title: Advanced Radial Basis Functions Mesh Morphing for High Fidelity Fluid-Structure Interaction with Known Movement of the Walls: Simulation of an Aortic Valve

text: High fidelity Fluid-Structure Interaction (FSI) can be tackled by means of non-linear Finite Element Models (FEM) suitable to capture large deflections of structural parts interacting with fluids and by means of detailed Computational Fluid Dynamics (CFD). High fidelity is gained thanks to the spatial resolution of the computational grids and a key enabler to have a proper exchange of information between the structural solver and the fluid one is the management of the interfaces. A class of applications consists in problems where the complex movement of the walls is known in advance or can be computed by FEM and has to be transferred to the CFD solver. The aforementioned approach, known also as one-way FSI, requires effective methods for the time marching adaption of the computation grid of the CFD model. A versatile and well established approach consists in a continuum update of the mesh that is regenerated so to fit the evolution of the moving walls. In this study, an innovative method based on Radial Basis Functions (RBF) mesh morphing is proposed, allowing to keep the same mesh topology suitable for a continuum update of the shape. A set of key configurations are exactly guaranteed whilst time interpolation is adopted between frames. The new framework is detailed and then demonstrated, adopting as a reference the established approach based on remeshing, for the study of a Polymeric-Prosthetic Heart Valve (P-PHV).


title: Development and Application of the Statistically Similar Representative Volume Element for Numerical Modelling of Multiphase Materials

text: Modern aerospace, automotive and construction industries rely on materials with non-homogeneous properties like composites or multiphase structures. Such materials offer a lot of advantages, but they also require application of advanced numerical models of exploitation condition, which are of high importance for designers, architects and engineers. However, computational cost is one of the most important problems in this approach, being very high and sometimes unacceptable. In this paper we propose approach based on Statistically Similar Representative Volume Element (SSRVE), which is generated by combination of isogeometric analysis and optimization methods. The proposed solution significantly decreases computational cost of complex multiscale simulations and simultaneously maintains high reliability of solvers. At first, the motivation of the work is described in introduction, which is followed by general idea of the SSRVE as a modelling technique. Afterwards, examples of generated SSRVEs based on two different cases are given and passed further to numerical simulations of exploitation conditions. The results obtained from these calculations are used in the model predicting gradients of material properties, which are crucial results for discussion on uniqueness of the proposed solution. Additionally, some aspects of computational cost reduction are discussed, as well.


title: Foundations for Workflow Application Scheduling on D-Wave System

text: Many scientific processes and applications can be represented in the standardized form of workflows. One of the key challenges related to managing and executing workflows is scheduling. As an NP-hard problem with exponential complexity it imposes limitations on the size of practically solvable problems. In this paper, we present a solution to the challenge of scheduling workflow applications with the help of the D-Wave quantum annealer. To the best of our knowledge, there is no other work directly addressing workflow scheduling using quantum computing. Our solution includes transformation into a Quadratic Unconstrained Binary Optimization (QUBO) problem and discussion of experimental results, as well as possible applications of the solution. For our experiments we choose four problem instances small enough to fit into the annealer’s architecture. For two of our instances the quantum annealer finds the global optimum for scheduling. We thus show that it is possible to solve such problems with the help of the D-Wave machine and discuss the limitations of this approach.


title: An Ontological Approach to Knowledge Building by Data Integration

text: This paper discusses the uncertainty in the automation of knowledge building from heterogeneous raw datasets. Ontologies play a critical role in such a process by providing a well consolidated support to link and semantically integrate datasets via interoperability, as well as semantic enrichment and annotations. By adopting Semantic Web technology, the resulting ecosystem is fully machine consumable. However, while the manual alignment of concepts from different vocabularies is reasonable at a small scale, fully automatic mechanisms are required once the target system scales up, leading to a significant uncertainty.


title: A Hybrid Solution Method for the Multi-Service Location Set Covering Problem

text: The Multi-Service Location Set Covering Problem is an extension of the well-known Set Covering Problem. It arises in practical applications where a set of physical locations need to be equipped with services to satisfy demand within a certain area, while minimizing costs. In this paper we formulate the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, apply the hybrid framework of the D-Wave quantum annealer to solve it, and investigate the feasibility of this approach. To improve the often suboptimal initial solutions found on the D-Wave system, we develop a hybrid quantum/classical optimization algorithm that starts from the seed solution and iteratively creates small subproblems that are more efficiently solved on the D-Wave but often still converge to feasible and improved solutions of the original problem. Finally we suggest some opportunities for increasing the accuracy and performance of our algorithm.


title: A Simple Stochastic Process Model for River Environmental Assessment Under Uncertainty

text: We consider a new simple stochastic single-species population dynamics model for understanding the flow-regulated benthic algae bloom in uncertain river environment: an engineering problem. The population dynamics are subject to regime-switching flow conditions such that the population is effectively removed in a high-flow regime while it is not removed at all in a low-flow regime. A focus in this paper is robust and mathematically rigorous statistical evaluation of the disutility by the algae bloom under model uncertainty. We show that the evaluation is achieved if the optimality equation derived from a dynamic programming principle is solved, which is a coupled system of non-linear and non-local degenerate elliptic equations having a possibly discontinuous coefficient. We show that the system is solvable in continuous viscosity and asymptotic senses. We also show that its solutions can be approximated numerically by a convergent finite difference scheme with a demonstrative example.


docid: ltbviybt

title: Node Classification in Complex Social Graphs via Knowledge-Graph Embeddings and Convolutional Neural Network

text: The interactions between humans and their environment, comprising living and non-living entities, can be studied via Social Network Analysis (SNA). Node classification, as well as community detection tasks, are still open research problems in SNA. Hence, SNA has become an interesting and appealing domain in Artificial Intelligence (AI) research. Immanent facts about social network structures can be effectively harnessed for training AI models in a bid to solve node classification and community detection problems in SNA. Hence, crucial aspects such as the individual attributes of spatial social actors, and the underlying patterns of relationship binding these social actors must be taken into consideration in the course of analyzing the social network. These factors determine the nature and dynamics of a given social network. In this paper, we have proposed a unique framework, Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN), for studying and extracting meaningful facts from social network structures to aid in node classification as well as community detection tasks. Our proposition utilizes an edge sampling approach for exploiting features of the social graph, via learning the context of each actor with respect to neighboring actors/nodes, with the goal of generating vector-space embedding per actor. Successively, these relatively low-dimensional vector embeddings are fed as input features to a downstream classifier for classification tasks about the social graph/network. Herein RLVECN has been trained, tested, and evaluated on real-world social networks.


title: Supermodeling: The Next Level of Abstraction in the Use of Data Assimilation

text: Data assimilation (DA) is a key procedure that synchronizes a computer model with real observations. However, in the case of overparametrized complex systems modeling, the task of parameter-estimation through data assimilation can expand exponentially. It leads to unacceptable computational overhead, substantial inaccuracies in parameter matching, and wrong predictions. Here we define a Supermodel as a kind of ensembling scheme, which consists of a few sub-models representing various instances of the baseline model. The sub-models differ in parameter sets and are synchronized through couplings between the most sensitive dynamical variables. We demonstrate that after a short pretraining of the fully parametrized small sub-model ensemble, and then training a few latent parameters of the low-parameterized Supermodel, we can outperform in efficiency and accuracy the baseline model matched to data by a classical DA procedure.


title: Multi Axes Sliding Mesh Approach for Compressible Viscous Flows

text: To compute flows around a body with a rotating or movable part like a tiltrotor aircraft, the multi axes sliding mesh approach has been proposed. This approach is based on the unstructured moving grid finite volume method, which has adopted the space-time unified domain for control volume. Thus, it can accurately express such a moving mesh. However, due to the difficulty of mesh control in viscous flows and the need to maintain the stability of computation, it is restricted to only inviscid flows. In this paper, the multi axes sliding mesh approach was extended to viscous flows to understand detailed flow phenomena around a complicated moving body. The strategies to solve several issues not present in inviscid flow computations are described. To show the validity of the approach in viscous flows, it was applied to the flow field of a sphere in uniform flow. Multiple domains that slide individually were placed around the sphere, and it was confirmed that the sliding mesh did not affect the flow field. The usability of the approach is expected to be applied to practical viscous flow computations.


title: Microtubule Biomechanics and the Effect of Degradation of Elastic Moduli

text: The present study aims at quantifying the effect of mechanical degradation of microtubules on their electro-elastic response. A three-dimensional continuum-based hollow cylindrical domain of a microtubule has been considered in this work. A fully coupled electro-mechanical model has been developed for conducting the comparative analysis considering three different cases, viz., no degradation, 50% degradation and 90% degradation of elastic modulus of the microtubule. The microtubule has been subjected to dynamic forces adopted from the commonly used loading-unloading conditions in nanoindentation experiments. The results show that the degradation of microtubules significantly influences their electro-elastic response when subjected to externally applied forces. The transient response of the model in terms of induced displacement, electric potential and volumetric strain has also been analyzed for different magnitudes of mechanical degradation. The modelling study presented here represents a more accurate electro-mechanical model compared to the classical mechanical model for quantifying the effects of mechanical transductions on microtubules biomechanics.


