Modeling the X-ray - UV Correlations in NGC 7469

We model the correlated X-ray - UV observations of NGC 7469, for which well sampled data in both these bands have been obtained recently in a multiwavelength monitoring campaign. To this end we derive the transfer function in wavelength \ls and time lag \t, for reprocessing hard (X-ray) photons from a point source to softer ones (UV-optical) by an infinite plane (representing a cool, thin accretion disk) located at a given distance below the X-ray source, under the assumption that the X-ray flux is absorbed and emitted locally by the disk as a black body of temperature appropriate to the incident flux. Using the observed X-ray light curve as input we have computed the expected continuum UV emission as a function of time at several wavelengths (\l \l 1315 \AA, \l \l 6962 \AA, \l \l 15000 \AA, \l \l 30000 \AA) assuming that the X-ray source is located one \sc radius above the disk plane, with the mass of the black hole $M$ and the latitude angle $\theta$ of the observer relative to the disk plane as free parameters. We have searched the parameter space of black hole masses and observer azimuthal angles but we were unable to reproduce UV light curves which would resemble, even remotely, those observed. We also explored whether particular combinations of the values of these parameters could lead to light curves whose statistical properties (i.e. the autocorrelation and cross correlation functions) would match those corresponding to the observed UV light curve at \l \l 1315 \AA. Even though we considered black hole masses as large as $10^9$ M$_{\odot}$ no such match was possible. Our results indicate that some of the fundamental assumptions of this model will have to be modified to obtain even approximate agreement between the observed and model X-ray - UV light curves.Comment: 16 pages, 13 figures, ApJ in pres

Modeling hepatitis C micro-elimination among people who inject drugs with direct-acting antivirals in metropolitan Chicago

Hepatitis C virus (HCV) infection is a leading cause of chronic liver disease and mortality worldwide. Direct-acting antiviral (DAA) therapy leads to high cure rates. However, persons who inject drugs (PWID) are at risk for reinfection after cure and may require multiple DAA treatments to reach the World Health Organization’s (WHO) goal of HCV elimination by 2030. Using an agent-based model (ABM) that accounts for the complex interplay of demographic factors, risk behaviors, social networks, and geographic location for HCV transmission among PWID, we examined the combination(s) of DAA enrollment (2.5%, 5%, 7.5%, 10%), adherence (60%, 70%, 80%, 90%) and frequency of DAA treatment courses needed to achieve the WHO’s goal of reducing incident chronic infections by 90% by 2030 among a large population of PWID from Chicago, IL and surrounding suburbs. We also estimated the economic DAA costs associated with each scenario. Our results indicate that a DAA treatment rate of >7.5% per year with 90% adherence results in 75% of enrolled PWID requiring only a single DAA course; however 19% would require 2 courses, 5%, 3 courses and <2%, 4 courses, with an overall DAA cost of $325 million to achieve the WHO goal in metropolitan Chicago. We estimate a 28% increase in the overall DAA cost under low adherence (70%) compared to high adherence (90%). Our modeling results have important public health implications for HCV elimination among U.S. PWID. Using a range of feasible treatment enrollment and adherence rates, we report robust findings supporting the need to address re-exposure and reinfection among PWID to reduce HCV incidence

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits

Nervous systems are information processing networks that evolved by natural selection, whereas very large scale integrated (VLSI) computer circuits have evolved by commercially driven technology development. Here we follow historic intuition that all physical information processing systems will share key organizational properties, such as modularity, that generally confer adaptivity of function. It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections, known as Rent's rule, which is related to the dimensionality of the circuit's interconnect topology and its logical capacity. We show that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent's rule, and exhibit some degree of hierarchical modularity. We further show that the estimated Rent exponent of human brain networks, derived from MRI data, can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species, again suggesting that these principles of nervous system design are highly conserved. For each of these fractal modular networks, the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded. This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs. Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity, resulting in the emergence of homologous principles of economical, fractal and modular design across many different kinds of nervous and computational networks

Efficient Network Reconstruction from Dynamical Cascades Identifies Small-World Topology of Neuronal Avalanches

Cascading activity is commonly found in complex systems with directed interactions such as metabolic networks, neuronal networks, or disease spreading in social networks. Substantial insight into a system's organization can be obtained by reconstructing the underlying functional network architecture from the observed activity cascades. Here we focus on Bayesian approaches and reduce their computational demands by introducing the Iterative Bayesian (IB) and Posterior Weighted Averaging (PWA) methods. We introduce a special case of PWA, cast in nonparametric form, which we call the normalized count (NC) algorithm. NC efficiently reconstructs random and small-world functional network topologies and architectures from subcritical, critical, and supercritical cascading dynamics and yields significant improvements over commonly used correlation methods. With experimental data, NC identified a functional and structural small-world topology and its corresponding traffic in cortical networks with neuronal avalanche dynamics

Band-in-band segregation of multidisperse granular mixtures

Radial and axial segregation is investigated experimentally in polydisperse mixtures of granular materials rotated in a long, partly filled, horizontal cylinder. Radial segregation by size is observed in all polydisperse mixtures. Axial segregation, with smaller-size particles forming bands within bands of larger-size particles, is observed for mixtures of 3 sizes. In addition, bands in ternary mixtures oscillate axially under some conditions. It is observed that the surface flow speeds depend on the particle size, both in the mixed and segregated case. A simple microscopic model based solely on different frictional properties of three particles yields qualitatively similar results

Learning-accelerated discovery of immune-tumour interactions

We present an integrated framework for enabling dynamic exploration of design spaces for cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our framework combines PhysiCell, an open source agent-based simulation platform for cancer and other multicellular systems, and EMEWS, an open source platform for extreme-scale model exploration. We build an agent-based model of immunosurveillance against heterogeneous tumours, which includes spatial dynamics of stochastic tumour–immune contact interactions. We implement active learning and genetic algorithms using high-performance computing workflows to adaptively sample the model parameter space and iteratively discover optimal cancer regression regions within biological and clinical constraints

Hybrid Simulation Development - Is It Just Analytics?

Hybrid simulations can take many forms, often connecting a diverse range of hardware and software components with heterogeneous data sets. The scale of examples is also diverse with both the high-performance computing community using high-performance data analytics (HPDA) to the synthesis of software libraries or packages on a single machine. Hybrid simulation configuration and output analysis is often akin to analytics with a range of dashboards, machine learning, data aggregations and graphical representation. Underpinning the visual elements are hardware, software and data architectures that execute hybrid simulation code. These are wide ranging with few generalized blueprints, methods or patterns of development. This panel will discuss a range of hybrid simulation development approaches and endeavor to uncover possible strategies for supporting the development and coupling of hybrid simulations.U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357

Band-in-band segregation of multidisperse granular mixtures

Radial and axial segregation is investigated experimentally in polydisperse mixtures of granular materials rotated in a long, partly filled, horizontal cylinder. Radial segregation by size is observed in all polydisperse mixtures. Axial segregation, with smaller-size particles forming bands within bands of larger-size particles, is observed for mixtures of 3 sizes. In addition, bands in ternary mixtures oscillate axially under some conditions. It is observed that the surface flow speeds depend on the particle size, both in the mixed and segregated case. A simple microscopic model based solely on different frictional properties of three particles yields qualitatively similar results