, 2005) [12] , [10] . Though there has been extensive research investigating the true spatial scale of the orientation specific signals (Op de Beeck, 2010; Swisher et al., 2010; Alink et al., 2013; Freeman et al., 2011, 2013) [2] , [15] , [1] , [4] , [5] , it remained inconclusive what spatial acquisition resolution is required, or is optimal, for decoding analyses. The research article entitled “The effect of acquisition resolution on orientation decoding from V1 BOLD fMRI at 7 T” Sengupta et al. (2017) [14] studied the effect of spatial acquisition resolution and also analyzed the strength and spatial scale of orientation discriminating signals. In this article, for the first time, we present empirical ultra high-field fMRI data, obtained as a part of the aforementioned study, which were recorded at four spatial resolutions (0.8 mm, 1.4 mm, 2 mm, and 3 mm isotropic voxel size) for orientation decoding in visual cortex. The dataset is compliant with the BIDS (Brain Imaging Data Structure) format, and freely available from the OpenfMRI portal (dataset accession number: http://openfmri.org/dataset/ds000113c ds000113c).

Scalable fabrication of atomically thin hexagonal boron nitride (h-BN) films is highly important for the future implementation of this two-dimensional dielectric in various applications. In this contribution, we report on systematical growth experiments of few-layer thick h-BN, synthesized by molecular beam epitaxy (MBE), on crystalline Ni films deposited on MgO(111). The samples are studied using scanning electron microscopy, atomic force microscopy, Raman spectroscopy, and synchrotron-based grazing incidence diffraction. Growth parameters for the realization of continuous h-BN films with high structural quality are presented and discussed. Additionally, our study also aims at gaining insight into the nucleation and growth behavior of h-BN on the Ni surface, which is crucial for achieving further improvement in terms of crystal quality and thickness homogeneity of h-BN layers grown not only by MBE but also by other methods.

Semiconductor-ferromagnet GaAs-Fe3Si core-shell nanowires were grown by molecular beam epitaxy and analyzed by scanning and transmission electron microscopy, X-ray diffraction, Mossbauer spectroscopy, and magnetic force microscopy. We obtained closed and smooth Fe3Si shells with a crystalline structure that show ferromagnetic properties with magnetizations along the nanowire axis (perpendicular to the substrate). Such nanobar magnets are promising candidates to enable the fabrication of new forward-looking devices in the field of spintronics and magnetic recording.

The neural substrates of feature binding are an old, yet still not completely resolved problem. While patient studies suggest that posterior parietal cortex is necessary for feature binding, imaging evidence has been inconclusive in the past. These studies compared visual feature and conjunction search to investigate the neural substrate of feature conjunctions. However, a common problem of these comparisons was a confound with search difficulty. To circumvent this confound, we directly investigated the localized representation of features (color and spatial frequency) and feature conjunctions in a single search task by using multivariate pattern analysis at high field strength (7T). In right superior parietal lobule, we found evidence for the representation of feature conjunctions that could not be explained by the summation of individual feature representations and thus indicates conjoined processing of color and spatial frequency.

Corporate sports sponsorship is an important part of many companies’ corporate communication strategy. In this paper, we take the example of major football tournaments to show that sponsorship indeed affects the sponsor's (stock) market value. We find a statistically significant impact of football results (at an individual match level) of the seven most important football nations at European and World Championships on the stock prices of jersey sponsors. In general, the more important a match and the less expected its result, the higher its impact. In addition, we find a form of ‘mere-exposure’ effect which is difficult to reconcile with the efficient markets hypothesis.

A decade after it was shown that the orientation of visual grating stimuli can be decoded from human visual cortex activity by means of multivariate pattern classification of BOLD fMRI data, numerous studies have investigated which aspects of neuronal activity are reflected in BOLD response patterns and are accessible for decoding. However, it remains inconclusive what the effect of acquisition resolution on BOLD fMRI decoding analyses is. The present study is the first to provide empirical ultra high-field fMRI data recorded at four spatial resolutions (0.8 mm, 1.4 mm, 2 mm, and 3 mm isotropic voxel size) on this topic — in order to test hypotheses on the strength and spatial scale of orientation discriminating signals. We present detailed analysis, in line with predictions from previous simulation studies, about how the performance of orientation decoding varies with different acquisition resolutions. Moreover, we also examine different spatial filtering procedures and its effects on orientation decoding. Here we show that higher-resolution scans with subsequent down-sampling or low-pass filtering yield no benefit over scans natively recorded in the corresponding lower resolution regarding decoding accuracy. The orientation-related signal in the BOLD fMRI data is spatially broadband in nature, includes both high spatial frequency components, as well as large-scale biases previously proposed in the literature. Moreover, we found above chance-level contribution from large draining veins to orientation decoding. Acquired raw data were publicly released to facilitate further investigation.