title: Learning Ergodic Averages in Chaotic Systems

text: We propose a physics-informed machine learning method to predict the time average of a chaotic attractor. The method is based on the hybrid echo state network (hESN). We assume that the system is ergodic, so the time average is equal to the ergodic average. Compared to conventional echo state networks (ESN) (purely data-driven), the hESN uses additional information from an incomplete, or imperfect, physical model. We evaluate the performance of the hESN and compare it to that of an ESN. This approach is demonstrated on a chaotic time-delayed thermoacoustic system, where the inclusion of a physical model significantly improves the accuracy of the prediction, reducing the relative error from 48% to 1%. This improvement is obtained at the low extra cost of solving a small number of ordinary differential equations that contain physical information. This framework shows the potential of using machine learning techniques combined with prior physical knowledge to improve the prediction of time-averaged quantities in chaotic systems.


title: New Hybrid Quantum Annealing Algorithms for Solving Vehicle Routing Problem

text: We introduce new hybrid algorithms, DBSCAN Solver and Solution Partitioning Solver, which use quantum annealing for solving Vehicle Routing Problem (VRP) and its practical variant: Capacitated Vehicle Routing Problem (CVRP). Both algorithms contain important classical components, but we also present two other algorithms, Full QUBO Solver and Average Partitioning Solver, which can be run only on a quantum processing unit (without CPU) and were prototypes which helped us develop better hybrid approaches. In order to validate our methods, we run comprehensive tests using D-Wave’s Leap framework on well-established benchmark test cases as well as on our own test scenarios built based on realistic road networks. We also compared our new quantum and hybrid methods with classical algorithms - well-known metaheuristics for solving VRP and CVRP. The experiments indicate that our hybrid methods give promising results and are able to find solutions of similar or even better quality than the tested classical algorithms.


title: Learn More from Context: Joint Modeling of Local and Global Attention for Aspect Sentiment Classification

text: Aspect sentiment classification identifies the sentiment polarity of the target that appears in a sentence. The key point of aspect sentiment classification is to capture valuable information from sentence. Existing methods have acknowledged the importance of the relationship between the target and the sentence. However, these approaches only focus on the local information of the target, such as the positional relationship and the semantic similarity between the words in a sentence and the target. Moreover, the global information of the interaction of words in sentence and their influence on the final prediction of sentiment polarity are ignored in related works. To tackle this issue, the present paper proposes Joint Modeling of Local and Global Attention (LGAJM), with the following two aspects: (1) the study develops a position-based attention network concentrating on the local information of semantic similarity and position information of the target. (2) In order to fetch global information, such as context information and interaction between words in sentences, the self-attention network is introduced. Besides, a BiGRU-based gating mechanism is proposed to weight the outputs of these two attention networks. The model is evaluated on two datasets: laptop and restaurant from SemEval 2014. Experimental results demonstrate the high effectiveness of the proposed method in aspect sentiment classification.


title: Ontology-Driven Edge Computing

text: The paper is devoted to new aspects of ontology-based approach to control the behavior of Edge Computing devices. Despite the ontology-driven solutions are widely used to develop adaptive mechanisms to the specifics of the Internet of Things (IoT) and ubiquitous computing ecosystems, the problem of creating withal full-fledged, easy to handle and efficient ontology-driven Edge Computing still remains unsolved. We propose the new approach to utilize ontology reasoning mechanism right on the extreme resource-constrained Edge devices, not in the Fog or Cloud. Thanks to this, on-the-fly modifying of device functions, as well as ad-hoc monitoring of intermediate data processed by the device and interoperability within the IoT are enabled and become more intelligent. Moreover, the smart leverage of on-demand automated transformation of Machine-to-Machine to Human-Centric IoT becomes possible. We demonstrate the practical usefulness of our solution by the implementation of ontology-driven Smart Home edge device that helps locating the lost things.


title: On the Complementary Role of Data Assimilation and Machine Learning: An Example Derived from Air Quality Analysis

text: We present a new formulation of the error covariances that derives from ensembles of model simulations, which captures terrain-dependent error correlations, without the prohibitive cost of ensemble Kalman filtering. Error variances are obtained from innovation variances empirically related to concentrations using a large data set. We use a k-fold cross-validation approach to estimate the remaining parameters. We note that by minimizing the cross-validation cost function, we obtain the optimal parameters for an optimal Kalman gain. Combined with the innovation variance consistent with the sum of observation and background error variances in observation space, yield a scheme that estimates the true error statistics, thus minimizing the true analysis error. Overall, this yield a new error statistics formulation and estimation out-performs the older optimum interpolation scheme using isotropic covariances with optimized covariance parameters. Yet, the analysis scheme is computationally comparable to optimum interpolation and can be used in real-time operational applications. These new error statistics comes as data-driven models, were we use validation techniques that are common to machine learning. We argue that the error statistics could benefit from a machine learning approach, while the air quality model and analysis scheme derives from physics and statistics.


title: Lessons Learned in a Decade of Research Software Engineering GPU Applications

text: After years of using Graphics Processing Units (GPUs) to accelerate scientific applications in fields as varied as tomography, computer vision, climate modeling, digital forensics, geospatial databases, particle physics, radio astronomy, and localization microscopy, we noticed a number of technical, socio-technical, and non-technical challenges that Research Software Engineers (RSEs) may run into. While some of these challenges, such as managing different programming languages within a project, or having to deal with different memory spaces, are common to all software projects involving GPUs, others are more typical of scientific software projects. Among these challenges we include changing resolutions or scales, maintaining an application over time and making it sustainable, and evaluating both the obtained results and the achieved performance.


title: Monolithic Arbitrary Lagrangian–Eulerian Finite Element Method for a Multi-domain Blood Flow–Aortic Wall Interaction Problem

text: In this paper, an arbitrary Lagrangian–Eulerian (ALE) finite element method in the monolithic approach is developed for a multi-domain blood flow–aortic wall interaction problem with multiple moving interfaces. An advanced fully discrete ALE-mixed finite element approximation is defined to solve the present fluid–structure interaction (FSI) problem in the cardiovascular environment, in which two fields of structures are involved with two fields of fluid flow, inducing three moving interfaces in between for the interactions. Numerical experiments are carried out for a realistic cardiovascular problem with the implantation of vascular stent graft to demonstrate the strength of our developed ALE-mixed finite element method.


title: Teaching About the Social Construction of Reality Using a Model of Information Processing

text: This paper proposes leveraging the recent fusing of Big Data, computer modeling, and complexity science for teaching students about complexity. The emergence and evolution of information orders, including social structural orders, is a product of social interactions. A simple computational model of how social interaction leads to emergent cultures is proposed for teaching students the mechanisms of complexity’s development. A large-scale poker game is also described where students learn the theory and principles behind the model in the course of play. Using the model and game can help instructors achieve learning objectives that include understanding the importance of diversity, how multiple realities can exist simultaneously, and why majority group members are usually unaware of their privileges.


title: Solving Non-linear Elasticity Problems by a WLS High Order Continuation

text: In this paper, a high order mesh-free continuation for nonlinear elasticity problems is presented. This proposal consists to introduce the Weighted Least Squares (WLS) in a High Order Continuation (HOC). The WLS has been employed to create shape functions using a local support domain. The HOC permits to transform the nonlinear problems in a succession of linear problems of the same tangent matrix. A strong formulation of the problem is adopted to avoid the numerical integration and mesh generation. In this work, a numerical study has been conducted in nonlinear elasticity problems in order to study the behaviour and stability of the proposed approach. Several examples are investigated numerically in order to demonstrate the robustness and efficiency of the proposed approach. This proposed approach has shown its efficiency in management of complex geometries and irregular nodal distributions with respect to other approaches.


docid: iemzdfyf

title: Computational Science vs. Zombies

text: Computational Science attempts to solve scientific problems through the design and application of mathematical models. Researchers and research teams need domain knowledge, along with skills in computing and, increasingly, data science expertise. We have been working to draw students into STEM, Computational Science and Data Science through our Team Zombie outreach program. The program leads the students through a scenario of a disease outbreak in their local area, which turns out to a potential zombie apocalypse. They become part of Team Zombie, a multi-disciplinary response team that investigates the outbreak; models the spread and potential interventions; works towards cures or vaccines; and provides options for detection and monitoring. Throughout the activities we make reference to real-world situations where the techniques are applied. Our program has engaged students from primary school through to university level, raising awareness of the range of approaches to problem solving through models and simulations. We hope this will inspire students to choose courses and careers in computational and data science.


title: Massively Parallel Stencil Strategies for Radiation Transport Moment Model Simulations

text: The radiation transport equation is a mesoscopic equation in high dimensional phase space. Moment methods approximate it via a system of partial differential equations in traditional space-time. One challenge is the high computational intensity due to large vector sizes (1 600 components for P39) in each spatial grid point. In this work, we extend the calculable domain size in 3D simulations considerably, by implementing the StaRMAP methodology within the massively parallel HPC framework NAStJA, which is designed to use current supercomputers efficiently. We apply several optimization techniques, including a new memory layout and explicit SIMD vectorization. We showcase a simulation with 200 billion degrees of freedom, and argue how the implementations can be extended and used in many scientific domains.


