ALBERT

Description: 〈p〉Publication date: Available online 8 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Maxim Rakhuba, Alexander Novikov, Ivan Oseledets〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Such problems as computation of spectra of spin chains and vibrational spectra of molecules can be written as 〈em〉high-dimensional eigenvalue problems〈/em〉, i.e., when the eigenvector can be naturally represented as a multidimensional tensor. Tensor methods have proven to be an efficient tool for the approximation of solutions of high-dimensional eigenvalue problems, however, their performance deteriorates quickly when the number of eigenstates to be computed increases. We address this issue by designing a new algorithm motivated by the ideas of 〈em〉Riemannian optimization〈/em〉 (optimization on smooth manifolds) for the approximation of multiple eigenstates in the 〈em〉tensor-train format〈/em〉, which is also known as matrix product state representation. The proposed algorithm is implemented in TensorFlow, which allows for both CPU and GPU parallelization.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 8 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Chen Liu, Florian Frank, Faruk O. Alpak, Béatrice Rivière〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Permeability estimation of porous media from directly solving the Navier–Stokes equations has a wide spectrum of applications in petroleum industry. In this paper, we utilize a pressure-correction projection algorithm in conjunction with the interior penalty discontinuous Galerkin scheme for space discretization to build an incompressible Navier–Stokes simulator and to use this simulator to calculate permeability of real rock samples. The proposed method is accurate, numerically robust, and exhibits the potential for tackling realistic problems.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 8 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Mustapha Malek, Nouh Izem, M. Shadi Mohamed, Mohammed Seaid, Omar Laghrouche〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉An efficient partition of unity finite element method for three-dimensional transient diffusion problems is presented. A class of multiple exponential functions independent of time variable is proposed to enrich the finite element approximations. As a consequence of this procedure, the associated matrix for the linear system is evaluated once at the first time step and the solution is obtained at subsequent time step by only updating the right-hand side of the linear system. This results in an efficient numerical solver for transient diffusion equations in three space dimensions. Compared to the conventional finite element methods with 〈em〉h〈/em〉-refinement, the proposed approach is simple, more efficient and more accurate. The performance of the proposed method is assessed using several test examples for transient diffusion in three space dimensions. We present numerical results for a transient diffusion equation with known analytical solution to quantify errors for the new method. We also solve time-dependent diffusion problems in complex geometries. We compare the results obtained using the partition of unity finite element method to those obtained using the standard finite element method. It is shown that the proposed method strongly reduces the necessary number of degrees of freedom to achieve a prescribed accuracy.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 8 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Lahbib Bourhrara〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This document presents a new numerical scheme dealing with the Boltzmann transport equation. This scheme is based on the expansion of the angular flux in a truncated spherical harmonics function and the discontinuous finite element method for the spatial variable. The advantage of this scheme lies in the fact that we can deal with unstructured, non-conformal and curved meshes. Indeed, it is possible to deal with distorted regions whose boundary is constituted by edges that can be either line segments or circular arcs or circles. In this document, we detail the derivation of the method for 2D geometries. However, the generalization to 2D extruded geometries is trivial.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 132〈/p〉 〈p〉Author(s): T. Carlotto, P.L.B. Chaffe〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Streamflow recession analysis is crucial for understanding how catchments release water in periods of drought and therefore is important for water resources planning and management. Despite there being several theories on how to model recession curves, few studies compare the different approaches to that problem. In this work, we developed the Master Recession Curve Parameterization tool (MRCPtool), which brings together a set of automated methods for the analysis of recession periods based only on streamflow data. The methods include: (i) hydrograph separation using numerical filters; (ii) automatic extraction of recession periods; (iii) creation of the MRC with the matching strip method; (iv) creation of the MRC for different flow classes defined from the flow duration curve; (v) analysis of flow recession rates 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si1.svg"〉〈mrow〉〈mo〉(〈/mo〉〈mo linebreak="goodbreak" linebreakstyle="after"〉−〈/mo〉〈mi〉d〈/mi〉〈mi〉Q〈/mi〉〈mo〉∕〈/mo〉〈mi〉d〈/mi〉〈mi〉t〈/mi〉〈mo〉)〈/mo〉〈/mrow〉〈/math〉 as a function of flow 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si2.svg"〉〈mrow〉〈mo〉(〈/mo〉〈mi〉Q〈/mi〉〈mo〉)〈/mo〉〈/mrow〉〈/math〉 and (vi) creation of the MRC from simulated recession curves with different analytical approaches, including linear and nonlinear models. The MRCPtool contains a graphical user interface developed in MATLAB software that facilitates the analysis of streamflow datasets. Finally, we present an example application of the MRCPtool using a streamflow dataset of 44 years. The MRCPtool is an open source tool that can be downloaded from the site: 〈a href="http://www.labhidro.ufsc.br/static/software/MRCPtool.rar" target="_blank"〉http://www.labhidro.ufsc.br/static/software/MRCPtool.rar〈/a〉.