Here we extend the information on the structure of the core stimulus of the studyforrest project (http://studyforrest.org) with a description of semantic conflict in the “Forrest Gump” movie. Three observers annotated the movie independently regarding episodes with portrayal of lies, irony or sarcasm. We present frequency statistics, and inter-observer reliability measures that qualify and quantify semantic conflict in the stimulus. While the number of identified events is limited, this annotation nevertheless enriches the knowledge about the complex high-level structure of this stimulus, and can help to evaluate its utility for future studies, and the usability of the existing brain imaging data regarding this aspect of cognition.

Here we present an update of the studyforrest (http://studyforrest.org) dataset that complements the previously released functional magnetic resonance imaging (fMRI) data for natural language processing with a new two-hour 3 Tesla fMRI acquisition while 15 of the original participants were shown an audio-visual version of the stimulus motion picture. We demonstrate with two validation analyses that these new data support modeling specific properties of the complex natural stimulus, as well as a substantial within-subject BOLD response congruency in brain areas related to the processing of auditory inputs, speech, and narrative when compared to the existing fMRI data for audio-only stimulation. In addition, we provide participants' eye gaze location as recorded simultaneously with fMRI, and an additional sample of 15 control participants whose eye gaze trajectories for the entire movie were recorded in a lab setting—to enable studies on attentional processes and comparative investigations on the potential impact of the stimulation setting on these processes.

The studyforrest (http://studyforrest.org) dataset is likely the largest neuroimag-ing dataset on natural language and story processing publicly available today. In this article, along with a companion publication, we present an update of this dataset that extends its scope to vision and multi-sensory research. 15 participants of the original cohort volunteered for a series of additional studies: a clinical examination of visual function, a standard retinotopic mapping procedure, and a localization of higher visual areas — such as the fusiform face area. The combination of this update, the previous data releases for the dataset, and the companion publication, which includes neuroimaging and eye tracking data from natural stimulation with a motion picture, form an extremely versatile and comprehensive resource for brain imaging research — with almost six hours of functional neuroimaging data across five different stimulation paradigms for each participant. Furthermore, we describe employed paradigms and present results that document the quality of the data for the purpose of characterising major properties of participants’ visual processing stream.

Here we present an update of the studyforrest (http://studyforrest.org) dataset that complements the previously released functional magnetic resonance imaging (fMRI) data for natural language processing with a new two-hour 3Tesla fMRI acquisition while 15 of the original participants were shown an audio-visual version of the stimulus motion picture. We demonstrate with two validation analyses that these new data support modeling specific properties of the complex natural stimulus, as well as a substantial within-subject BOLD response congruency in brain areas related to the processing of auditory inputs, speech, and narrative when compared to the existing fMRI data for audio-only stimulation. In addition, we provide participants’ eye gaze location as recorded simultaneously with fMRI, and an additional sample of 15 control participants whose eye gaze trajectories for the entire movie were recorded in a lab setting — to enable studies on attentional processes and comparative investigations on the potential impact of the stimulation setting on these processes.

Many areas of neuroscience are now critically dependent on computational tools to help understand the large volumes of data being created. Furthermore, computer models are increasingly being used to help predict and understand the function of the nervous system. Many of these computations are complex and often cannot be concisely reported in the methods section of a scientific article. In a few areas there are widely used software packages for analysis (e.g., SPM, FSL, AFNI, BrainVoyager, FreeSurfer in neuroimaging) or simulation (e.g. NEURON, NEST, Brian). However, we often write new computer programs to solve specific problems in the course of our research. Some of these programs may be relatively small scripts that help analyze all of our data, and these rarely get described in papers. As authors, how best can we maximize the chances that other scientists can reproduce our computations or reuse our methods on their data? Is our research reproducible? Our article lists practical suggestions to maximise the reproducibility of our work.

Little is known about the cortical regions involved in representing task-related content in preparation for visual task performance. Here we used representational similarity analysis (RSA) to investigate the BOLD response pattern similarity between task relevant and task irrelevant feature dimensions during conjunction viewing and target template maintenance prior to visual search. Subjects were cued to search for a spatial frequency (SF) or orientation of a Gabor grating and we measured BOLD signal during cue and delay periods before the onset of a search display. RSA of delay period activity revealed that widespread regions in frontal, posterior parietal, and occipitotemporal cortices showed general representational differences between task relevant and task irrelevant dimensions (e.g., orientation vs. SF). In contrast, RSA of cue period activity revealed sensory-related representational differences between cue images (regardless of task) at the occipital pole and additionally in the frontal pole. Our data show that task and sensory information are represented differently during viewing and during target template maintenance, and that task relevance modulates the representation of visual information across the cortex.