title: The Quantization Algorithm Impact in Hydrological Applications: Preliminary Results

text: A computationally efficient surface to groundwater coupled hydrological model is being developed based on the Extended Cellular Automata (XCA) formalism. The three-dimensional unsaturated flow model was the first to be designed and implemented in OpenCAL. Here, the response of the model with respect to small variations of the quantization threshold has been assessed, which is the OpenCAL’s quantization algorithm’s parameter used for evaluating cell’s steady state condition. An unsaturated flow test case was considered where the elapsed times of both the non quantized execution and the execution run by setting the quantization threshold to zero (with respect to the moist content variable) were already evaluated. The model response has been assessed in terms of both accuracy and computational performance in the case of an MPI/OpenMP hybrid execution. Results have pointed out that a very good tradeoff between accuracy and computational performance can be achieved, allowing for a considerable speed-up of the model against a very limited loss of precision.


title: Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Model

text: Porous media and conduit coupled systems are heavily used in a variety of areas such as groundwater system, petroleum extraction, and biochemical transport. A coupled dual porosity Stokes model has been proposed to simulate the fluid flow in a dual-porosity media and conduits coupled system. Data assimilation is the discipline that studies the combination of mathematical models and observations. It can improve the accuracy of mathematical models by incorporating data, but also brings challenges by increasing complexity and computational cost. In this paper, we study the application of data assimilation methods to the coupled dual porosity Stokes model. We give a brief introduction to the coupled model and examine the performance of different data assimilation methods on a finite element implementation of the coupled dual porosity Stokes system. We also study how observations on different variables of the system affect the data assimilation process.


title: Decoupled and Energy Stable Time-Marching Scheme for the Interfacial Flow with Soluble Surfactants

text: In this work, we develop an efficient energy stable scheme for the hydrodynamics coupled phase-field surfactant model with variable densities. The thermodynamically consistent model consists of two Cahn–Hilliard–type equations and incompressible Navier–Stokes equation. We use two scalar auxiliary variables to transform nonlinear parts in the free energy functional into quadratic forms, and then they can be treated efficiently and semi-implicitly. A splitting method based on pressure stabilization is used to solve the Navier–Stokes equation. By some subtle explicit-implicit treatments to nonlinear convection and stress terms, we construct a first-order energy stable scheme for the two-phase system with soluble surfactants. The developed scheme is efficient and easy-to-implement. At each time step, computations of phase-field variables, the velocity and pressure are decoupled. We rigorously prove that the proposed scheme is unconditionally energy stable. Numerical results confirm that our scheme is accurate and energy stable.


title: Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

text: Dynamic data-driven simulation (DDDS) incorporates real-time measurement data to improve simulation models during model run-time. Data assimilation (DA) methods aim to best approximate model states with imperfect measurements, where particle Filters (PFs) are commonly used with discrete-event simulations. In this paper, we study three critical conditions of DA using PFs: (1) the time interval of iterations, (2) the number of particles and (3) the level of actual and perceived measurement errors (or noises), and provide recommendations on how to strategically use data assimilation for DDDS considering these conditions. The results show that the estimation accuracy in DA is more constrained by the choice of time intervals than the number of particles. Good accuracy can be achieved without many particles if the time interval is sufficiently short. An over estimation of the level of measurement errors has advantages over an under estimation. Moreover, a slight over estimation has better estimation accuracy and is more responsive to system changes than an accurate perceived level of measurement errors.


title: Uncertainty Quantification for Multiscale Fusion Plasma Simulations with VECMA Toolkit

text: Within VECMAtk platform we perform Uncertainty Quantification (UQ) for multiscale fusion plasmas simulations. The goal of VECMAtk is to enable modular and automated tools for a wide range of applications to archive robust and actionable results. Our aim in the current paper is to incorporate suitable features to build UQ workflow over the existing fusion codes and to tackle simulations on high performance parallel computers.


title: Accelerated Gaussian Convolution in a Data Assimilation Scenario

text: Machine Learning algorithms try to provide an adequate forecast for predicting and understanding a multitude of phenomena. However, due to the chaotic nature of real systems, it is very difficult to predict data: a small perturbation from initial state can generate serious errors. Data Assimilation is used to estimate the best initial state of a system in order to predict carefully the future states. Therefore, an accurate and fast Data Assimilation can be considered a fundamental step for the entire Machine Learning process. Here, we deal with the Gaussian convolution operation which is a central step of the Data Assimilation approach and, in general, in several data analysis procedures. In particular, we propose a parallel algorithm, based on the use of Recursive Filters to approximate the Gaussian convolution in a very fast way. Tests and experiments confirm the efficiency of the proposed implementation.


title: A Three-Dimensional, One-Field, Fictitious Domain Method for Fluid-Structure Interactions

text: In this article we consider the three-dimensional numerical simulation of Fluid-Structure Interaction (FSI) problems involving large solid deformations. The one-field Fictitious Domain Method (FDM) is introduced in the framework of an operator splitting scheme. Three-dimensional numerical examples are presented in order to validate the proposed approach: demonstrating energy stability and mesh convergence; and extending two dimensional benchmarks from the FSI literature. New three dimensional benchmarks are also proposed.


docid: vipysuku

title: Quantum Hopfield Neural Networks: A New Approach and Its Storage Capacity

text: At the interface between quantum computing and machine learning, the field of quantum machine learning aims to improve classical machine learning algorithms with the help of quantum computers. Examples are Hopfield neural networks, which can store patterns and thereby are used as associative memory. However, the storage capacity of such classical networks is limited. In this work, we present a new approach to quantum Hopfield neural networks with classical inputs and outputs. The approach is easily extendable to quantum inputs or outputs. Performance is evaluated by three measures of error rates, introduced in this paper. We simulate our approach and find increased storage capacity compared to classical networks for small systems. We furthermore present classical results that indicate an increased storage capacity for quantum Hopfield neural networks in large systems as well.


docid: oidnevdg

title: Intrusive Polynomial Chaos for CFD Using OpenFOAM

text: We present the formulation and implementation of a stochastic Computational Fluid Dynamics (CFD) solver based on the widely used finite volume library - OpenFOAM. The solver employs Generalized Polynomial Chaos (gPC) expansion to (a) quantify the uncertainties associated with the fluid flow simulations, and (b) study the non-linear propagation of these uncertainties. The aim is to accurately estimate the uncertainty in the result of a CFD simulation at a lower computational cost than the standard Monte Carlo (MC) method. The gPC approach is based on the spectral decomposition of the random variables in terms of basis polynomials containing randomness and the unknown deterministic expansion coefficients. As opposed to the mostly used non-intrusive approach, in this work, we use the intrusive variant of the gPC method in the sense that the deterministic equations are modified to directly solve for the (coupled) expansion coefficients. To this end, we have tested the intrusive gPC implementation for both the laminar and the turbulent flow problems in CFD. The results are in accordance with the analytical and the non-intrusive approaches. The stochastic solver thus developed, can serve as an alternative to perform uncertainty quantification, especially when the non-intrusive methods are significantly expensive, which is mostly true for a lot of stochastic CFD problems.


title: Path Markup Language for Indoor Navigation

text: Indoor navigation is critical in many tasks such as firefighting, emergency medical response, and SWAT response, where GPS signals are not available. Prevailing approaches such as beacons, radio signal triangulation, SLAM, and IMU methods are either expensive or impractical in extreme conditions, e.g. poor visibility and sensory drifting. In this study, we develop a path markup language for pre-planning routes and interacting with the user on a mobile device for real-time indoor navigation. The interactive map is annotated with walkable paths and landmarks that can be used for inertial motion sensor-based navigation. The wall-following and landmark-checking algorithms help to cancel drifting errors along the way. Our preliminary experiments show that the approach is affordable and efficient to generate annotated building floor path maps and it is feasible to use the map for indoor navigation in real-time on a mobile device with motion sensors. The method can be applied to intelligent helmets and mobile phones, including potential applications of first responders, tour guide for buildings, and assistance for visually impaired users.


title: Adaptive Multiscale Model Reduction for Nonlinear Parabolic Equations Using GMsFEM

text: In this paper, we propose a coupled Discrete Empirical Interpolation Method (DEIM) and Generalized Multiscale Finite element method (GMsFEM) to solve nonlinear parabolic equations with application to the Allen-Cahn equation. The Allen-Cahn equation is a model for nonlinear reaction-diffusion process. It is often used to model interface motion in time, e.g. phase separation in alloys. The GMsFEM allows solving multiscale problems at a reduced computational cost by constructing a reduced-order representation of the solution on a coarse grid. In [14], it was shown that the GMsFEM provides a flexible tool to solve multiscale problems by constructing appropriate snapshot, offline and online spaces. In this paper, we solve a time dependent problem, where online enrichment is used. The main contribution is comparing different online enrichment methods. More specifically, we compare uniform online enrichment and adaptive methods. We also compare two kinds of adaptive methods. Furthermore, we use DEIM, a dimension reduction method to reduce the complexity when we evaluate the nonlinear terms. Our results show that DEIM can approximate the nonlinear term without significantly increasing the error. Finally, we apply our proposed method to the Allen Cahn equation.


title: Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach

text: We extend the Physics-Informed Echo State Network (PI-ESN) framework to reconstruct the evolution of an unmeasured state (hidden state) in a chaotic system. The PI-ESN is trained by using (i) data, which contains no information on the unmeasured state, and (ii) the physical equations of a prototypical chaotic dynamical system. Non-noisy and noisy datasets are considered. First, it is shown that the PI-ESN can accurately reconstruct the unmeasured state. Second, the reconstruction is shown to be robust with respect to noisy data, which means that the PI-ESN acts as a denoiser. This paper opens up new possibilities for leveraging the synergy between physical knowledge and machine learning to enhance the reconstruction and prediction of unmeasured states in chaotic dynamical systems.


