Correlation Structures of Correlated Binomial Models and Implied Default Distribution

We show how to analyze and interpret the correlation structures, the conditional expectation values and correlation coefficients of exchangeable Bernoulli random variables. We study implied default distributions for the iTraxx-CJ tranches and some popular probabilistic models, including the Gaussian copula model, Beta binomial distribution model and long-range Ising model. We interpret the differences in their profiles in terms of the correlation structures. The implied default distribution has singular correlation structures, reflecting the credit market implications. We point out two possible origins of the singular behavior.Comment: 16 pages, 7 figure

Collaborative custodianship through collaborative cloud mapping : challenges and opportunities

Collaborative custodianship refers to an arrangement where a number of custodians work together to produce integrated datasets for a spatial data infrastructure (SDI), e.g. local authorities contributing address or street data to a national SDI dataset. Collaborative cloud mapping allows for ubiquitous, convenient, on-demand, configured and tailor-made mapping with resources shared between various entities collaborating on a specific initiative, such as an SDI or for disaster management. This paper presents the results of a workshop in South Africa during which case studies from the Netherlands, Belgium and Austria of collaborative custodianship of address data were presented, and OpenStreetMap as a case study of collaborative cloud mapping. Subsequently, challenges and opportunities for implementing similar initiatives in the context of the South African SDI were debated in break-away sessions. The results from these sessions were analysed using the PESTEL framework

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

We introduce the \texttt{pyunicorn} (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, \texttt{pyunicorn} provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure

Quantitative analysis of spectroscopic Low Energy Electron Microscopy data: High-dynamic range imaging, drift correction and cluster analysis

For many complex materials systems, low-energy electron microscopy (LEEM) offers detailed insights into morphology and crystallography by naturally combining real-space and reciprocal-space information. Its unique strength, however, is that all measurements can easily be performed energy-dependently. Consequently, one should treat LEEM measurements as multi-dimensional, spectroscopic datasets rather than as images to fully harvest this potential. Here we describe a measurement and data analysis approach to obtain such quantitative spectroscopic LEEM datasets with high lateral resolution. The employed detector correction and adjustment techniques enable measurement of true reflectivity values over four orders of magnitudes of intensity. Moreover, we show a drift correction algorithm, tailored for LEEM datasets with inverting contrast, that yields sub-pixel accuracy without special computational demands. Finally, we apply dimension reduction techniques to summarize the key spectroscopic features of datasets with hundreds of images into two single images that can easily be presented and interpreted intuitively. We use cluster analysis to automatically identify different materials within the field of view and to calculate average spectra per material. We demonstrate these methods by analyzing bright-field and dark-field datasets of few-layer graphene grown on silicon carbide and provide a high-performance Python implementation

Direct observation of free-exciton thermalization in quantum-well structures

We report on a direct observation of free-exciton thermalization in quantum-well structures. A narrow energy distribution of free 1s excitons is created in ZnSe-based quantum wells by emission of one LO phonon after optical excitation of the continuum states with picosecond laser pulses. The subsequent relaxation dynamics within the 1s-exciton dispersion is directly monitored by time-resolved studies of the phonon-assisted photoluminescence. It is demonstrated that the free-exciton distribution remains nonthermal for some 100 ps. The observed dynamics is in reasonable agreement with numerical results of a rate-equation model which accounts for the relevant exciton-phonon coupling mechanisms

Third Yearly Activity Report

The calculation work performed during the 3rd project year in WP2 as well as the R&D activities carried out in WP3, WP4 and WP5 are described in this report. In addition, the work dedicated to the project management (WP1) as well as to WP6 regarding the dissemination/communication activities and the education/training program (e.g. the follow-up of the mobility program between different organizations in the consortium, training on simulation tools and activities accomplished by PhD/post-doctoral students) is also reported

Lateralization of mesial temporal lobe epilepsy with chronic ambulatory electrocorticography