We develop a ROM simulation algorithm that generates multivariate samples with exact means, covariances, and multivariate skewness. If required for financial applications, absence of arbitrage can be ensured. We use the Kollo measure of multivariate skewness, which is more informative than the Mardia skewness previously used in this context. Potential applications include the simulation of risk factors for the risk management of financial institutions.

Employed for a long time in optical disks, Ge2Sb2Te5 is nowadays considered the most promising material also for phase-change nonvolatile memories. In the current paper, Ge–Sb–Te phase-change mater...

This work focuses on homoepitaxial growth of β-Ga2O3 on (100)-oriented substrates during molecular beam epitaxy. It provides a comprehensive study on the growth mode by combining in situ with ex situ tools. In situ reflection high-energy electron diffraction (RHEED) indicates 2D layer-by-layer mode accompanied by (1 × 1) surface reconstruction. The homoepitaxial layers are grown pseudomorphic with the substrate without in-plane strain as probed by in-plane azimuthal RHEED and out-of-plane synchrotron-based high resolution x-ray diffraction. In contrast to the substrate, stacking faults and twin domains are present in the layer.

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

Summary Introduction The purpose of the study was to compare the efficacy on rat skull defects of two bone growth factors derived from the GDF-5 family. Material and methods The study was conducted on 17 adult Wistar rats. On each animal, two symmetrical 6-mm wide, full-thickness, skull defects were carried out in the parietal regions. In 15 out of 17 animals, both experimental defects were filled by the implants. In the group I ( n =2), both defects were left empty for control. The 15 other rats were divided into 3 groups: In group II ( n =5), a collagen sponge was implanted. In group III ( n =5), a collagen sponge impregnated with rhGDF-5 (the genuine dimeric form) was implanted. In group IV ( n =5), a collagen sponge impregnated with rhGDF-5C465A (a monomeric form of GDF-5) was implanted. All animals were sacridiced at 8 weeks. The harvested specimens were processed for contact radiography and standard histological examination. The quantitative results were assessed with a semi-quantitative histological scoring system. Results One animal in the group II was excluded because it died of unknown reasons. In group I, no bone healing was observed in the defects. In group II, no bone healing was observed in 4 out of 10 defects, and partial bone healing was observed in 5 out of 10 defects. In group III, partial bone healing was also observed in 3 out of 8 defects and complete bone healing in 4 out of 8 defects. In group IV, partial bone healing was observed in 8 out of 10 defects and complete bone healing in 2 out of 10 defects. Conclusion Bone healing was improved in all treated groups. Further studies are necessary to determine the optimal formulation of these composite implants.

We describe a procedure to consider the impact of lateral positional correlation of SiGe nanoscale islands onto the diffuse scattering within a high resolution x-ray diffraction experiment. The samples have been grown by means of liquid phase epitaxy which provides monodisperse island ensembles containing up to 109 equivalent objects. It is shown that a proper numerical simulation of the x-ray diffuse scattering pattern requires careful consideration of the partial coherence of x rays. An appropriate numerical procedure consists of coherent summation over sample areas with lateral dimensions as given by the coherence properties of the radiation and subsequent incoherent summation over a large enough number of such areas. For the given case an effective lateral coherence length of ≈1μm has been used, which is derived taking into account also the detector resolution. The according simulation is in good agreement with the experimentally observed x-ray diffuse intensity pattern.

X-ray diffuse scattering is used to probe size, shape, and strain distribution of self-organized SiGe∕Si(001) islands, which were grown by liquid phase epitaxy. The SiGe islands show a truncated pyramidal shape with {111} side facets and a (001) top facet and they are highly uniform in size. With an averaged island base width of 130nm and a corresponding height of 65nm all the islands have a characteristic geometrical base-to-height aspect ratio of about 2. X-ray diffuse scattering is used to locally probe the elastically relaxed regions inside the island apex and the strongly strained regions near the substrate-island interface. It is found that the geometrical aspect ratio has a large impact on the x-ray diffuse intensity pattern in reciprocal space. By performing corresponding kinematical x-ray simulations this fact can be utilized to determine the aspect ratio with high sensitivity.