title: Optimal Representation of Quantum Channels

text: This work shows an approach to reduce the dimensionality of matrix representations of quantum channels. It is achieved by finding a base of the cone of positive semidefinite matrices which represent quantum channels. Next, this is implemented in the Julia programming language as a part of the QuantumInformation.jl package.


title: Neural Assimilation

text: We introduce a new neural network for Data Assimilation (DA). DA is the approximation of the true state of some physical system at a given time obtained combining time-distributed observations with a dynamic model in an optimal way. The typical assimilation scheme is made up of two major steps: a prediction and a correction of the prediction by including information provided by observed data. This is the so called prediction-correction cycle. Classical methods for DA include Kalman filter (KF). KF can provide a rich information structure about the solution but it is often complex and time-consuming. In operational forecasting there is insufficient time to restart a run from the beginning with new data. Therefore, data assimilation should enable real-time utilization of data to improve predictions. This mandates the choice of an efficient data assimilation algorithm. Due to this necessity, we introduce, in this paper, the Neural Assimilation (NA), a coupled neural network made of two Recurrent Neural Networks trained on forecasting data and observed data respectively. We prove that the solution of NA is the same of KF. As NA is trained on both forecasting and observed data, after the phase of training NA is used for the prediction without the necessity of a correction given by the observations. This allows to avoid the prediction-correction cycle making the whole process very fast. Experimental results are provided and NA is tested to improve the prediction of oxygen diffusion across the Blood-Brain Barrier (BBB).


title: Formation of Morphogenetic Patterns in Cellular Automata

text: One of the most important problems in contemporary science, and especially in biology, is to reveal mechanisms of pattern formation. On the level of biological tissues, patterns form due to interactions between cells. These interactions can be long-range if mediated by diffusive molecules or short-range when associated with cell-to-cell contact sites. Mathematical studies of long-range interactions involve models based on differential equations while short-range interactions are modelled using discrete type models. In this paper, we use cellular automata (CA) technique to study formation of patterns due to short-range interactions. Namely, we use von Neumann cellular automata represented by a finite set of lattices whose states evolve according to transition rules. Lattices can be considered as representing biological cells (which, in the simplest case, can only be in one of the two different states) while the transition rules define changes in their states due to the cell-to-cell contact interactions. In this model, we identify rules resulting in the formation of stationary periodic patterns. In our analysis, we distinguish rules which do not destroy preset patterns and those which cause pattern formation from random initial conditions. Also, we check whether the forming patterns are resistant to noise and analyse the time frame for their formation. Transition rules which allow formation of stationary periodic patterns are then discussed in terms of pattern formation in biology.


title: Spatiotemporal Filtering Pipeline for Efficient Social Networks Data Processing Algorithms

text: One of the areas that gathers momentum is the investigation of location-based social networks (LBSNs) because the understanding of citizens’ behavior on various scales can help to improve quality of living, enhance urban management, and advance the development of smart cities. But it is widely known that the performance of algorithms for data mining and analysis heavily relies on the quality of input data. The main aim of this paper is helping LBSN researchers to perform a preliminary step of data preprocessing and thus increase the efficiency of their algorithms. To do that we propose a spatiotemporal data processing pipeline that is general enough to fit most of the problems related to working with LBSNs. The proposed pipeline includes four main stages: an identification of suspicious profiles, a background extraction, a spatial context extraction, and a fake transitions detection. Efficiency of the pipeline is demonstrated on three practical applications using different LBSN: touristic itinerary generation using Facebook locations, sentiment analysis of an area with the help of Twitter and VK.com, and multiscale events detection from Instagram posts.


title: Recursive Updates of Wildfire Perimeters Using Barrier Points and Ensemble Kalman Filtering

text: This paper shows how the wildfire simulation tool farsite is augmented with data assimilation capabilities that exploit the notion of barrier points and a constraint-point ensemble Kalman filtering to update wildfire perimeter predictions. Based on observations of the actual fire perimeter, stationary points on the fire perimeter are identified as barrier points and combined with a recursive update of the initial fire perimeter. It is shown that the combination of barrier point identification and using the barrier points as constraints in the ensemble Kalman filter gives a significant improvement in the forward prediction of the fire perimeter. The results are illustrated on the use case of the 2016 Sandfire that burned in the Angeles National Forest, east of the Santa Clarita Valley in Los Angeles County, California.


title: Hybrid Mixed Methods Applied to Miscible Displacements with Adverse Mobility Ratio

text: We propose stable and locally conservative hybrid mixed finite element methods to approximate the Darcy system and convection-diffusion problem, presented in a mixed form, to solve miscible displacements considering convective flows with adverse mobility ratio. The stability of the proposed formulations is achieved due to the choice of non-conforming Raviart-Thomas spaces combined to upwind scheme for the convection-dominated regimes, where the continuity conditions, between the elements, are weakly enforced by the introduction of Lagrange multipliers. Thus, the primal variables of both systems can be condensed in the element level leading a positive-definite global problem involving only the degrees of freedom associated with the multipliers. This approach, compared to the classical conforming Raviart-Thomas, present a reduction of the computational cost because, in both problems, the Lagrange multiplier is associated with a scalar field. In this context, a staggered algorithm is employed to decouple the Darcy problem from the convection-diffusion mixed system. However, both formulations are solved at the same time step, and the time discretization adopted for the convection-diffusion problem is the implicit backward Euler method. Numerical results show optimal convergence rates for all variables and the capacity to capture the formation and the propagation of the viscous fingering, as can be seen in the comparisons of the simulations of the Hele-Shaw cell with experimental results of the literature.


title: Combined Metrics for Quality Assessment of 3D Printed Surfaces for Aesthetic Purposes: Towards Higher Accordance with Subjective Evaluations

text: Objective quality assessment for 3D printing purposes may be considered as one of the most useful applications of machine vision in smart monitoring related to the development of the Industry 4.0 solutions. During recent years several approaches have been proposed, assuming observing the side surfaces, mainly based on the analysis of the regularity of visible patterns, which represent the consecutive printed layers. These methods, based on the use of general purpose image quality assessment (IQA) metrics, Hough transform, entropy and texture analysis, make it possible to classify the printed samples, independently of the filament’s colour, into low and high quality classes, with the use of photos or 3D scans of the side surfaces. The next step of research, investigated in this paper, is the combination of various proposed approaches to develop a combined metric, possibly highly correlated with subjective opinions. Since the correlation of single metrics developed mainly for classification is relatively low, their combination makes it possible to achieve much better results, verified using an original, newly developed database containing 107 captured images and 3D scans of the 3D printed surfaces with various colours and local distortions caused by external factors, together with Mean Opinion Scores (MOS) gathered from independent observers. Obtained results are promising and may be a starting point for further research towards the optimisation of the newly developed metrics for the automatic assessment of the 3D printed surfaces, mainly for aesthetic purposes.


title: Imperfect Distributed Quantum Phase Estimation

text: In the near-term, the number of qubits in quantum computers will be limited to a few hundreds. Therefore, problems are often too large and complex to be run on quantum devices. By distributing quantum algorithms over different devices, larger problem instances can be run. This distributing however, often requires operations between two qubits of different devices. Using shared entangled states and classical communication, these operations between different devices can still be performed. In the ideal case of perfect fidelity, distributed quantum computing is a solution to achieving scalable quantum computers with a larger number of qubits. In this work we consider the effects on the output fidelity of a quantum algorithm when using noisy shared entangled states. We consider the quantum phase estimation algorithm and present two distribution schemes for the algorithm. We give the resource requirements for both and show that using less noisy shared entangled states results in a higher overall fidelity.


title: Descending Flight Simulation of Tiltrotor Aircraft at Different Descent Rates

text: Helicopters and tiltrotor aircrafts are known to fall into an unstable state called vortex ring state when they descend rapidly. This paper presents a six degrees of freedom descending flight simulation of a tiltrotor aircraft represented by the V-22 Osprey, considering the interaction between fluid and a rigid body. That is, an aircraft affects the surrounding flow field by rotating the rotors, and flies with the generated force as thrust. Similarly, an orientation of the airframe is controlled by aerodynamic force which is generated by manipulating the shape. This numerical analysis is a complicated moving boundary problem involving motion of an air-frame or rotation of rotors. As a numerical approach, the Moving Computational Domain (MCD) method in combination with the multi-axis sliding mesh approach is adopted. In the MCD method, the whole computational domain moves with objects in the domain. At this time, fluid flow around the objects is generated by the movement of the boundaries. In addition, this method removes computational space restrictions, allowing an aircraft to move freely within the computational space regardless of a size of a computational grid. The multi-axis sliding mesh approach allows rotating bodies to be placed in a computational grid. Using the above approach, the flight simulation at two different descent rates is performed to reveal a behavior of a tiltrotor aircraft and a state of the surrounding flow field.