OBJECTIVE: Patients with suspected mesial temporal lobe (MTL) epilepsy typically undergo inpatient video-electroencephalography (EEG) monitoring with scalp and/or intracranial electrodes for 1 to 2 weeks to localize and lateralize the seizure focus or foci. Chronic ambulatory electrocorticography (ECoG) in patients with MTL epilepsy may provide additional information about seizure lateralization. This analysis describes data obtained from chronic ambulatory ECoG in patients with suspected bilateral MTL epilepsy in order to assess the time required to determine the seizure lateralization and whether this information could influence treatment decisions. METHODS: Ambulatory ECoG was reviewed in patients with suspected bilateral MTL epilepsy who were among a larger cohort with intractable epilepsy participating in a randomized controlled trial of responsive neurostimulation. Subjects were implanted with bilateral MTL leads and a cranially implanted neurostimulator programmed to detect abnormal interictal and ictal ECoG activity. ECoG data stored by the neurostimulator were reviewed to determine the lateralization of electrographic seizures and the interval of time until independent bilateral MTL electrographic seizures were recorded. RESULTS: Eighty-two subjects were implanted with bilateral MTL leads and followed for 4.7 years on average (median 4.9 years). Independent bilateral MTL electrographic seizures were recorded in 84%. The average time to record bilateral electrographic seizures in the ambulatory setting was 41.6 days (median 13 days, range 0-376 days). Sixteen percent had only unilateral electrographic seizures after an average of 4.6 years of recording. SIGNIFICANCE: About one third of the subjects implanted with bilateral MTL electrodes required >1 month of chronic ambulatory ECoG before the first contralateral MTL electrographic seizure was recorded. Some patients with suspected bilateral MTL seizures had only unilateral electrographic seizures. Chronic ambulatory ECoG in patients with suspected bilateral MTL seizures provides data in a naturalistic setting, may complement data from inpatient video-EEG monitoring, and can contribute to treatment decisions

Whole Exome Sequencing of Patients with Steroid-Resistant Nephrotic Syndrome

BACKGROUND AND OBJECTIVES: Steroid-resistant nephrotic syndrome overwhelmingly progresses to ESRD. More than 30 monogenic genes have been identified to cause steroid-resistant nephrotic syndrome. We previously detected causative mutations using targeted panel sequencing in 30% of patients with steroid-resistant nephrotic syndrome. Panel sequencing has a number of limitations when compared with whole exome sequencing. We employed whole exome sequencing to detect monogenic causes of steroid-resistant nephrotic syndrome in an international cohort of 300 families. DESIGN, SETTING, PARTIIPANTS AND MEASUREMENTS: Three hundred thirty-five individuals with steroid-resistant nephrotic syndrome from 300 families were recruited from April of 1998 to June of 2016. Age of onset was restricted to <25 years of age. Exome data were evaluated for 33 known monogenic steroid-resistant nephrotic syndrome genes. RESULTS: In 74 of 300 families (25%), we identified a causative mutation in one of 20 genes known to cause steroid-resistant nephrotic syndrome. In 11 families (3.7%), we detected a mutation in a gene that causes a phenocopy of steroid-resistant nephrotic syndrome. This is consistent with our previously published identification of mutations using a panel approach. We detected a causative mutation in a known steroid-resistant nephrotic syndrome gene in 38% of consanguineous families and in 13% of nonconsanguineous families, and 48% of children with congenital nephrotic syndrome. A total of 68 different mutations were detected in 20 of 33 steroid-resistant nephrotic syndrome genes. Fifteen of these mutations were novel. NPHS1, PLCE1, NPHS2, and SMARCAL1 were the most common genes in which we detected a mutation. In another 28% of families, we detected mutations in one or more candidate genes for steroid-resistant nephrotic syndrome. CONCLUSIONS: Whole exome sequencing is a sensitive approach toward diagnosis of monogenic causes of steroid-resistant nephrotic syndrome. A molecular genetic diagnosis of steroid-resistant nephrotic syndrome may have important consequences for the management of treatment and kidney transplantation in steroid-resistant nephrotic syndrome

Transfer-free electrical insulation of epitaxial graphene from its metal substrate

High-quality, large-area epitaxial graphene can be grown on metal surfaces but its transport properties cannot be exploited because the electrical conduction is dominated by the substrate. Here we insulate epitaxial graphene on Ru(0001) by a step-wise intercalation of silicon and oxygen, and the eventual formation of a SiO$_2$ layer between the graphene and the metal. We follow the reaction steps by x-ray photoemission spectroscopy and demonstrate the electrical insulation using a nano-scale multipoint probe technique.Comment: Accepted for publication in Nano Letter