We report on structural investigations on multi-fold stacks of In 0.6 Ga 0.4 As quantum dots (QDs) embedded within a GaAs matrix. The structures have been grown by means of metalorganic chemical vapor deposition. Cross-sectional transmission electron micrographs prove a pronounced vertical QD correlation, while plan-view images do not show any lateral ordering. Grazing incidence diffraction within various crystallographic zones clearly reveal a QD shape with a four-fold symmetry. Comparing dynamical scattering simulations, which base on finite element calculations for the strain field show that the shape of the QDs can be described by a prism with a flat top on a thin wetting layer. The mean lateral dot distance evaluated from diffuse X-ray scattering agrees well with the QD density estimated from TEM plan-view images.

In the present paper we report on structural investigations of fivefold In0.2Ga0.8As/GaAs superlattices which have been grown by means of metal organic chemical vapor deposition on vicinal GaAs(001) substrates. Cross-sectional transmission electron micrographs exhibit an initially flat and nonfaceted grooved surface, while step bunching occurs during subsequent growth stages with an inclined vertical inheritance approximately 45° off the (001) direction. A reconstructed sample cross section on the base of high resolution x-ray diffraction data qualitatively confirms the local morphology proved by transmission electron microscopy. Moreover, a line shape analysis of diffusely scattered intensity using Gauss profiles indicates a lateral short range ordering of step bunches.

We have investigated the x-ray intensity distribution around 220 reciprocal lattice point in case of grazing incidence diffraction at SiGe nanoscale free-standing islands grown on Si(001) substrate by LPE. Experiments and computer simulations based on the distorted wave Born approximation utilizing the results of elasticity theory obtained by FEM modelling have been carried out. The data reveal fine structure in the distribution of scattered radiation with well-pronounced maxima and complicated fringe pattern. Explanation of the observed diffraction phenomena in their relation to structure and morphology of the island is given. An optimal island model including its shape, size and Ge spatial distribution was elaborated.

We highlight the great potential of x-ray diffuse scattering for the characterization of mesoscopic structures. The mesoscopic length scale plays a fundamental role at semiconductors, since structures with spatial dimensions in the nanometer regime exhibit quantum size effects. Also the driving forces during epitaxial growth of these structures are most relevant at mesoscopic length scales and they may lead to self-organization processes in that structures can form spontaneously during epitaxial growth. One of the most interesting features are quantum dots (QDs) since they exhibit zero-dimensional electronic properties and thus show an impressive potential for applications in optoelectronic devices. X-ray diffuse scattering is an excellent tool to investigate shape, size, and positional correlation and to characterize the strain field inside and in the vicinity of such self-organized mesoscopic structures. It provides non-destructive structural information over the entire mesoscopic length scale. X-ray scattering is complementary to direct imaging techniques in that it gives a statistical average over large ensembles with high strain sensitivity. The application of different scattering techniques for the structural characterization of mesoscopic structures will be demonstrated. For the example of SiGe islands on Si (001) substrate it will be shown that x-ray diffuse scattering is able to reveal details of germanium distribution in such islands. In a second example the investigation of the asymmetric shape and size of InP QDs that are grown on (001) In0.48Ga0.52 P/GaAs is described. The influence of positional correlation on the diffuse scattering will be discussed.

Coherent phonons (CPs) generated by laser pulses on the femtosecond scale have been proposed as a means to achieve ultrafast, nonthermal switching in phase-change materials such as ${\mathrm{Ge}}_{2}{\mathrm{Sb}}_{2}{\mathrm{Te}}_{5}$ (GST). Here we use ultrafast optical pump pulses to induce coherent acoustic phonons and stroboscopically measure the corresponding lattice distortions in GST using 100-ps x-ray pulses from the European Synchrotron Radiation Facility (ESRF) storage ring. A linear-chain model provides a good description of the observed changes in the diffraction signal; however, the magnitudes of the measured shifts are too large to be explained by thermal effects alone, implying the presence of excited-state effects in addition to temperature-driven expansion. The information on the movement of atoms during the excitation process can lead to greater insight into the possibilities of using CP-induced phase transitions in GST.

We derive no-arbitrage bounds for expected excess returns to generate scenarios used in financial applications. The bounds allow to distinguish three regions: one where arbitrage opportunities will never exist, a second where arbitrage may be present, and a third, where arbitrage opportunities will always exist. No-arbitrage bounds are derived in closed form for a given covariance matrix using the least possible number of scenarios. Empirical examples illustrate the practical potential of knowing these bounds. (authors' abstract)

Here we present a high-resolution functional magnetic resonance (fMRI) dataset – 20 participants recorded at high field strength (7 Tesla) during prolonged stimulation with an auditory feature film (“Forrest Gump”). In addition, a comprehensive set of auxiliary data (T1w, T2w, DTI, susceptibility-weighted image, angiography) as well as measurements to assess technical and physiological noise components have been acquired. An initial analysis confirms that these data can be used to study common and idiosyncratic brain response patterns to complex auditory stimulation. Among the potential uses of this dataset are the study of auditory attention and cognition, language and music perception, and social perception. The auxiliary measurements enable a large variety of additional analysis strategies that relate functional response patterns to structural properties of the brain. Alongside the acquired data, we provide source code and detailed information on all employed procedures – from stimulus creation to data analysis. In order to facilitate replicative and derived works, only free and open-source software was utilized.

