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In-stent restenosis, the maladaptive response of a blood vessel to injury caused by the deployment of a stent, is a multiscale problem involving a large number of processes. We describe a Complex Automata Model for in-stent restenosis, coupling a bulk flow, drug diffusion, and smooth muscle cell model, all operating on different time scales. Details of the single scale models and of the coupling interfaces are described, together with first simulation results, obtained with a dedicated software environment for Complex Automata simulations. The results show that the model can reproduce growth trends observed in experimental studies.

A multivariate stochastic model for describing the dynamics of complex non-numerical ensembles, such as observed in Human Immunodeficiency Virus (HIV) genome, is developed. This model is based on principle component analyses for numberized variables. The model coefficients are presented in the terms of deterministic trends with correlated lags. The results indicate that we may use this model in short-term forecast of HIV evolution, for evaluation of HIV drug resistance and for testing and validation of diagnostic expert rules. The model also reproduces the specific shape of the bi-modal distribution for the mutations number

In this paper, we apply the projective integration method to moment models of the Boltzmann-BGK equation and investigate the numerical properties of the resulting scheme. Projective integration is an explicit, asymptotic-preserving scheme that is tailored to problems with a large spectral gap between fast and slow eigenvalues of the model. A spectral analysis of the moment model shows a clear spectral gap and reveals the multi-scale nature of the model. The new scheme overcomes the severe time step constraint of standard explicit schemes like the forward Euler scheme by performing a number of inner iterations and then extrapolating the solution forward in time. The projective integration scheme is non-intrusive and yields fast and accurate solutions, as demonstrated using a 1D shock tube test case. These observations open up many possibilities for further use of the scheme for high-resolution discretizations and different collision models.

We propose a novel variance reduction strategy based on control variables for simulating the averaged equation of a stochastic slow-fast system. In this system, we assume that the fast equation is ergodic, implying the existence of an invariant measure, for every fixed value of the slow variable. The right hand side of the averaged equation contains an integral with respect to this unknown invariant measure, which is approximated by the heterogeneous multiscale method (HMM). The HMM method corresponds to a Markov chain Monte Carlo method in which samples are generated by simulating the fast equation. As a consequence, the variance of the HMM estimator decays slowly. Therefore, we introduce a variance-reduced HMM estimator based on control variables: from the current time HMM estimation, we subtract a second HMM estimator at the previous time step using the exact same seed as the current time HMM estimator. To avoid introducing a bias, we add the previously calculated variance-reduced estimator. We analyze convergence of the proposed estimator and apply it to a linear and nonlinear model problem. ispartof: pages:1255-1266 ispartof: Procedia Computer Science vol:80 pages:1255-1266 ispartof: International Conference on Computational Science 2016, ICCS 2016 location:San Diego date:6 Jun - 8 Jun 2016 status: published

Recommendation algorithms have been researched extensively to help people deal with abundance of information. In recent years, the incorporation of multiple relevance criteria has attracted increased interest. Such multi-criteria recommendation approaches are researched as a paradigm for building intelligent systems that can be tailored to multiple interest indicators of end-users – such as combinations of implicit and explicit interest indicators in the form of ratings or ratings on multiple relevance dimensions. Nevertheless, evaluation of these recommendation techniques in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined that the performance of such algorithms is often dependent on the dataset – and indicate the importance of carrying out careful testing and parameterization. Especially when looking at large scale datasets, it becomes very difficult to deploy evaluation methods that may help in assessing the effect that different system components have to the overall design. In this paper, we study how layered evaluation can be applied for the case of a multi-criteria recommendation service that we plan to deploy for paper recommendation using the Mendeley dataset. The paper introduces layered evaluation and suggests two experiments that may help assess the components of the envisaged system separately. ispartof: pages:1189-1197 ispartof: Procedia Computer Science vol:18 pages:1189-1197 ispartof: 2013 International Conference on Computational Science location:SPAIN, Barcelona date:5 Jun - 7 Jun 2013 status: published

In the globalized world, good products may not be enough to reach potential clients if creative marketing strategies are not well delineated. Public relations are also important when it comes to capture clients attention, making the first contact between them and companies products while being persuasive enough to trust that the company has the right products to fit their needs. A virtual public relations is purposed, combining technology and a human like public relations capable of interacting with potential clients located 180 degrees in front of the installation, by using gestures and sound. Four Microsoft Kinects were used to develop the 180 degrees model for interaction, which allows tracking and recognition of gestures, sound sources, voice commands, extract the face and body of the user and track users positions (heat map).

