Decentralized Routing on Spatial Networks with Stochastic Edge Weights

We investigate algorithms to find short paths in spatial networks with stochastic edge weights. Our formulation of the problem of finding short paths differs from traditional formulations because we specifically do not make two of the usual simplifying assumptions: (1) we allow edge weights to be stochastic rather than deterministic; and (2) we do not assume that global knowledge of a network is available. We develop a decentralized routing algorithm that provides en route guidance for travelers on a spatial network with stochastic edge weights without the need to rely on global knowledge about the network. To guide a traveler, our algorithm uses an estimation function that evaluates cumulative arrival probability distributions based on distances between pairs of nodes. The estimation function carries a notion of proximity between nodes and thereby enables routing without global knowledge. In testing our decentralized algorithm, we define a criterion that allows one to discriminate among arrival probability distributions, and we test our algorithm and this criterion using both synthetic and real networks.Comment: 10 pages, 9 figures (some with multiple parts

Scalable Gaussian Process Inference with Stan

Gaussian processes (GPs) are sophisticated distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. We implement two methods for scaling GP inference in Stan: First, a general sparse approximation using a directed acyclic dependency graph. Second, a fast, exact method for regularly spaced data modeled by GPs with stationary kernels using the fast Fourier transform. Based on benchmark experiments, we offer guidance for practitioners to decide between different methods and parameterizations. We consider two real-world examples to illustrate the package. The implementation follows Stan's design and exposes performant inference through a familiar interface. Full posterior inference for ten thousand data points is feasible on a laptop in less than 20 seconds.Comment: 19 pages, 5 figure

Hierarchical Cosmic Shear Power Spectrum Inference

We develop a Bayesian hierarchical modelling approach for cosmic shear power spectrum inference, jointly sampling from the posterior distribution of the cosmic shear field and its (tomographic) power spectra. Inference of the shear power spectrum is a powerful intermediate product for a cosmic shear analysis, since it requires very few model assumptions and can be used to perform inference on a wide range of cosmological models \emph{a posteriori} without loss of information. We show that joint posterior for the shear map and power spectrum can be sampled effectively by Gibbs sampling, iteratively drawing samples from the map and power spectrum, each conditional on the other. This approach neatly circumvents difficulties associated with complicated survey geometry and masks that plague frequentist power spectrum estimators, since the power spectrum inference provides prior information about the field in masked regions at every sampling step. We demonstrate this approach for inference of tomographic shear $E$-mode, $B$-mode and $EB$-cross power spectra from a simulated galaxy shear catalogue with a number of important features; galaxies distributed on the sky and in redshift with photometric redshift uncertainties, realistic random ellipticity noise for every galaxy and a complicated survey mask. The obtained posterior distributions for the tomographic power spectrum coefficients recover the underlying simulated power spectra for both $E$- and $B$-modes.Comment: 16 pages, 8 figures, accepted by MNRA

Cost-based feature selection for network model choice

Selecting a small set of informative features from a large number of possibly noisy candidates is a challenging problem with many applications in machine learning and approximate Bayesian computation. In practice, the cost of computing informative features also needs to be considered. This is particularly important for networks because the computational costs of individual features can span several orders of magnitude. We addressed this issue for the network model selection problem using two approaches. First, we adapted nine feature selection methods to account for the cost of features. We show for two classes of network models that the cost can be reduced by two orders of magnitude without considerably affecting classification accuracy (proportion of correctly identified models). Second, we selected features using pilot simulations with smaller networks. This approach reduced the computational cost by a factor of 50 without affecting classification accuracy. To demonstrate the utility of our approach, we applied it to three different yeast protein interaction networks and identified the best-fitting duplication divergence model.Comment: 34 pages, 6 figure

Frühe Hilfen für Kinder psychisch kranker Eltern - Forschungsergebnisse des Nationalen Zentrums Frühe Hilfen