title: Unit Tests of Scientific Software: A Study on SWMM

text: Testing helps assure software quality by executing program and uncovering bugs. Scientific software developers often find it challenging to carry out systematic and automated testing due to reasons like inherent model uncertainties and complex floating point computations. We report in this paper a manual analysis of the unit tests written by the developers of the Storm Water Management Model (SWMM). The results show that the 1,458 SWMM tests have a 54.0% code coverage and a 82.4% user manual coverage. We also observe a “getter-setter-getter” testing pattern from the SWMM unit tests. Based on these results, we offer insights to improve test development and coverage.


title: Easing Multiscale Model Design and Coupling with MUSCLE 3

text: Multiscale modelling and simulation typically entails coupling multiple simulation codes into a single program. Doing this in an ad-hoc fashion tends to result in a tightly coupled, difficult-to-change computer program. This makes it difficult to experiment with different submodels, or to implement advanced techniques such as surrogate modelling. Furthermore, building the coupling itself is time-consuming. The MUltiScale Coupling Library and Environment version 3 (MUSCLE 3) aims to alleviate these problems. It allows the coupling to be specified in a simple configuration file, which specifies the components of the simulation and how they should be connected together. At runtime a simulation manager takes care of coordination of submodels, while data is exchanged over the network in a peer-to-peer fashion via the MUSCLE library. Submodels need to be linked to this library, but this is minimally invasive and restructuring simulation codes is usually not needed. Once operational, the model may be rewired or augmented by changing the configuration, without further changes to the submodels. MUSCLE 3 is developed openly on GitHub, and is available as Open Source software under the Apache 2.0 license.


title: Modeling and Automatic Code Generation Tool for Teaching Concurrent and Parallel Programming by Finite State Processes

text: Understanding concurrent and parallel programming can be a very hard task on first contact by students. This paper describes the development and experimental results of the FSP2JAVA tool. The proposed method starts from concurrent systems modeling through Finite State Processes (FSP). After that, the method includes an automatic code generation from the model. This goal is achieved by a domain-specific language compiler which translates from the FSP model to Java code. The FSP2JAVA tool is available for free download in the github site. We argue that this tool helps in teaching concurrent systems, since it abstracts all complex languages concern and encourages the student to be focused at the fundamental concepts of modeling and analysis.


title: Early Adaptive Evaluation Scheme for Data-Driven Calibration in Forest Fire Spread Prediction

text: Forest fires severally affect many ecosystems every year, leading to large environmental damages, casualties and economic losses. Established and emerging technologies are used to help wildfire analysts determine fire behavior and spread aiming at a more accurate prediction results and efficient use of resources in fire fighting. Natural hazards simulations need to deal with data input uncertainty and their impact on prediction results, usually resorting to compute-intensive calibration techniques. In this paper, we propose a new evaluation technique capable of reducing the overall calibration time by 60% when compared to the current data-driven approaches. This is achieved by means of the proposed adaptive evaluation technique based on a periodic monitoring of the fire spread prediction error [Formula: see text] estimated by the normalized symmetric difference for each simulation run. Our new strategy avoid wasting too much computing time running unfit individuals thanks to an early adaptive evaluation.


title: Multidimensional BSDEs with Mixed Reflections and Balance Sheet Optimal Switching Problem

text: In this paper, we study a system of multidimensional coupled reflected backward stochastic differential equations (RBSDEs) with interconnected generators and barriers and mixed reflections, i.e. oblique and normal reflections. This system of equations is arising in the context of optimal switching problem when both sides of the balance sheet are considered. This problem incorporates both the action of switching between investment modes and the action of abandoning the investment project before its maturity once it becomes unprofitable. Pricing such real options (switch option and abandon option) is equivalent to solve the system of coupled RBSDEs considered in the paper, for which we show the existence of a continuous adapted minimal solution via a Picard iteration method.


title: A Comparison of Multiple Objective Algorithms in the Context of a Dial a Ride Problem

text: In their operations private chauffeur companies have to solve variations of the multiple objective dial a ride problem. The number and type of restrictions make the problem extremely intricate and, when manually done, requires specialized people with a deep knowledge of the modus operandi of the company and of the environment in which the procedure takes place. Nevertheless, the scheduling can be automated through mean of computational methods, allowing to deliver solutions faster and, possible, optimized. In this context, this paper compares six algorithms applied to solving a multiple objective dial a ride problem, using data from a company mainly working in the Algarve, Portugal. The achieved results show that [Formula: see text]-MOEA overcomes the other algorithms tested, namely the NSGA-II, NSGA-III, [Formula: see text]-NSGA-II, SPEA2, and PESA2.


title: Sensitivity Analysis of Soil Parameters in Crop Model Supported with High-Throughput Computing

text: Uncertainty of input parameters in crop models and high costs of their experimental evaluation provide an exciting opportunity for sensitivity analysis, which allows identifying the most significant parameters for different crops. In this research, we perform a sensitivity analysis of soil parameters which play an essential role in plant growth for the MONICA agro-ecosystem model. We utilize Sobol’ sensitivity indices to estimate the importance of main soil parameters for several crop cultures (soybeans, sugar beet and spring barley). High-throughput computing allows us to speed up the computations by more than thirty times and increase the number of sampling points significantly. We identify soil indicators that play an essential role in crop yield productivity and show that their influence is the highest in the topsoil layer.


title: Towards Accurate Simulation of Global Challenges on Data Centers Infrastructures via Coupling of Models and Data Sources

text: Accurate digital twinning of the global challenges (GC) leads to computationally expensive coupled simulations. These simulations bring together not only different models, but also various sources of massive static and streaming data sets. In this paper, we explore ways to bridge the gap between traditional high performance computing (HPC) and data-centric computation in order to provide efficient technological solutions for accurate policy-making in the domain of GC. GC simulations in HPC environments give rise to a number of technical challenges related to coupling. Being intended to reflect current and upcoming situation for policy-making, GC simulations extensively use recent streaming data coming from external data sources, which requires changing traditional HPC systems operation. Another common challenge stems from the necessity to couple simulations and exchange data across data centers in GC scenarios. By introducing a generalized GC simulation workflow, this paper shows commonality of the technical challenges for various GC and reflects on the approaches to tackle these technical challenges in the HiDALGO project.


title: Testing Research Software: A Case Study

text: Background: The increasing importance of software for the conduct of various types of research raises the necessity of proper testing to ensure correctness. The unique characteristics of the research software produce challenges in the testing process that require attention. Aims: Therefore, the goal of this paper is to share the experience of implementing a testing framework using a statistical approach for a specific type of research software, i.e. non-deterministic software. Method: Using the ParSplice research software project as a case, we implemented a testing framework based on a statistical testing approach called Multinomial Test. Results: Using the new framework, we were able to test the ParSplice project and demonstrate correctness in a situation where traditional methodical testing approaches were not feasible. Conclusions: This study opens up the possibilities of using statistical testing approaches for research software that can overcome some of the inherent challenges involved in testing non-deterministic research software.


title: A Heterogeneous Multi-scale Model for Blood Flow

text: This research focuses on developing a heterogeneous multi-scale model (HMM) for blood flow. Two separate scales are considered in this study, a Macro-scale, which models whole blood as a continuous fluid and tracks the transport of hematocrit profiles through an advection diffusion solver. And a Micro-scale, which computes directly local diffusion coefficients and viscosities using cell resolved simulations. The coupling between these two scales also includes the use of a surrogate model, which saved local viscosity and diffusion coefficients from previously simulated local hematocrit and shear rate combinations. As the HMM model progresses fewer micro models will be spawned. This is accomplished through the surrogate by interpolating from previously computed viscosities and diffusion coefficients. The benefit of using the HMM method for blood flow is that it, along with resolving the rheology of whole blood, can be extended with other types computational models to model physiological processes like thrombus formation.


title: Development of an Object-Oriented Programming Tool Based on FEM for Numerical Simulation of Mineral-Slurry Transport

text: The early stages of the development of a finite element method (FEM) based computational tool for numerically simulating mineral-slurry transport involving both Newtonian and non-Newtonian flows are described in this work. The rationale behind the conception, design and implementation of the referred object-oriented programming tool is thus initially highlighted. A particular emphasis is put on several architectural aspects accounted for and object class hierarchies defined during the development of the tool. Next one of the main modules composing the tool under development is further described. Finally, as a means of illustration, the use of the FEM based tool for simulating two-dimensional laminar flows is discussed. More specifically, canonical configurations widely studied in the past are firstly accounted for. A more practical application involving the simulation of a mineral-slurry handling device is then studied using the power-law rheological model. The results from the simulations carried out highlight the usefulness of the tool for realistically predicting the associated flow behavior. The FEM based tool discussed in this work will be used in future for carrying out high-fidelity numerical simulations of turbulent multiphase flows including fluid-particle interactions.


title: A Variational Algorithm for Quantum Neural Networks

text: Quantum Computing leverages the laws of quantum mechanics to build computers endowed with tremendous computing power. The field is attracting ever-increasing attention from both academic and private sectors, as testified by the recent demonstration of quantum supremacy in practice. However, the intrinsic restriction to linear operations significantly limits the range of relevant use cases for the application of Quantum Computing. In this work, we introduce a novel variational algorithm for quantum Single Layer Perceptron. Thanks to the universal approximation theorem, and given that the number of hidden neurons scales exponentially with the number of qubits, our framework opens to the possibility of approximating any function on quantum computers. Thus, the proposed approach produces a model with substantial descriptive power, and widens the horizon of potential applications already in the NISQ era, especially the ones related to Quantum Artificial Intelligence. In particular, we design a quantum circuit to perform linear combinations in superposition and discuss adaptations to classification and regression tasks. After this theoretical investigation, we also provide practical implementations using various simulation environments. Finally, we test the proposed algorithm on synthetic data exploiting both simulators and real quantum devices.