Online remote visualization and steering of critical weather applications like cyclone tracking are essential for effective and timely analysis by geographically distributed climate science community. A steering framework for controlling the high-performance simulations of critical weather events needs to take into account both the steering inputs of the scientists and the criticality needs of the application including minimum progress rate of simulations and continuous visualization of significant events. In this work, we have developed an integrated user-driven and automated steering framework InSt for simulations, online remote visualization, and analysis for critical weather applications. InSt provides the user control over various application parameters including region of interest, resolution of simulation, and frequency of data for visualization. Unlike existing efforts, our framework considers both the steering inputs and the criticality of the application, namely, the minimum progress rate needed for the application, and various resource constraints including storage space and network bandwidth to decide the best possible parameter values for simulations and visualization.

In many biologically relevant situations, cells of a clonal population show a heterogeneous response upon a common stimulus. The computational analysis of such situations requires the study of cell-cell variability and modeling of heterogeneous cell populations. In this work, we consider populations where the behavior of every single cell can be described by a system of ordinary differential equations. Heterogeneity among individual cells is modeled via differences in parameter values and initial conditions. Both are subject to a distribution function which is part of the cell population model. We present a novel approach to estimate the distribution of parameters and initial conditions from single cell measurements, e.g. flow cytometry and cytometric fluorescence microscopy. Therefore, a maximum likelihood estimator for the distribution is derived. The resulting optimization problem is reformulated via a parameterization of the distribution of parameters and initial conditions to allow the use of convex optimization techniques. To evaluate the proposed method, artificial data from a model of TNF signal transduction are considered. It is shown that the proposed method yields a good estimate of the parameter distributions in case of a limited amount of noise corrupted data. ispartof: pages:1649-1657 ispartof: ICCS 2010 - INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, PROCEEDINGS vol:1 issue:1 pages:1649-1657 ispartof: International Workshop on Computational Stochastics location:Univ Amsterdam, Amsterdam, NETHERLANDS date:31 May - 2 Jun 2010 status: published

Grid infrastructure is a valuable tool for scientific users, but it is characterized by a high level of complexity which makes it difficult for them to quantify their requirements and allocate resources. In this paper, we show that resource trading is a viable and scalable approach for scientific users to consume resources. We propose the use of Grid resource bundles to specify supply and demand combined with a hybrid metaheuristic method to determine the allocation of resources in a market-based approach. We evaluate this through the application domain of scientific workflow execution on the Grid.

6th International Conference on Computational Science (ICCS 2006) -- MAY 28-31, 2006 -- Reading, ENGLAND WOS: 000238417500084 In sub-micron technologies MOSFETs are modeled by complex nonlinear equations. These equations include many process parameters, terminal voltages of the transistor and also the transistor geometries; channel width (W) and length (L) parameters. The designers have to choose the most suitable transistor geometries considering the critical parameters, which determine the DC and AC characteristics of the circuit. Due to the difficulty of solving these complex nonlinear equations, the choice of appropriate geometry parameters depends on designer's knowledge and experience. This work aims to develop a neural network based MOSFET model to find the most suitable channel parameters for TSMC 0.18 mu technology, chosen by the circuit designer. The proposed model is able to find the channel parameters using the input information, which are terminal voltages and the drain current. The training data are obtained by various simulations in the HSPICE design environment with TSMC 0.18 mu m process nominal parameters. The neural network structure is developed and trained in the MATLAB 6.0 program. To observe the utility of proposed MOSFET neural network model it is tested through two basic integrated circuit blocks. Intel Corp, IBM, SGI, Microsoft Res, EPSRC, Springer, ACET Ctr, Univ Reading, SIAM, IMACS, UK e Sci Programme