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 132〈/p〉 〈p〉Author(s): Hongxing Zhang, Mingliang Zhang, Yongpeng Ji, Yini Wang, Tianping Xu〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Vegetation exerts a significant damping effect on tsunami wave run-up on coastal beaches, thus effectively mitigating the tsunami hazard. A depth-integrated two-dimensional numerical model (HydroSed2D, Liu et al., 2008; Liu et al., 2010) is developed to investigate tsunami wave run-up and land inundation on coastal beaches covered with 〈em〉Pandanus odoratissimus〈/em〉 (〈em〉P. odoratissimus〈/em〉). The present model is based on a finite volume Roe-type scheme, that solves the non-linear shallow water equations with the capacity of treating the wet or dry boundary at the wave front. The momentum equations in this model are modified by adding a drag force term, thus considering the resistance effects of vegetation on tsunami waves. The accuracy of the numerical scheme and the vegetation drag force are validated by three experimental cases of dam-break flow propagation in a dry channel, solitary wave propagation in a vegetated flume, and tsunami run-up over an uneven bed. Subsequently, a numerical model is applied to simulate tsunami run-up and land inundation on actual-scale vegetated beaches and a series of sensitive analyses are conducted by comparing numerical results. The obtained numerical results suggest that 〈em〉P. odoratissimus〈/em〉 can effectively attenuate tsunami run-up and land inundation distance on coastal beaches, and a higher attenuation rate for tsunami wave can be achieved by increasing both vegetation width and vegetation density. The tsunami wave height is also an important factor that impacts the tsunami wave run-up and land inundation on vegetated beaches.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): R.D. Martin, Q. Cai, T. Garrow, C. Kapahi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉QExpy is an open source python-3 module that was developed in order to simplify the analysis of data in undergraduate physics laboratories. Through the use of this module, students can focus their time on understanding the science and the data from their experiments, rather than on processing their data. In particular, the module allows users to easily propagate uncertainties from measured quantities using a variety of techniques (derivatives, Monte Carlo), as well as to plot and fit functions to data. The interface is designed to be pedagogical so that students with no prior programming experience can be eased into using python in their introductory physics laboratories.〈/p〉〈/div〉

Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): Simon Behrendt, Thomas Dimpfl, Franziska J. Peter, David J. Zimmermann〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper shows how to quantify and test for the information flow between two time series with Shannon transfer entropy and Rényi transfer entropy using the 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si1.svg"〉〈mi〉R〈/mi〉〈/math〉 package 〈em〉RTransferEntropy〈/em〉. We discuss the methodology, the bias correction applied to calculate effective transfer entropy and outline how to conduct statistical inference. Furthermore, we describe the package in detail and demonstrate its functionality by means of several simulated processes and present an application to financial time series.〈/p〉〈/div〉

Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): Jacob L. Moore, Nathaniel R. Morgan, Mark F. Horstemeyer〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We discuss the creation and implementation of a generalized library, named ELEMENTS, of mathematical functions for supporting a very broad range of element types including: linear, quadratic, and cubic serendipity elements in 2D and 3D; high-order spectral elements; and a linear 4D element. The ELEMENTS library can be used for research and development of both continuous and discontinuous finite element methods for solving a diverse range of partial differential equations. The library has functions for calculating quantities that are commonly used in finite element methods such as the gradient of a basis function, the Jacobi matrix, the inverse Jacobi matrix, the determinant of the Jacobi matrix, and a physical position inside the element, to name a few examples. The library also supports both Gauss–Legendre and Gauss–Lobatto quadrature rules up to 8 quadrature points in each coordinate direction. The examples and discussions in this paper will focus on Lagrangian solid mechanics and dynamics, but ELEMENTS can be used for many other applications.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 396〈/p〉 〈p〉Author(s): Luigi Brugnano, Juan I. Montijano, Luis Rández〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper we study arbitrarily high-order energy-conserving methods for simulating the dynamics of a charged particle. They are derived and studied within the framework of 〈em〉Line Integral Methods (LIMs)〈/em〉, previously used for defining 〈em〉Hamiltonian Boundary Value Methods (HBVMs)〈/em〉, a class of energy-conserving Runge-Kutta methods for Hamiltonian problems. A complete analysis of the new methods is provided, which is confirmed by a few numerical tests.