title: [Image: see text]: A Cross-Platform Programming Framework for Quantum-Accelerated Scientific Computing

text: This paper introduces a new cross-platform programming framework for developing quantum-accelerated scientific computing applications and executing them on most of today’s cloud-based quantum computers and simulators. It makes use of C++ template meta-programming techniques to implement quantum algorithms as generic, platform-independent expressions, which get automatically synthesized into device-specific compute kernels upon execution. Our software framework supports concurrent and asynchronous execution of multiple quantum kernels via a CUDA-inspired stream concept.


title: Markov Chain Monte Carlo Methods for Fluid Flow Forecasting in the Subsurface

text: Accurate predictions in subsurface flows require the forecasting of quantities of interest by applying models of subsurface fluid flow with very little available data. In general a Bayesian statistical approach along with a Markov Chain Monte Carlo (MCMC) algorithm can be used for quantifying the uncertainties associated with subsurface parameters. However, the complex nature of flow simulators presents considerable challenges to accessing inherent uncertainty in all flow simulator parameters of interest. In this work we focus on the transport of contaminants in a heterogeneous permeability field of a aquifer. In our problem the limited data comes in the form of contaminant fractional flow curves at monitoring wells of the aquifer. We then employ a Karhunen-Loève expansion to truncate the stochastic dimension of the permeability field and thus the expansion helps reducing the computational burden. Aiming to reduce the computational burden further, we code our numerical simulator using parallel programming procedures on Graphics Processing Units (GPUs). In this paper we mainly present a comparative study of two well-known MCMC methods, namely, two-stage and DiffeRential Evolution Adaptive Metropolis (DREAM), for the characterization of the two-dimensional aquifer. A thorough statistical analysis of ensembles of the contaminant fractional flow curves from both MCMC methods is presented. The analysis indicates that although the average fractional flow curves are quite similar, both time-dependent ensemble variances and posterior analysis are considerably distinct for both methods.


title: Data Mining for Big Dataset-Related Thermal Analysis of High Performance Computing (HPC) Data Center

text: Greening of Data Centers could be achieved through energy savings in two significant areas, namely: compute systems and cooling systems. A reliable cooling system is necessary to produce a persistent flow of cold air to cool the servers due to increasing computational load demand. Servers’ dissipated heat effects a strain on the cooling systems. Consequently, it is necessary to identify hotspots that frequently occur in the server zones. This is facilitated through the application of data mining techniques to an available big dataset for thermal characteristics of High-Performance Computing ENEA Data Center, namely Cresco 6. This work presents an algorithm that clusters hotspots with the goal of reducing a data centre’s large thermal-gradient due to uneven distribution of server dissipated waste heat followed by increasing cooling effectiveness.


title: Robust Single Machine Scheduling with Random Blocks in an Uncertain Environment

text: While scheduling problems in deterministic models are quite well investigated, the same problems in an uncertain environment require very often further exploration and examination. In the paper we consider a single machine tabu search method with block approach in an uncertain environment modeled by random variables with the normal distribution. We propose a modification to the tabu search method which improves the robustness of the obtained solutions. The conducted computational experiments show that the proposed improvement results in a much more robust solutions than the ones obtained in the classic block approach.


title: deal.II Implementation of a Two-Field Finite Element Solver for Poroelasticity

text: This paper presents a finite element solver for poroelasticity in the 2-field approach and its implementation on the deal.II platform. Numerical experiments on benchmarks are presented to demonstrate the accuracy and efficiency of this new solver.


title: Global Sensitivity Analysis of Various Numerical Schemes for the Heston Model

text: The pricing of financial options is usually based on statistical sampling of the evolution of the underlying under a chosen model, using a suitable numerical scheme. It is widely accepted that using low-discrepancy sequences instead of pseudorandom numbers in most cases increases the accuracy. It is important to understand and quantify the reasons for this effect. In this work, we use Global Sensitivity Analysis in order to study one widely used model for pricing of options, namely the Heston model. The Heston model is an important member of the family of the stochastic volatility models, which have been found to better describe the observed behaviour of option prices in the financial markets. By using a suitable numerical scheme, like those of Euler, Milstein, Kahl-Jäckel, Andersen, one has the flexibility needed to compute European, Asian or exotic options. In any case the problem of evaluating an option price can be considered as a numerical integration problem. For the purposes of modelling and complexity reduction, one should make the distinction between the model nominal dimension and its effective dimension. Another notion of “average dimension” has been found to be more practical from the computational point of view. The definitions and methods of evaluation of effective dimensions are based on computing Sobol’ sensitivity indices. A classification of functions based on their effective dimensions is also known. In the context of quantitative finance, Global Sensitivity Analysis (GSA) can be used to assess the efficiency of a particular numerical scheme. In this work we apply GSA based on Sobol sensitivity indices in order to assess the interactions of the various dimensions in using the above mentioned schemes. We observe that the GSA offers useful insight on how to maximize the advantages of using QMC in these schemes.


title: MCMC for Bayesian Uncertainty Quantification from Time-Series Data

text: In computational neuroscience, Neural Population Models (NPMs) are mechanistic models that describe brain physiology in a range of different states. Within computational neuroscience there is growing interest in the inverse problem of inferring NPM parameters from recordings such as the EEG (Electroencephalogram). Uncertainty quantification is essential in this application area in order to infer the mechanistic effect of interventions such as anaesthesia. This paper presents [Image: see text] software for Bayesian uncertainty quantification in the parameters of NPMs from approximately stationary data using Markov Chain Monte Carlo (MCMC). Modern MCMC methods require first order (and in some cases higher order) derivatives of the posterior density. The software presented offers two distinct methods of evaluating derivatives: finite differences and exact derivatives obtained through Algorithmic Differentiation (AD). For AD, two different implementations are used: the open source Stan Math Library and the commercially licenced [Image: see text] tool distributed by NAG (Numerical Algorithms Group). The use of derivative information in MCMC sampling is demonstrated through a simple example, the noise-driven harmonic oscillator. And different methods for computing derivatives are compared. The software is written in a modular object-oriented way such that it can be extended to derivative based MCMC for other scientific domains.


title: Bringing Harmony to Computational Science Pedagogy

text: Inherent in a musical composition are properties that are analogous to properties and laws that exist in mathematics, physics, and psychology. It follows then that computation using musical models can provide results that are analogous to results provided by mathematical, physical, and psychological models. The audible output of a carefully constructed musical model can demonstrate properties and relationships in a way that is immediately perceptible. For this reason, the study of musical computation is a worthwhile pursuit as a pedagogical tool for computational science. Proposed in this paper is a curriculum for implementing the study of musical computation within a larger computer science or computational science program at a college or university. A benefit of such a curriculum is that it provides a way to integrate artistic endeavors into a STREAM program, while maintaining the mathematical foundations of STEM. Furthermore, the study of musical computation aligns well with the arts-related components of Human-Centered Computing. The curriculum is built on the following two hypotheses: The first hypothesis is that the cognitive, creative, and structural processes involved in both musical composition and computer programming, are similar enough that skilled computer scientists, with or without musical backgrounds, can learn to use programming languages to compose interesting, expressive, and sophisticated musical works. The second hypothesis is that the links between music, mathematics, and several branches of science are strong enough that skilled computational scientists can create musical models that are able to be designed using vocabularies of mathematics and science. While the curriculum defined in this paper focuses on musical computation, the design principles behind it may be applied to other disciplines.


title: A Quantum Annealing Algorithm for Finding Pure Nash Equilibria in Graphical Games

text: We introduce Q-Nash, a quantum annealing algorithm for the NP-complete problem of finding pure Nash equilibria in graphical games. The algorithm consists of two phases. The first phase determines all combinations of best response strategies for each player using classical computation. The second phase finds pure Nash equilibria using a quantum annealing device by mapping the computed combinations to a quadratic unconstrained binary optimization formulation based on the Set Cover problem. We empirically evaluate Q-Nash on D-Wave’s Quantum Annealer 2000Q using different graphical game topologies. The results with respect to solution quality and computing time are compared to a Brute Force algorithm and the Iterated Best Response heuristic.


title: Cyber Attribution from Topological Patterns

text: We developed a crawler to collect live malware distribution network data from publicly available sources including Google Safe Browser and VirusTotal. We then generated a dynamic graph with our visualization tool and performed malware attribution analysis. We found: 1) malware distribution networks form clusters rather than a single network; 2) those cluster sizes follow the Power Law; 3) there is a correlation between cluster size and the number of malware species in the cluster; 4) there is a correlation between the number of malware species and cyber events; and finally, 5) infrastructure components such as bridges, hubs, and persistent links play significant roles in malware distribution dynamics.


title: Empirical Analysis of Stochastic Methods of Linear Algebra

text: In this paper we present the results of an empirical study of stochastic projection and stochastic gradient descent methods as means of obtaining approximate inverses and preconditioners for iterative methods. Results of numerical experiments are used to analyse scalability and overall suitability of the selected methods as practical tools for treatment of large linear systems of equations. The results are preliminary due to the code being not yet fully optimized.