5th International Conference on Computational Science (ICCS 2005) -- MAY 22-25, 2005 -- Atlanta, GA, WOS: 000230414400096, This paper proposes a Time Delay Dynamic Fuzzy Network (TDDFN) that can be used for tracking and prediction of chaotic time series. TDDFN considered here has unconstrained connectivity and dynamical elements in its fuzzy processing units with time delay state feedbacks. The minimization of a quadratic performance index is considered for trajectory tracking applications. Gradient with respect to model parameters are calculated based on adjoint sensitivity analysis. The computational complexity is significantly less than direct method, but it requires a backward integration capability. For updating model parameters, Broyden-Fletcher-Golfarb-Shanno (BFGS) algorithm that is one of the approximate second order algorithms is used. The TDDFN network is able to predict the Mackey-Glass chaotic time series and gives good results for the nonlinear system identification., Intel Corp, IBM Corp, Microsoft Res, SGI Silicon Graph, Emory Univ, Dpet Math & Comp Sci, Emory Univ, Inst Comparat & Int Studies, Emory Univ, Emory Coll, Emory Univ, Off Provost, Emory Univ, Grad Sch Arts & Sci, Springer

5th International Conference on Computational Science (ICCS 2005) -- MAY 22-25, 2005 -- Atlanta, GA, WOS: 000230414400049, A recent trend in storage systems design is to move disks to a storage area network for direct client access. While several highly scalable file systems have been built using such direct attached disks, they have all been designed for traditional text-based data, and are not well suited for streaming continuous media, i.e., audio and video files, which are characterized by high volumes of data and require strict timing requirements during storage and retrieval. In this paper, we propose a scalable distributed continuous media file system built using Storage Area Network (SAN)-Attached disks, and describe bandwidth and time sensitive read/write procedures for our file system. We present experimental results on the performance of our Linux-based prototype implementation of the file system and show that the file system can provide strict bandwidth guarantees for continuous media streams., Intel, IBM Corp, Microsoft Res, SGI Silicon Graph, Emory Univ, Dept Math & Comp Sci, Emory Univ, Inst Comparat & Int Studies, Emory Univ, Emory Coll, Emory Univ, Off Provost, Emory Univ, Grad Sch Arts & Sci, Springer

A multiscale nonlinear finite element model for analysis and design of the deformation gradient and the magnetic field distribution in Terfenol-D/epoxy thin film device under Transverse Magnetic (TM) mode of operation is developed in this work. A phenomenological constitutive model based on the density of domain switching (DDS) of an ellipsoidal inclusion in unit cell of matrix is implemented. A sub-grid scale homogenization technique is employed to upwind the microstructural information. A general procedure to ensure the solution convergence toward an approximate inertial manifold is reported.

In this paper we present our approach to teaching High Performance Computing at both the undergraduate and graduate level. For undergraduate students, we emphasize the key role of an hands on approach. Parallel computing theory at this stage is kept at minimal level since this knowledge is fundamental, but our main goal for undergraduate students is the required ability to develop real parallel applications. For this reason we spend about one third of the class lectures on the theory and remaining two thirds on programming environments, tools and libraries for development of parallel applications. The availability of widely adopted standards provides us, as teachers of high performance computing, with the opportunity to present parallel algorithms uniformly, to teach how portable parallel software must be developed, how to use parallel libraries etc. When teaching at the graduate level instead, we spend more time on theory, highlighting all of the relevant aspects of parallel computation, models, parallel complexity classes, architectures, message passing and shared memory paradigms etc. In particular, we stress the key points of design and analysis of parallel applications. As a case study, we present to our students the parallelization of a real computational science application, namely a remote sensing SAR (Synthetic Aperture Radar) processor, using both MPI and OpenMP.

In the present work, we describe a fast Motion-Planner for Mobile Robots. Considering robots moving with smoothed trajectories on variable terrains, we have developed an algorithm based on an anisotropic propagation of attracting potentials on a non-Euclidean manifold. The optimal collision-free trajectories are found following the minimum valley of a potential hypersurface embedded in a 4D space. The planner is very flexible: it can be use on a wide class of vehicles with different kinematics and with generic shapes. Because of the latest property, it is also applicable to plan the movements of generic objects (e.g. in assembly workstations in manufacturing industry) as in Piano Mover's problem. Thanks to the underlying Multilayered Cellular Automata (MCA) architecture, it is a distributed approach. This planner turn out to be very fast, allowing to react to the dynamics of the world, evolving toward new solutions every time the environment changes without to be re-initialized.