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 131〈/p〉 〈p〉Author(s): Ludovic Räss, Dmitriy Kolyukhin, Alexander Minakov〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We present an efficient implementation of the method for sampling spatial realisations of a 3-D random fields with given power spectrum. The method allows for a multi-scale resolution and approaches well for parallel implementations, overcoming the physical limitation of computer memory when dealing with large 3-D problems. We implement the random field generator to execute on graphical processing units (GPU) using the CUDA C programming language. We compare the memory footprint and the wall-time of our implementation to FFT-based solutions. We illustrate the efficiency of the proposed numerical method using examples of an acoustic scattering problem which can be encountered both in controlled-source and earthquake seismology. In particular, we apply our method to study the scattering of seismic waves in 3-D anisotropic random media with a particular focus on P-wave coda observations and seismic monitoring of hydrocarbon reservoirs.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 131〈/p〉 〈p〉Author(s): R. Moreno, F.J. Pérez-Gil, J.J. Pardo, A. Navarro, F.J. Tapiador〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Current computing platforms, including HPC, Grid and Cloud computing, offer many computational resources. These platforms can provide the scientific world with on-demand and scalable computing power. However, their use requires advanced computer skills. In our work, we summarize the main problems found by research scientists when they attempt to execute complex software on one or several of these platforms. As a solution to these problems, we propose the 〈em〉Science for Everyone〈/em〉 (ScifE) framework, and an intuitive and easy-to-use web interface, to perform scientific experiments on HPC or Cloud platforms. The Community Earth System Model (CESM) has been used to test our proof-of-concept framework as it is a complex scientific software used by many climate researchers. Using our framework, a user can execute CESM with custom parameters with only a few clicks on the web interface. As ScifE is generic, other software can be imported relatively easy to our framework. Additional collaborative tools are being developed for successive versions of ScifE, enhancing the practice of science.〈/p〉〈/div〉

Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): Cornelis Marcel Pieter ’t Hart, Georgios Leontaris, Oswaldo Morales-Nápoles〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉This is an update to PII: 〈a href="https://www.sciencedirect.com/science/article/pii/S2352711018300608" target="_blank"〉S2352711018300608〈/a〉〈/p〉 〈p〉In this paper, we discuss ANDURYL, which is a Python-based open source successor of the MATLAB toolbox ANDURIL. The output of ANDURYL is in good agreement with the results obtained from ANDURIL and EXCALIBUR. Additional features available in ANDURYL, and not available in its predecessors, are discussed.〈/p〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 132〈/p〉 〈p〉Author(s): Xenia Specka, Philipp Gärtner, Carsten Hoffmann, Nikolai Svoboda, Markus Stecker, Udo Einspanier, Kristian Senkler, M.A. Muqit Zoarder, Uwe Heinrich〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉A spatial data infrastructure (SDI) for the upload and provision of soil-agricultural research data in Germany was developed and launched in 2017. The precondition for the new SDI were to be compliant with the European initiative for spatial information (INSPIRE), to consider FAIR data principles, to be interoperable with other disciplinary national and international SDIs and to support dataset registrations with digital object identifiers (DOI). To meet these requirements, the new SDI had to support both the INSPIRE and DataCite metadata standards. As there was no metadata standard available that supported both metadata schemas from different disciplines, it became necessary to merge these two existing standards.〈/p〉 〈p〉The objective of this paper is to describe the developmental steps of the newly created BonaRes metadata schema for geospatial soil-agricultural research data. First, we analyzed the INSPIRE and DataCite metadata schemas, identified and compared semantically equivalent metadata elements, including properties and allowed content values, for potential mapping. In the next step, we specified the new metadata model, whose elements were derived from INSPIRE or DataCite under consideration of the previously developed mapping. Third, additional metadata elements were defined to directly describe the underlying data model.〈/p〉 〈p〉The advantages and disadvantages of the method that was applied to define the BonaRes metadata schema elements originating from INSPIRE and DataCite schemas are presented and discussed. By implementing the new metadata model, the SDIs can achieve cross-portal metadata interoperability with other INSPIRE-compliant SDIs. Furthermore, researchers who store their geospatial research data in SDIs to implement the BonaRes metadata schema can increase the visibility and findability of their research, as the data can be assigned a DOI, which is a prerequisite for data citations and data publications. The new metadata elements of the BonaRes metadata schema allows targeted dataset queries, e.g., soil pH values, yields or parameters, which were measured by a specific method, using the underlying data model, and improves the discovery and reusability of research data.〈/p〉 〈/div〉 〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S009830041930086X-fx1.jpg" width="306" alt="Image 1" title="Image 1"〉〈/figure〉〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 21 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Konstantin Pieper, K. Chad Sockwell, Max Gunzburger〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A framework for exponential time discretization of the multilayer rotating shallow water equations is developed in combination with a mimetic discretization in space. The method is based on a combination of existing exponential time differencing (ETD) methods and a careful choice of approximate Jacobians. The discrete Hamiltonian structure and conservation properties of the model are taken into account, in order to ensure stability of the method for large time steps and simulation horizons. In the case of many layers, further efficiency can be gained by a layer reduction which is based on the vertical structure of fast and slow modes. Numerical experiments on the example of a mid-latitude regional ocean model confirm long term stability for time steps increased by an order of magnitude over the explicit CFL, while maintaining accuracy for key statistical quantities.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 20 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Stéphane Zaleski, Feng Xiao〈/p〉