title: Radial Basis Functions Mesh Morphing: A Comparison Between the Bi-harmonic Spline and the Wendland C2 Radial Function

text: Radial basis functions (RBFs) based mesh morphing allows to adapt the shape of a computational grid onto a new one by updating the position of all its nodes. Usually nodes on surfaces are used as sources to define the interpolation field that is propagated into the volume mesh by the RBF. The method comes with two distinctive advantages that makes it very flexible: it is mesh independent and it allows a node wise precision. There are however two major drawbacks: large data set management and excessive distortion of the morphed mesh that may occur. Two radial kernels are widely adopted to overtake such issues: the bi-harmonic spline (BHS) and the Wendland C2 (WC2). The BHS minimizes the mesh distortion but it is computational intense as a dense linear system has to be solved whilist the WC2 leads to a sparse system easier to solve but which can lack in smoothness. In this paper we compare these two radial kernels with a specific focus on mesh distortion. A detailed insight about RBF fields resulting from BHS and WC2 is first provided by inspecting the intensity and the distribution of the strain for a very simple shape: a square plate with a central circular hole. An aeronautical example, the ice formation onto the leading edge of a wing, is then exposed adopting an industrial software implementation based on the state of the art of RBF solvers.


title: Numerical Simulation of Heat Transfer in an Enclosure with Time-Periodic Heat Generation Using Finite-Difference Method

text: This paper reports a numerical investigation of highly coupled system of partial differential equations, simulating the fluid flow and heat transfer in a large-scale enclosure with time-periodic heat generation. The bottom wall of the enclosure is insulated, and heat exchange with the environment is modeled at other external boundaries. The heater with time-periodic heat generation is located at the bottom of the enclosure. The internal surfaces of both the heater and walls are assumed to be gray. Air is the working fluid and the Rayleigh number is 10(9). To solve the governing equations with dimensionless vorticity – stream function – temperature variables, the finite difference method has been used. The developed model has been validated through a comparison with data of other authors. The effect of surface emissivity and periodic heat generation on Nusselt numbers and both stream function and temperature distributions has been investigated. The results showed that the influence of the thermal radiation on total thermal transmission increases with surface emissivity of walls and heater surfaces. The present numerical method can be applied in several engineering problems, such as designing passive cooling systems and the simulation of heat transfer in building constructions.


title: Supporting Education in Algorithms of Computational Mathematics by Dynamic Visualizations Using Computer Algebra System

text: Computer algebra systems (CAS) are often used programs in universities to support calculations and visualization in teaching mathematical subjects. In this paper we present some examples of dynamic visualizations which we prepared for students of Warsaw University of Life Sciences using Mathematica. Visualizations for simplex algorithm and Karush-Kuhn-Tucker algorithm are presented. We also describe a didactic experiment for students of the Production Engineering Faculty of Warsaw University of Life Sciences using dynamic visualization of the network flow problem.


title: A New Multilayer Network Construction via Tensor Learning

text: Multilayer networks proved to be suitable in extracting and providing dependency information of different complex systems. The construction of these networks is difficult and is mostly done with a static approach, neglecting time delayed interdependences. Tensors are objects that naturally represent multilayer networks and in this paper, we propose a new methodology based on Tucker tensor autoregression in order to build a multilayer network directly from data. This methodology captures within and between connections across layers and makes use of a filtering procedure to extract relevant information and improve visualization. We show the application of this methodology to different stationary fractionally differenced financial data. We argue that our result is useful to understand the dependencies across three different aspects of financial risk, namely market risk, liquidity risk, and volatility risk. Indeed, we show how the resulting visualization is a useful tool for risk managers depicting dependency asymmetries between different risk factors and accounting for delayed cross dependencies. The constructed multilayer network shows a strong interconnection between the volumes and prices layers across all the stocks considered while a lower number of interconnections between the uncertainty measures is identified.


title: Interactive Travel Aid for the Visually Impaired: from Depth Maps to Sonic Patterns and Verbal Messages

text: This paper presents user trials of a prototype micro-navigation aid for the visually impaired. The main advantage of the system is its small form factor. The device consists of a Structure Sensor depth camera, a smartphone, a remote controller and a pair of headphones. An original feature of the system is its interactivity. The user can activate different space scanning modes and different sound presentation schemes for 3D scenes on demand. The results of the trials are documented by timeline logs recording the activation of different interactive modes. The aim of the first trial was to test system capability for aiding the visually impaired to avoid obstacles. The second tested system efficiency at detecting open spaces. The two visually impaired testers performed the trials successfully, although the times required to complete the tasks seem rather long. Nevertheless, the trials show the potential usefulness of the system as a navigational aid and have enabled us to introduce numerous improvements to the tested prototype.


title: A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication

text: Matrix factorization is a very common machine learning technique in recommender systems. Bayesian Matrix Factorization (BMF) algorithms would be attractive because of their ability to quantify uncertainty in their predictions and avoid over-fitting, combined with high prediction accuracy. However, they have not been widely used on large-scale data because of their prohibitive computational cost. In recent work, efforts have been made to reduce the cost, both by improving the scalability of the BMF algorithm as well as its implementation, but so far mainly separately. In this paper we show that the state-of-the-art of both approaches to scalability can be combined. We combine the recent highly-scalable Posterior Propagation algorithm for BMF, which parallelizes computation of blocks of the matrix, with a distributed BMF implementation that users asynchronous communication within each block. We show that the combination of the two methods gives substantial improvements in the scalability of BMF on web-scale datasets, when the goal is to reduce the wall-clock time.


title: Perturbation of the Numerical Range of Unitary Matrices

text: In this work we show how to approach the problem of manipulating the numerical range of a unitary matrix. This task has far-reaching impact on the study of discrimination of quantum measurements. We achieve the aforementioned manipulation by introducing a method which allows us to find a unitary matrix whose numerical range contains the origin where at the same time the distance between unitary matrix and its perturbation is relative small in given metric.


title: Automatic Feedback Provision in Teaching Computational Science

text: We describe a method of automatic feedback provision for students learning computational science and data science methods in Python. We have implemented, used and refined this system since 2009 for growing student numbers, and summarise the design and experience of using it. The core idea is to use a unit testing framework: the teacher creates a set of unit tests, and the student code is tested by running these tests. With our implementation, students typically submit work for assessment, and receive feedback by email within a few minutes after submission. The choice of tests and the reporting back to the student is chosen to optimise the educational value for the students. The system very significantly reduces the staff time required to establish whether a student’s solution is correct, and shifts the emphasis of computing laboratory student contact time from assessing correctness to providing guidance. The self-paced nature of the automatic feedback provision supports a student-centred learning approach. Students can re-submit their work repeatedly and iteratively improve their solution, and enjoy using the system. We include an evaluation of the system from using it in a class of 425 students.


docid: xdiytfye

title: Generalized Quantum Deutsch-Jozsa Algorithm

text: Quantum computing aims to provide algorithms and hardware that allows for solving computational problems asymptotically faster than on classical computers. Yet, design of new, fast quantum algorithms is not straightforward, and the field faces high barriers of entry for traditional computer scientists. One of the main didactic examples used to introduce speedup resulting from quantum computing is the Deutsch-Jozsa algorithm for discriminating between constant and balanced functions. Here, we show a generalization of the Deutsch-Jozsa algorithm beyond balanced functions that can be used to further illustrate the design choices underpinning quantum algorithms.


title: A Numerical Algorithm to Solve the Two-Phase Flow in Porous Media Including Foam Displacement

text: This work is dedicated to simulating the Enhanced Oil Recovery (EOR) process of foam injection in a fully saturated reservoir. The presence of foam in the gas-water mixture acts in controlling the mobility of the gas phase, contributing to reduce the effects of fingering and gravity override. A fractional flow formulation based on global pressure is used, resulting in a system of Partial Differential Equations (PDEs) that describe two coupled problems of distinct kinds: elliptic and hyperbolic. The numerical methodology is based on splitting the system of equations into two sub-systems that group equations of the same kind and on applying a hybrid finite element method to solve the elliptic problem and a high-order finite volume method to solve the hyperbolic equations. Numerical results show good efficiency of the algorithm, as well as the remarkable ability of the foam to increase reservoir sweep efficiency by reducing gravity override and fingering effects.


title: Multilevel Monte Carlo with Improved Correlation for Kinetic Equations in the Diffusive Scaling

text: In many applications, it is necessary to compute the time-dependent distribution of an ensemble of particles subject to transport and collision phenomena. Kinetic equations are PDEs that model such particles in a position-velocity phase space. In the low collisional regime, explicit particle-based Monte Carlo methods simulate these high dimensional equations efficiently, but, as the collision rate increases, these methods suffer from severe time-step constraints. In the high collision regime, asymptotic-preserving particle schemes are able to produce stable results. However, this stability comes at the cost of a bias in the computed results. In earlier work, the multilevel Monte Carlo method was used to reduce this bias by combining simulations with large and small time steps. This multilevel scheme, however, still has large variances when correlating fine and coarse simulations, which leads to sub-optimal multilevel performance. In this work, we present an improved correlation approach that decreases the variance when bridging the gap from large time steps to time steps of the order of magnitude of the collision rate. We further demonstrate that this reduced variance results in a sharply reduced simulation cost at the expense of a small bias.