The paper presents first steps in a project that aims at exploring new modelling and simulation tools (e.g. the ones offered by the Cellular Automata approach) in order to study complex systems and phenomena concerning the economic and social contexts. The specific problem we are presenting in this paper concerns the complex dynamics involved in an asset price model with heterogeneous agents. The dynamics of the global asset market can be studied as a result of local interactions that emerges from the autonomous entities that are involved in the market system. The proposed model allows to describe in a unified way both global and local interactions that characterize economic agents involved in financial market trading. Even if experimental work is still ongoing, first performed steps have allowed us to gather some significant results that allowed us to verify the suitability of the proposed approach.

4th International Conference on Computational Science (ICCS 2004) -- JUN 06-09, 2004 -- Krakow, POLAND, WOS: 000222043200045, We describe, analyze and submit results of a real-time total order multicast protocol developed on a distributed real-time system architecture that consists of hierarchical rings with synchronous packet delivering characteristics. The protocol is structured on and closely interacts with the distributed clock synchronization and the real-time group management modules. The synchronous characteristics of the protocol makes it suitable for hard real-time applications where total ordering is required. The complexity analysis of the protocol is given and the performance results are shown for several scenarios. We show that the developed protocol is correct, scalable and real-time..., Hewlett-Packard, Intel, SGI, ATM, Sun Microsyst, IBM, Polish Airlines LOT, ACC CYFRONET AGH, Inst Comp Sci AGH, Polish Minist Sci Res & Informat Technol, Spring Verlag

We present an image processing technique for the identification of ‘axial lines’ [1] from ridges in isovist fields first proposed by Rana [2,3]. These ridges are formed from the maximum diametric lengths of the individual isovists, sometimes called viewsheds, that make up the isovist fields [4]. We discuss current strengths and weaknesses of the method, and show how it can be implemented easily and effectively.

In this work, we present a numerical treatment for the model of two-phase flow in porous media including specific interfacial area. For numerical discretization we use the cell-centered finite difference (CCFD) method based on the shifting-matrices method which can reduce the time-consuming operations. A new iterative implicit algorithm has been developed to solve the problem under consideration. All advection and advection-like terms that appear in saturation equation and interfacial area equation are treated using upwind schemes. Selected simulation results such as pc–Sw–awn surface, capillary pressure, saturation and specific interfacial area with various values of model parameters have been introduced. The simulation results show a good agreement with those in the literature using either pore network modeling or Darcy scale modeling.

© The Authors. Published by Elsevier B.V. We demonstrate the improvements to an implicit Particle-in-Cell code, iPic3D, on the example of dipolar magnetic field immersed in the flow of the plasma and show the formation of a magnetosphere. We address the problem of modelling multi-scale phenomena during the formation of a magnetosphere by implementing an adaptive sub-cycling technique to resolve the motion of particles located close to the magnetic dipole centre, where the magnetic field intensity is maximum. In addition, we implemented new open boundary conditions to model the inflow and outflow of plasma. We present the results of a global three-dimensional Particle-in-Cell simulation and discuss the performance improvements from the adaptive sub-cycling technique. publisher: Elsevier articletitle: The Formation of a Magnetosphere with Implicit Particle-in-Cell Simulations journaltitle: Procedia Computer Science articlelink: http://dx.doi.org/10.1016/j.procs.2015.05.288 content_type: article copyright: Copyright © 2015 Published by Elsevier B.V. ispartof: pages:1178-1187 ispartof: INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, ICCS 2015 COMPUTATIONAL SCIENCE AT THE GATES OF NATURE vol:51 issue:1 pages:1178-1187 ispartof: 15th Annual International Conference on Computational Science (ICCS) location:Reykjavik Univ, Reykjavik, ICELAND date:1 Jun - 3 Jun 2015 status: published