Description: 〈p〉Publication date: Available online 20 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Dario Collia, Marija Vukicevic, Valentina Meschini, Luigino Zovatto, Gianni Pedrizzetti〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉The fluid dynamics inside the left ventricle of the human heart is considered a potential indicator of long term cardiovascular outcome. In this respect, numerical simulations can play an important role for integrating existing technology to reproduce flow details and even conditions associated to virtual therapeutic solutions. Nevertheless, numerical models encounter serious practical difficulties in describing the interaction between flow and surrounding tissues due to the limited information inherently available in real clinical applications.〈/p〉 〈p〉This study presents a computational method for the fluid dynamics inside the left ventricle designed to be efficiently integrated in clinical scenarios. It includes an original model of the mitral valve dynamics, which describes an asymptotic behavior for tissues with no elastic stiffness other than the constrain of the geometry obtained from medical imaging; in particular, the model provides an asymptotic description without requiring details of tissue properties that may not be measurable 〈em〉in vivo〈/em〉.〈/p〉 〈p〉The advantages of this model with respect to a valveless orifice and its limitations with respect to a complete tissue modeling are verified. Its performances are then analyzed in details to ensure a correct interpretation of results. It represents a potential option when information about tissue mechanical properties is insufficient for the implementations of a full fluid-structure interaction approach.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 9 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences〈/p〉 〈p〉Author(s): Andrés F. Osorio, Sebastian Montoya-Vargas, Cesar A. Cartagena, Jairo Espinosa, Alejandro Orfila, Christian Winter〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A new approach to estimate the peak period using time stack images from a video monitoring system is presented. The method is an improvement of the one developed by Stockdon and Holman (Stockdon, H.F., Holman, R.A., 2000. Estimation of wave phase speed and nearshore bathymetry from video imagery. J. Geophys. Res. Ocean. 105, 22015–22033), (S&H) including thresholding of the image to automatically identify free foam zone in the image, improved filtering procedure and windowing of the pixel intensity series in order to avoid noisy signals and spectral leakage. Results are tested against measurements in four beaches showing an absolute error below 2 s for 75% of the analyzed images despite the variable light conditions and noisy signals. Comparison with the S&H method is given showing that the new method reduces relative error on average by 28%. These results are achieved without taking into account the camera configuration requiring only the identification of a representative amount of land pixels in order to adequately eliminate noisy signals produced by environmental light fluctuations.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: Available online 22 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): L. Nouveau, M. Ricchiuto, G. Scovazzi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We propose an extension of the embedded boundary method known as “shifted boundary method” to elliptic diffusion equations in mixed form (e.g., Darcy flow, heat diffusion problems with rough coefficients, etc.). Our aim is to obtain an improved formulation that, for linear finite elements, is at least second-order accurate for both flux and primary variable, when either Dirichlet or Neumann boundary conditions are applied. Following previous work of Nishikawa and Mazaheri in the context of residual distribution methods, we consider the mixed form of the diffusion equation (i.e., with Darcy-type operators), and introduce an enrichment of the primary variable. This enrichment is obtained exploiting the relation between the primary variable and the flux variable, which is explicitly available at nodes in the mixed formulation. The proposed enrichment mimics a formally quadratic pressure approximation, although only nodal unknowns are stored, similar to a linear finite element approximation. We consider both continuous and discontinuous finite element approximations and present two approaches: a non-symmetric enrichment, which, as in the original references, only improves the consistency of the overall method; and a symmetric enrichment, which enables a full error analysis in the classical finite element context. Combined with the shifted boundary method, these two approaches are extended to high-order embedded computations, and enable the approximation of both primary and flux (gradient) variables with second-order accuracy, independently on the type of boundary conditions applied. We also show that the the primary variable is third-order accurate, when pure Dirichlet boundary conditions are embedded.