Correction to Lancet Gastroenterol Hepatol 2020; published online June 8. https://doi.org/10.1016/S2468-1253(20)30185-0


Returning to digestive endoscopy normality will be slow and must include novelty and telemedicine


US law enforcement crowd control tactics at anti-racism protests: a public health threat


title: Nationale Diabetes Registratie: Voor een toekomstbestendige en persoonsgerichte diabeteszorg in Nederland

text: Diabetes mellitus vormt een aanzienlijke ziektelast in Nederland; ruim 1,2 miljoen Nederlanders hebben diabetes, waarvan 90 procent type 2-diabetes. De daarmee gepaard gaande economische last wordt geschat op ruim 6,9 miljard euro. De prevalentie zal naar verwachting in de toekomst nog verder toenemen als gevolg van toenemende obesitas en een vergrijzende bevolking. Er is dus een dringende behoefte om goede diabeteszorg in Nederland toekomstbestendiger en patiëntgerichter te maken. Het meten en vergelijken van gezondheidsuitkomsten - en het identificeren van de oorzaken van variatie - helpt om gebieden te identificeren waar betere resultaten en efficiëntieverbeteringen kunnen worden bereikt. Op dit moment zijn de cijfers over diabetes erg gefragmenteerd en onvolledig. De Nederlandse Diabetes Federatie (NDF) wil zich inzetten om op basis van gecombineerde − reeds bestaande − bronnen te komen tot een Nationale Diabetes Registratie. Het doel van deze dataverzameling is om te komen tot een uitkomstgerichte en persoonsgerichtere diabeteszorg, die mensen met diabetes ondersteunt om gezond te blijven.


title: 5-(Perylen-3-ylethynyl)uracil Derivatives Inhibit Reproduction of Respiratory Viruses

text: In this work, we describe the synthesis of 5-(perylen-3-ylethynyl)uridine and its ability to effectively inhibit the replication of respiratory disease pathogens in cell culture, namely: influenza A virus (IVA); type 3 parainfluenza virus (PIV-3); and human respiratory syncytial virus (RSV). Related known compounds were also analyzed: 5-(perylen-3-ylethynyl)-2′-deoxy-uridine; 5-(perylen-3-ylethynyl)-arabino-uridine; and 1-carboxymethyl-3-pivaloyloxymethyl-5-(perylen-3-ylethynyl)uracil.


title: An Automatic Disinfection System for Passenger Luggage at Airports and Train/Bus Stations

text: Many researchers are working on multiple aspects of the COVID-19 pandemic including disease detection, treatment, and vaccine development. It is expected that there will be a large increase in passenger traffic at airports and railway stations and other public places. This paper proposes one solution to reduce the transmission of the disease through fomites such as passenger luggage and packages at bus/train stations and airports in the country. A tunnel system similar to the X-ray machines used for passenger luggage at airports has been proposed for disinfection of fomites. The system consists of eight 36 W T8 TUV bulbs illuminating each square meter area of the fomites on a conveyor belt for 10 s. For the standard airline luggage dimensions, 24 such bulbs will be distributed evenly on all four sides of the tunnel. The entry and exit points on the conveyor are shielded from the UV-C light leakage by placing thin plastic (such as acrylic) curtains. An optional non-foaming soap solution spray system may be used as an additional disinfection step. The toxic sodium hypochlorite solutions are not used in this design. Non-foaming soap solutions which are very effective against coronavirus and are non-toxic and biodegradable may be used as an optional disinfectant. It is to be noted that while the disinfection systems are designed to effectively mitigate the threat of coronavirus on the passenger fomite surfaces, these are purely based on theoretical calculations and have not been tested with actual viral particles. However, considering the time-sensitive emergency with COVID-19 pandemic, these systems will be very useful even without rigorous campaign of testing.


De virtuele diabeteskliniek in een stroomversnelling?


Geachte lezer,


Acquired resistance to osimertinib plus savolitinib is mediated by MET-D1228 and Y1230 mutations in EGFR mutated MET amplified lung cancer


title: Nanomaterials and Their Negative Effects on Human Health

text: Mesostructured silica, dendrimers, and allotropes of carbon were exhaustively used in biomedical, cosmetics, semiconductors, and food industry applications. Considering the huge prospect of nanomaterials, their potential hazards on exposure to humans and their related ecotoxicological effects needs to be summarized. Nanoparticles with size below 100 nm could pass into the lung and then to blood through inhalation, ingestion, and skin contact. As nanotechnology innovation is expected to achieve $ 2231 million by 2025, humans will be exposed ever increasingly in day-to-day life and in industries. In this review, the latest synthetic methodology of silica, dendrimers, and CNTs, their biological applications (in vitro and in vivo) related to toxicity were discussed. In terms of structured silica, the toxic and non-toxic effect induced by specific templates (cetylpyridinium bromide, cetyltrimethylammonium bromide, dipalmitoylphosphatidylcholine, C16L-tryptophan, C16-L-histidine, and C16-L-poline) that are used to generate mesoporous silica, silica nanoparticle sizes (25, 50, 60, 115, and 500 nm), and silane functionalization (NH(2) and COOH) were discussed. The recent applications of different generations (G3, G4, G5, and G6) of amphiphilic Janus dendrimers were discussed along with toxicity effect of different charged dendrimers (cationic and anionic) and effect of PEGylation. Recent synthesis, advantages, and disadvantages of carbon nanotubes (CNTs) were presented for structures like single walled carbon nanotubes (SWCNTs) and multiwalled carbon nanotubes (MWCNTs). The influence of diameter of SWCNTs (linear and short), thickness (thin and thick), effect of oxidation, metal oxide species (TiO(2), Fe, and Au), and biocompatible polymers (polyethylene glycol, bisphosphonate, and alendronate) were shown in relation to molecular pathways in animal cells.


Commentary: Burning Your Bridges


Birth Settings in America: Outcomes, Quality, Access, and Choice


title: Sefficiency (Sustainable Efficiency)

text: There is a fundamental difference between descriptive and performance indicators of a water use system. The former responds to the question “What is happening?”, and the latter focuses on the questions, such as “Does it matter? Are we reaching targets?” To answer these questions, we use efficiency as a performance indicator, which “helps us attain more of the things we value.” To this end, we employ the theory presented in the previous Chapter to develop systemic, comprehensive and objective performance indicators based on a universal principle integrating the differentials of the three Pillars of water management, namely water quantity, water quality and water benefit. They reveal trade-offs among the three Pillars at three levels of management with climate and energy descriptors and stakeholder enablers. The internal behaviour and the trade-offs of these Sefficiency indicators are explained and shown graphically.


Kai-Fu-Lee (2019): AI Superpowers—China, Silicon Valley and the New World Order


title: Optimization of the Recovery of Anthocyanins from Chokeberry Juice Pomace by Homogenization in Acidified Water

text: ABSTRACT: The recovery efficiency of waste valorization processes depends on an interplay of different conditions that are sometimes overlooked. Process optimization by the means of establishing mathematical relations between the process parameters and outputs is a strong tool to identify optimal operating conditions based on experimental data. In this study, the extraction of anthocyanins from chokeberry (Aronia melancocarpa) juice pomace using homogenization in acidified water was selected as a case study for process optimization using response surface methodology. The parameters studied were the citric acid content in the water, the temperature and the liquid–solid ratio. The optimal conditions to maximize both anthocyanin concentration and total anthocyanin content extracted were 1.5 wt% citric acid, 45 °C and 34 g solvent/g fresh pomace. Furthermore, the model developed predicted satisfactorily the overall anthocyanin content and anthocyanin concentration in the extract, as well as the final pH and total dissolved solids. The process optimization performed in this study sets the ground for further process design targeting the production of high-value products from byproducts or biowaste to be used in food ingredients or supplements. GRAPHIC ABSTRACT: [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12649-020-01127-w) contains supplementary material, which is available to authorized users.


title: Cold Plasma: Clean Technology to Destroy Pathogenic Micro-organisms

text: Atmospheric pressure cold plasma is a promising technology in fighting pathogenic micro-organisms. In times of Covid-19 pandemic, it was decided to modify two types of cold plasma devices to study their effectiveness in the killing of pathogenic micro-organisms. These studies have shown that both the devices are efficient in this purpose. While pencil like microwave based device can destroy Aeromonas bacteria and its bacteriophage from 6 cm distance in 2 min, the larger (~ 40 cm(2)) RF plasma based device could do the similar killing ability for the larger possible area in 4 min. Optical Emission Spectroscopy (OES) studies revealed that both these devices produce OH radicals which helped in the destruction of both bacteria and its bacteriophage. With suitable modifications, these devices, especially the larger area device may even be implemented for the elimination of Covid-19 affected wards of hospital without using any sensitive chemical process.


Part 2: Exciting Changes in Feline Medicine


Careful clinical evaluation of donor fraction cell-free DNA in rejection surveillance after heart transplantation


Fellows Front and Center: Tele-training and Telehealth


Diagnosis of patients with suspected COVID-19: What is the role of chest CT?()


Maculopapular rash as initial manifestation of SRAS-coronavirus-2 infection()


Infection prevention and control in cystic fibrosis: One size fits all?


Une autre pandémie !


Safety practices and appropriate infection prevention mitigate potential staffing shortage