Die Frühen Hilfen für Eltern mit Kindern von 0 bis 3 sind in Deutschland flächendeckend ausgebaut. Mit speziellen Angeboten für Eltern in Belastungslagen tragen sie dazu bei, Erziehungskompetenzen in Familien zu entwickeln und Kindern ein gesundes und geschütztes Aufwachsen zu ermöglichen. Ein großer Teil der Eltern, die in den Frühen Hilfen unterstützt werden, zeigt Anzeichen einer psychischen Erkrankung. Dies wird in der Praxis Früher Hilfen als besonders schwer zu bewältigende Herausforderung erlebt. Es gibt jedoch bislang nur wenig verlässliches Faktenwissen darüber, wie hoch der Anteil psychisch belasteter Familien in den Angeboten Früher Hilfen tatsächlich ist, welche Bedeutung eine psychische Erkrankung für die Versorgung der Familien hat und wie sich die Hilfesysteme - zum Wohle von Kindern und Eltern - weiter entwickeln müssen. Das Nationale Zentrum Frühe Hilfen (NZFH) hat den flächendeckenden Ausbau der Frühen Hilfen in Deutschland wissenschaftlich begleitet. In diesem Beitrag werden die Befunde zur Versorgung von Familien mit mindestens einem psychisch belasteten Elternteil aus mehreren Studien zusammengetragen, analysiert und diskutiert.In Germany, networks and measures of early childhood intervention (ECI) have been implemented nationwide. By specifically targeting families with multiple psychosocial challenges, ECI contributes to the enhancement of families’ parenting skills, in order to promote equal opportunities for all children to grow up healthy and safe. In many families supported by ECI measures at least one parent shows symptoms of a mental health disorder, which poses a major challenge to ECI practitioners. Nevertheless, there is a lack of valid scientific knowledge about the proportion of young families living with symptoms of mental disorders, the degree to which parents’ psychic burdens affect care in ECI measures and about the cooperation of different care providing systems. The National Center for Early Prevention (NCEP) monitors and evaluates the scaling up of ECI networks and measures in Germany. The present article compiles results of different NCEP studies focusing on parents with mental illness in Early Childhood Intervention. Results are discussed with regard to their relevance for further improving the care systems

The SCLtTAxBCR-ABL transgenic mouse model closely reflects the differential effects of dasatinib on normal and malignant hematopoiesis in chronic phase-CML patients

The second generation tyrosine kinase inhibitor (TKI) dasatinib is a clinically approved drug for chronic myeloid leukemia (CML) as well as Ph+ acute lymphoblastic leukemia. In addition to its antileukemic effects, dasatinib was shown to impact on normal hematopoiesis and cells of the immune system. Due to the fact that the murine in vivo studies so far have not been performed in a chronic-phase CML model under steady-state conditions, our aim was to study the hematopoietic effects of dasatinib (20 mg/kg p.o.) in BCR-ABL expressing SCLtTAxBCR-ABL double transgenic (dtg) mice. Dasatinib robustly antagonized the CML phenotype in vivo in our transgenic mouse model, and this effect included both mature and immature cell populations. However, similar to patients with CML, the fraction of Lin(neg)Sca-1(+)KIT(+)CD48(neg)CD150(+) hematopoietic stem cells was not reduced by dasatinib treatment, suggesting that these cells are not oncogene-addicted. Moreover, we observed differential effects of dasatinib in these animals as compared to wild-type (wt) animals: while granulocytes were significantly reduced in dtg animals, they were increased in wt mice. And Ter119(+) erythrocytic and B220(+) B cells were increased in dtg mice but decreased in wt mice. Finally, while dasatinib induced a shift from CD49b/NK1.1 positive NK cells from the bone marrow to the spleen in wt animals, there was no change in dtg mice. In conclusion, the present mouse model provides a useful tool to study mechanisms of TKI resistance and dasatinib-associated beneficial effects and adverse events.Peer reviewe