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Artificial Intelligence, Volume 275〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers in Industry, Volume 112〈/p〉 〈p〉Author(s): Patrick Dallasega, Rafael A. Rojas, Giulia Bruno, Erwin Rauch〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A special characteristic of Engineer-to-Order (ETO) construction supplier companies is that they are composed of an off-site and on-site production part. Often, their synchronization is difficult by using traditional scheduling and monitoring methodologies resulting in excessive lead times, buffer levels and, as a result, additional costs. Real-time data, made available by Industry 4.0 technologies, represent a high potential to sense and react to plan deviations as soon as they appear. The paper presents a decentralized and agile approach for scheduling and control in ETO construction supply chains. The approach was modelled and validated based on a case study of an ETO façade supplier company and a discrete event simulation. It emerged that the agile approach has its main benefits in the reduction of buffer sizes and construction lead-times. The original aspect of the article is the investigation of effects of agile scheduling and control in ETO construction supply chains.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers in Industry, Volume 112〈/p〉 〈p〉Author(s): Juan Jose Rubio, Takahiro Kashiwa, Teera Laiteerapong, Wenlong Deng, Kohei Nagai, Sergio Escalera, Kotaro Nakayama, Yutaka Matsuo, Helmut Prendinger〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Structural Health Monitoring (SHM) has benefited from computer vision and more recently, Deep Learning approaches, to accurately estimate the state of deterioration of infrastructure. In our work, we test Fully Convolutional Networks (FCNs) with a dataset of deck areas of bridges for damage segmentation. We create a dataset for delamination and rebar exposure that has been collected from inspection records of bridges in Niigata Prefecture, Japan. The dataset consists of 734 images with three labels per image, which makes it the largest dataset of images of bridge deck damage. This data allows us to estimate the performance of our method based on regions of agreement, which emulates the uncertainty of in-field inspections. We demonstrate the practicality of FCNs to perform automated semantic segmentation of surface damages. Our model achieves a mean accuracy of 89.7% for delamination and 78.4% for rebar exposure, and a weighted F1 score of 81.9%.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 20 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Complexity〈/p〉 〈p〉Author(s): Zhiying Fang, Zheng-Chu Guo, Ding-Xuan Zhou〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We study a learning algorithm for distribution regression with regularized least squares. This algorithm, which contains two stages of sampling, aims at regressing from distributions to real valued outputs. The first stage sample consists of distributions and the second stage sample is obtained from these distributions. To extract information from samples, we embed distributions to a reproducing kernel Hilbert space (RKHS) and use the second stage sample to form the regressor by a tool of mean embedding. We show error bounds in the 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si1.svg"〉〈msup〉〈mrow〉〈mi〉L〈/mi〉〈/mrow〉〈mrow〉〈mn〉2〈/mn〉〈/mrow〉〈/msup〉〈/math〉-norm and prove that the regressor is a good approximation to the regression function. We derive a learning rate which is optimal in the setting of standard least squares regression and improve the existing work. Our analysis is achieved by using a novel second order decomposition to bound operator norms.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 29 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences〈/p〉 〈p〉Author(s): Yilun Zhang, Bin Hu, Yanguo Teng, Kevin Tu, Chen Zhu〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Rate equations and kinetic parameters for about 100 minerals were programmed into a library of callable Basic language scripts for the geochemical modeling program 〈span〉Phreeqc〈/span〉 (version 3.5.0) to facilitate the application of kinetics in geochemical modeling. For most minerals, the following general equation is used: 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈mrow〉〈msub〉〈mrow〉〈mi〉r〈/mi〉〈/mrow〉〈mrow〉〈mi〉n〈/mi〉〈mi〉e〈/mi〉〈mi〉t〈/mi〉〈/mrow〉〈/msub〉〈mo linebreak="goodbreak" linebreakstyle="after"〉=〈/mo〉〈msub〉〈mrow〉〈mi〉S〈/mi〉〈/mrow〉〈mrow〉〈mi〉A〈/mi〉〈/mrow〉〈/msub〉〈munder〉〈mo〉∑〈/mo〉〈mrow〉〈mi〉j〈/mi〉〈/mrow〉〈/munder〉〈mrow〉〈msub〉〈mrow〉〈mi〉A〈/mi〉〈/mrow〉〈mrow〉〈mi〉j〈/mi〉〈/mrow〉〈/msub〉〈msup〉〈mrow〉〈mi〉e〈/mi〉〈/mrow〉〈mrow〉〈mo〉−〈/mo〉〈msub〉〈mrow〉〈mi〉E〈/mi〉〈/mrow〉〈mrow〉〈mi〉a〈/mi〉〈mo〉,〈/mo〉〈mi〉j〈/mi〉〈/mrow〉〈/msub〉〈mo〉/〈/mo〉〈mi〉R〈/mi〉〈mi〉T〈/mi〉〈/mrow〉〈/msup〉〈munder〉〈mo〉∏〈/mo〉〈mrow〉〈mi〉i〈/mi〉〈/mrow〉〈/munder〉〈mrow〉〈msup〉〈mrow〉〈msubsup〉〈mrow〉〈mi〉a〈/mi〉〈/mrow〉〈mrow〉〈mi〉i〈/mi〉〈mo〉,〈/mo〉〈mi〉j〈/mi〉〈/mrow〉〈mrow〉〈mi〉n〈/mi〉〈mi〉i〈/mi〉〈/mrow〉〈/msubsup〉〈mrow〉〈mo stretchy="true"〉(〈/mo〉〈mn〉1〈/mn〉〈mo linebreak="badbreak"〉−〈/mo〉〈msup〉〈mrow〉〈mi〉Ω〈/mi〉〈/mrow〉〈mrow〉〈msub〉〈mrow〉〈mi〉p〈/mi〉〈/mrow〉〈mrow〉〈mi〉j〈/mi〉〈/mrow〉〈/msub〉〈/mrow〉〈/msup〉〈mo stretchy="true"〉)〈/mo〉〈/mrow〉〈/mrow〉〈mrow〉〈msub〉〈mrow〉〈mi〉q〈/mi〉〈/mrow〉〈mrow〉〈mi〉j〈/mi〉〈/mrow〉〈/msub〉〈/mrow〉〈/msup〉〈/mrow〉〈/mrow〉〈/mrow〉〈/math〉〈/p〉 〈p〉where 〈em〉r〈/em〉〈sub〉net〈/sub〉 stands for the net dissolution rate of a mineral phase (mol kgw〈sup〉−1〈/sup〉 s〈sup〉−1〈/sup〉); 〈em〉j〈/em〉 the jth reaction mechanism; 〈em〉S〈/em〉〈sub〉A〈/sub〉 the surface area per unit water mass (m〈sup〉2〈/sup〉 kgw〈sup〉−1〈/sup〉); 〈em〉A〈/em〉〈sub〉〈em〉j〈/em〉〈/sub〉 the Arrhenius pre-exponential factor (mol m〈sup〉−2〈/sup〉 s〈sup〉−1〈/sup〉); 〈em〉E〈/em〉〈sub〉a,j〈/sub〉 the apparent reaction activation energy (J mol〈sup〉−1〈/sup〉); 〈em〉R〈/em〉 the universal gas constant (8.31446 J mol〈sup〉−1〈/sup〉 K〈sup〉−1〈/sup〉); 〈em〉T〈/em〉 the temperature (K); 〈em〉a〈/em〉〈sub〉〈em〉i〈/em〉〈/sub〉 the activity of aqueous species 〈em〉i〈/em〉; Ω the mineral saturation quotient. 〈em〉p〈/em〉〈sub〉〈em〉j〈/em〉〈/sub〉 and 〈em〉q〈/em〉〈sub〉〈em〉j〈/em〉〈/sub〉 are empirical fitting parameters. 