Grazing incidence X-ray diffraction (GID) was employed to probe the structure of atomically thin carbon layers on SiC(0001): a so-called buffer layer (BL) with a $6(\sqrt{3}\times\sqrt{3})$R30$^\circ$ periodicity, a monolayer graphene (MLG) on top of the BL, and a bilayer graphene (BLG). The GID analysis was complemented by Raman spectroscopy. The lattice parameter of each layer was measured with high precision by GID. The BL possesses a different lattice parameter and corrugation when it is uncovered or beneath MLG. Our results demonstrate that the interfacial BL is the main responsible for the strain in MLG. By promoting its decoupling from the substrate via intercalation, it turns into graphene, leading to a simultaneous relaxation of the MLG and formation of a quasi-free-standing BLG., Comment: Accepted at Phys. Rev. B Rapid Communications. Supplementary material included

(In,Ga)N insertions embedded in self-assembled GaN nanowires are of current interest for applications in solid state light emitters. Such structures exhibit a notoriously broad emission band. We use cathodoluminescence spectral imaging in a scanning electron microscope and micro-photoluminescence spectroscopy on single nanowires to learn more about the mechanisms underlying this emission. We observe a shift of the emission energy along the stack of six insertions within single nanowires that may be explained by compositional pulling. Our results also corroborate reports that the localization of carriers at potential fluctuations within the insertions plays a crucial role for the luminescence of these nanowire based emitters. Furthermore, we resolve contributions from both structural and point defects in our measurements., Comment: 10 pages, 7 figures

Batch distillation processes are widely used in the chemical industry. In this work, we consider the optimization of such processes by simulated annealing. Although this method is stochastically in nature, it has two well-known advantages: it can be readily connected to highly sophisticated simulation codes and it converges towards a global optimum. According to the characteristics of batch distillation operation we propose to use a two-step computation approach. A feasible strategy (admissible control) will be searched for in the first step and it will be optimized in the second step. The approach has been applied to a model of a pilot batch distillation plant. These results show the potential of the method for developing optimal operation strategies for batch chemical processes.

Highly regular islands of strained Si1-xGex have been grown on Si (001) by liquid phase epitaxy (LPE). The islands themselves are {111}-faceted truncated pyramids with a narrow base-width size distribution and coherent behaviour. In this study we report on the state of lateral ordering within a single layer of Si1-xGex islands. Grazing incidence small-angle x-ray scattering (GISAXS) was used to investigate the ordering as a function of island coverage. At very low island coverage the array behaves like a two-dimensional liquid exhibiting an increased number of island dimers which orient along the island base diagonal 100. On increasing the coverage these dimers develop into extended chains of islands oriented along 100. At high coverage there is strong ordering along both the 100 and the 110 directions. The self-organised ordering can be explained by two possible mechanisms: (i) that the observed ordering is driven by the anisotropy of the elastic constants where short-range order along the soft 100 directions is expected, and (ii) that there is an underlying two-dimensional ripple pattern in the wetting layer aligned along the 100 directions. At later stages of growth this pattern transforms into pseudomorphic islands.

We have investigated pseudomorphic ${\mathrm{Si}}_{1\ensuremath{-}x}{\mathrm{Ge}}_{x}$ layers grown on Si (001) by means of liquid-phase epitaxy. The layers have been grown in Stranski-Krastanov growth mode and consist of coherent {111}-faceted truncated pyramids exhibiting unique shape and a narrow size distribution. Samples with different spatial island densities, i.e., different mean island-island distances, have been investigated by means of grazing incidence small-angle x-ray scattering (GISAXS) and atomic force microscopy (AFM). We found initial stages of developing ordering of the island array at very low island densities, which is manifested as island dimers oriented along the island base diagonal 〈100〉. At medium densities extended chains of islands along 〈100〉 appear; however, also increased ordering along 〈110〉 could be observed. At high island densities there is strong ordering along both 〈100〉 and 〈110〉 directions. There are interesting differences in the GISAXS intensity profiles along these two directions. The close relationship between GISAXS and AFM power spectra is discussed.