Generalized Master Equations for Non-Poisson Dynamics on Networks

The traditional way of studying temporal networks is to aggregate the dynamics of the edges to create a static weighted network. This implicitly assumes that the edges are governed by Poisson processes, which is not typically the case in empirical temporal networks. Consequently, we examine the effects of non-Poisson inter-event statistics on the dynamics of edges, and we apply the concept of a generalized master equation to the study of continuous-time random walks on networks. We show that the equation reduces to the standard rate equations when the underlying process is Poisson and that the stationary solution is determined by an effective transition matrix whose leading eigenvector is easy to calculate. We discuss the implications of our work for dynamical processes on temporal networks and for the construction of network diagnostics that take into account their nontrivial stochastic nature

Simultaneous computed tomography-guided biopsy and radiofrequency ablation of solitary pulmonary malignancy in high-risk patients

Background: In recent years experience has been accumulated in percutaneous radiofrequency ablation (RFA) of lung malignancies in nonsurgical patients. Objectives: In this study, we retrospectively evaluated a simultaneous diagnostic and therapeutic approach including CT-guided biopsy followed immediately by RFA of solitary malignant pulmonary lesions. Methods: CT-guided transthoracic core needle biopsy of solitary pulmonary lesions suspicious for malignancy was performed and histology was proven based on immediate frozen sections. RFA probes were placed into the pulmonary tumors under CT guidance and the ablation was performed subsequently. The procedure-related morbidity was analyzed. Follow-up included a CT scan and pulmonary function parameters. Results: A total of 33 CT-guided biopsies and subsequent RFA within a single procedure were performed. Morbidity of CT-guided biopsy included pulmonary hemorrhage (24%) and a mild pneumothorax (12%) without need for further interventions. The RFA procedure was not aggravated by the previous biopsy. The rate of pneumothorax requiring chest tube following RFA was 21%. Local tumor control was achieved in 77% with a median follow-up of 12 months. The morbidity of the CT-guided biopsy had no statistical impact on the local recurrence rate. Conclusions: The simultaneous diagnostic and therapeutic approach including CT-guided biopsy followed immediately by RFA of solitary malignant pulmonary lesions is a safe procedure. The potential of this combined approach is to avoid unnecessary therapies and to perform adequate therapies based on histology. Taking the local control rate into account, this approach should only be performed in those patients who are unable to undergo or who refuse surgery. Copyright (C) 2012 S. Karger AG, Base

A multinational survey on the infrastructural quality of paediatric intensive care units

Background: The aim of the present study was to assess whether paediatric intensive care units (PICUs) in three central European countries comply with guidelines concerning infrastructure provided by the European Society of Intensive Care Medicine (ESICM). Between July 2016 and May 2017, a survey was conducted based on the ESICM guidelines. The questionnaire was structured into four categories: structural quality, diagnostic/therapeutic equipment, personnel and organization. All PICUs treating paediatric patients in the D-A-CH region [Germany (D), Austria (A) and Switzerland (CH)] were researched through the national societies. A total of 126 PICUs were contacted (D: 106;A: 12;and CH: 8).Results: Eighty-five of 126 PICUs responded (D: 67%;A: 61%;and CH: 100%). A median of 500 patients was treated annually (D: 500;A: 350;and CH: 600) with a median of 12 beds (D: 12;A: 8;and CH: 12). Recommendations regarding infrastructure were met as follows: structural quality 62% in D, 71% in A and 75% in CH;diagnostic/therapeutic equipment: 87% in D, 91% in A and 89% in CH;personnel: 65% in D, 87% in A and 85% in CH;and organization: 75% in D, 73% in A and 88% in CH.Conclusion: sThis survey reveals deficits concerning structural quality in all countries. Furthermore, shortcomings regarding personnel were found in Germany and for organization in Germany and Austria. These issues need to be addressed urgently to further improve treatment quality and patient safety in the future