〈em〉j〈/em〉 stands for the specific mechanisms of reaction. Other forms of rate equations and associated parameters programmed in the library include parallel mechanisms, Langmuir adsorption isotherm, and empirical rate equations that apply to a specific reaction mechanism or geochemical system. A separate file of PHASEs, which define the chemical stoichiometry of the phases, dissolution reactions, and equilibrium constants of the dissolution reactions, is also provided. PHREEQC requires that the names in PHASES and RATES blocks match with each other.〈/p〉 〈p〉The Basic language scripts can also be used as templates for writing other rate equations which users might wish to use. To illustrate the application of the script library, we simulated the reaction path of albite dissolution at 25 °C and 1 bar, using three rate equations and compared the results. The script and phase library and supporting materials can be downloaded from 〈a href="https://github.com/HydrogeoIU/PHREEQC-Kinetic-Library" target="_blank"〉https://github.com/HydrogeoIU/PHREEQC-Kinetic-Library〈/a〉 and doi.org/10.5967/41gq-yr13.〈/p〉 〈/div〉 〈/div〉

Description: 〈p〉Publication date: Available online 21 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Walter Boscheri, Dinshaw S. Balsara〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉In this work we present a conservative WENO Adaptive Order (AO) reconstruction operator applied to an explicit one-step Arbitrary-Lagrangian-Eulerian (ALE) discontinuous Galerkin (DG) method. The spatial order of accuracy is improved by reconstructing higher order piecewise polynomials of degree 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈mi〉M〈/mi〉〈mo linebreak="goodbreak" linebreakstyle="after"〉〉〈/mo〉〈mi〉N〈/mi〉〈/math〉, starting from the underlying polynomial solution of degree 〈em〉N〈/em〉 provided by the DG scheme. High order of accuracy in time is achieved by the ADER approach, making use of an element-local space-time Galerkin finite element predictor that arises from a one-step time integration procedure. As a result, space-time polynomials of order 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"〉〈mi〉M〈/mi〉〈mo linebreak="goodbreak" linebreakstyle="after"〉+〈/mo〉〈mn〉1〈/mn〉〈/math〉 are obtained and used to perform the time evolution of the numerical solution adopting a fully explicit DG scheme.〈/p〉 〈p〉To maintain algorithm simplicity, the mesh motion is restricted to be carried out using straight lines, hence the old mesh configuration at time 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"〉〈msup〉〈mrow〉〈mi〉t〈/mi〉〈/mrow〉〈mrow〉〈mi〉n〈/mi〉〈/mrow〉〈/msup〉〈/math〉 is connected with the new one at time 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.svg"〉〈msup〉〈mrow〉〈mi〉t〈/mi〉〈/mrow〉〈mrow〉〈mi〉n〈/mi〉〈mo linebreak="badbreak" linebreakstyle="after"〉+〈/mo〉〈mn〉1〈/mn〉〈/mrow〉〈/msup〉〈/math〉 via space-time segments, which result in space-time control volumes on which the governing equations have to be integrated in order to obtain the time evolution of the discrete solution. Our algorithm falls into the category of 〈em〉direct〈/em〉 Arbitrary-Lagrangian-Eulerian (ALE) schemes, where the governing PDE system is directly discretized relying on a space-time conservation formulation and which already takes into account the new grid geometry 〈em〉directly〈/em〉 during the computation of the numerical fluxes. A local rezoning strategy might be used in order to locally optimize the mesh quality and avoiding the generation of invalid elements with negative determinant. The proposed approach reduces to direct ALE finite volume schemes if 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg"〉〈mi〉N〈/mi〉〈mo linebreak="goodbreak" linebreakstyle="after"〉=〈/mo〉〈mn〉0〈/mn〉〈/math〉, while explicit direct ALE DG schemes are recovered in the case of 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si6.svg"〉〈mi〉N〈/mi〉〈mo linebreak="goodbreak" linebreakstyle="after"〉=〈/mo〉〈mi〉M〈/mi〉〈/math〉.〈/p〉 〈p〉In order to stabilize the DG solution, an 〈em〉a priori〈/em〉 WENO based limiting technique is employed, that makes use of the numerical solution inside the element under consideration and its neighbor cells to find a less oscillatory polynomial approximation. By using a 〈em〉modal basis〈/em〉 in a reference element, the evaluation of the oscillation indicators is very easily and efficiently carried out, hence allowing higher order modes to be properly limited, while leaving the zero-〈em〉th〈/em〉 order mode untouched for ensuring conservation.〈/p〉 〈p〉Numerical convergence rates for 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si7.svg"〉〈mn〉2〈/mn〉〈mo〉≤〈/mo〉〈mi〉N〈/mi〉〈mo〉,〈/mo〉〈mi〉M〈/mi〉〈mo〉≤〈/mo〉〈mn〉4〈/mn〉〈/math〉 are presented as well as a wide set of benchmark test problems for hydrodynamics on moving and fixed unstructured meshes.〈/p〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers in Industry, Volume 112〈/p〉 〈p〉Author(s): Rui Miao, Yuntian Gao, Liang Ge, Zihang Jiang, Jie Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉To improve the defect identification of eddy current detection signals for narrow lap welds, we propose a method, which combines continuous wavelet transform (CWT) with convolutional neural network (CNN). Firstly, a two-dimensional time-frequency diagram is generated from the one-dimensional eddy current signal through CWT, and then the time-frequency diagram of the eddy current signal is obtained as the input of CNN. In order to meet the real-time requirement of narrow overlap weld defect recognition in practical inspection, a two-stage defect recognition model is further proposed. The first stage is to detect the anomaly of narrow lap weld based on CWT and CNN, and the second stage is to classify and identify defects by combining CWT and CNN as well. Through case study, the accuracy of our method is 96.94%, which is nearly 10% higher than the traditional method. Furthermore, the actual average detection time is only 2.4 s, making the proposed approach capable of being deployed for enterprises’ online operation.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 20 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Ali Zidane, Abbas Firoozabadi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Non-planar fractures are often created in hydraulic fracturing. These irregular shape fractures may reduce the penetration into the formation; they may also improve the reservoir reach. Accurate flow simulation of two-phase compositional flows in domains with complex non-planar fractures is beyond the capabilities of current numerical models. In this work we present a higher-order numerical model for compositional two-phase flow in a domain with non-planar fractures. Fully unstructured gridding in 3D is a natural choice for description of geometry with irregular fracture shapes. We apply the concept of fracture cross-flow equilibrium (FCFE) in simulations of porous media flows with non-planar fractures. FCFE allows accurate flow and composition calculations at low CPU cost. Our implementation is in the context of the hybridized form of the mass conservative mixed finite element (MFE) and the higher-order discontinuous Galerkin (DG) method. In this work we introduce a simple and effective approach for design of non-planar fractures through the mesh interface that connects computer-aided-design (CAD) software to the mesh generator. In our algorithm we can simulate all ranges of fracture permeability accurately as opposed to other approaches where low permeability fractures affect the accuracy.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 132〈/p〉 〈p〉Author(s): D. Hasterok, M. Gard, C.M.B. Bishop, D. Kelsey〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The fundamental origins of metamorphic rocks as sedimentary or igneous are integral to the proper interpretation of a terrane’s tectonic and geodynamic evolution. In some cases, the protolith class cannot be determined from field relationships, texture, and/or compositional layering. In this study, we utilize machine learning to predict a metamorphic protolith from its major element chemistry so that accurate interpretation of the geology may proceed when the origin is uncertain or to improve confidence in field predictions. We survey the efficacy of several machine learning techniques to predict the protolith class (igneous or sedimentary) for whole rock geochemical analyses using 9 major oxides. The data are drawn from a global geochemical database with 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si1.svg"〉〈mo〉〉〈/mo〉〈/math〉533 000 geochemical analyses. In addition to metamorphic samples, igneous and sedimentary analyses are used to supplement the dataset based on their similar chemical distributions to their metamorphic counterparts. We train the classifiers on most of the data, retaining 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si2.svg"〉〈mo〉∼〈/mo〉〈/math〉10% for post-training validation. We find that the RUSBoost algorithm performs best overall, achieving a true-positive rate of 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si1.svg"〉〈mo〉〉〈/mo〉〈/math〉95% and 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si1.svg"〉〈mo〉〉〈/mo〉〈/math〉85% for igneous- and sedimentary-derived samples, respectively. Even the traditionally-difficult-to-differentiate metasedimentary and metaigneous rocks of granitic–granodioritic composition were consistently identified with a 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si1.svg"〉〈mo〉〉〈/mo〉〈/math〉75% success rate (92% for granite; 85% for granodiorite; 88% for wacke; 76% for arkose). The least correctly identified rock types were iron-rich shale (58%) and quartzolitic rocks (6%). These trained classifiers are able to classify metamorphic protoliths better than common discrimination methods, allowing for the appropriate interpretation of the chemical, physical, and tectonic contextual history of a rock. The preferred classifier is available as a MATLAB function that can be applied to a spreadsheet of geochemical analyses, returning a predicted class and estimated confidence score. We anticipate this classifier’s use as a cheap tool to aid geoscientists in accurate protolith prediction and to increase the size of global geochemical datasets where protolith information is ambiguous or not retained.〈/p〉〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 133〈/p〉 〈p〉Author(s): Georgios Panou〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A new method to convert Cartesian to geodetic coordinates is presented. The geodetic longitude is computed by a simple formula while the geodetic latitude and height are determined after the computation of the foot point of the normal line to a meridian ellipse. For this reason, the simpler form of the “latitude equation” is used and the corresponding quartic equation is solved using the Horner's scheme and the bisection method, which guarantees the convergence. Care is given to error analysis when computing the geodetic latitude and height. The performance of the proposed numerical method is examined using an extensive test set of points. The results are also validated by comparison to the exact analytical method of Vermeille and the numerical method of Fukushima. We conclude that the presented method gives accurate results for all input points, for arbitrary semiaxes of an oblate spheroid and can be generalized on a triaxial ellipsoid.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 133〈/p〉 〈p〉Author(s): S.J. Claessens〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The derivation of algorithms for the computation of geodetic coordinates from 3D Cartesian coordinates has been a very active field of research among geodesists for more than forty years. Many authors have sought the most efficient method, i.e. the method that provides the fastest computational speed, which nevertheless yields sufficient accuracy for practical applications. The problem is a special case of a more general mathematical problem that has also been studied by researchers in other fields. This paper investigates the applicability of methods by Sampson (1982, 〈em〉Computer graphics and image processing〈/em〉, 18: 97–108) and Uteshev and Goncharova (2018, 〈em〉Journal of Computational and Applied Mathematics〈/em〉, 328: 232–251) to the computation of geodetic coordinates. Both methods have been modified to make them more suitable for this particular problem. The methods are compared to several commonly used geodetic methods in terms of accuracy and computational efficiency. It is found that a simple modification improves the accuracy of the methods by ~3 orders of magnitude, and the modified method of Uteshev and Goncharova (2018) achieves an accuracy of 〈0.1 mm anywhere on the surface of the Earth. The methods are especially efficient in the computation of ellipsoidal height. As an additional result of this study, a new formulation of the well-known method by Bowring (1976, 〈em〉Survey Review〈/em〉, 23: 323–327) is derived, and it is shown to improve the computation speed of Bowring's method by ~12%–~27% compared to the conventional formulation.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 96〈/p〉 〈p〉Author(s): Rameswar Panda, Amran Bhuiyan, Vittorio Murino, Amit K. Roy-Chowdhury〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Existing approaches for person re-identification have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identification problem, where one or multiple new cameras may be temporarily on-boarded into an existing system to get additional information or added to expand an existing network. To address such a very practical problem, we propose a novel approach for adapting existing multi-camera re-identification frameworks with limited supervision. First, we formulate a domain perceptive re-identification method based on geodesic flow kernel that can effectively find the best source camera (already installed) to adapt with newly introduced target camera(s), without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Third, we develop a target-aware sparse prototype selection strategy for finding an informative subset of source camera data for data-efficient learning in resource constrained environments. Our approach can greatly increase the flexibility and reduce the deployment cost of new cameras in many real-world dynamic camera networks. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art unsupervised alternatives whilst being extremely efficient to compute.〈/p〉〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 96〈/p〉 〈p〉Author(s): Chengzu Bai, Ren Zhang, Zeshui Xu, Rui Cheng, Baogang Jin, Jian Chen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Kernel entropy component analysis (KECA) is a recently proposed dimensionality reduction approach, which has showed superiority in many pattern analysis algorithms previously based on principal component analysis (PCA). The optimized KECA (OKECA) is a state-of-the-art extension of KECA and can return projections retaining more expressive power than KECA. However, OKECA is not robust to outliers and has high computational complexities attributed to its inherent properties of L2-norm. To tackle these two problems, we propose a new variant of KECA, namely L1-norm-based KECA (L1-KECA) for data transformation and feature extraction. L1-KECA attempts to find a new kernel decomposition matrix such that the extracted features store the maximum information potential, which is measured by L1-norm. Accordingly, we present a greedy iterative algorithm which has much faster convergence than OKECA's. Additionally, L1-KECA retains OKECA's capability to obtain accurate density estimation with very few features (just one or two). Moreover, a new semi-supervised L1-KECA classifier is developed and employed into the data classification. Extensive experiments on different real-world datasets validate that our model is superior to most existing KECA-based and PCA-based approaches. Code has been also made publicly available.〈/p〉〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 96〈/p〉 〈p〉Author(s): Samitha Herath, Basura Fernando, Mehrtash Harandi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper we raise two important question, “〈strong〉1.〈/strong〉 Is temporal information beneficial in recognizing actions from still images? 〈strong〉2.〈/strong〉 Do we know how to take the maximum advantage from them?”. To answer these question we propose a novel transfer learning problem, Temporal To Still Image Learning (〈em〉i.e.〈/em〉, T2SIL) where we learn to derive temporal information from still images. Thereafter, we use a two-stream model where still image action predictions are fused with derived temporal predictions. In T2SIL, the knowledge transferring occurs from temporal representations of videos (〈em〉e.g.〈/em〉, Optical-flow, Dynamic Image representations) to still action images. Along with the T2SIL we propose a new action still image action dataset and a video dataset sharing the same set of classes. We explore three well established transfer learning frameworks (〈em〉i.e.〈/em〉, GANs, Embedding learning and Teacher Student Networks (TSNs)) in place of the temporal knowledge transfer method. The use of derived temporal information from our TSN and Embedding learning improves still image action recognition.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 97〈/p〉 〈p〉Author(s): Pooya Ashtari, Fateme Nateghi Haredasht, Hamid Beigy〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Centroid-based methods including k-means and fuzzy c-means are known as effective and easy-to-implement approaches to clustering purposes in many applications. However, these algorithms cannot be directly applied to supervised tasks. This paper thus presents a generative model extending the centroid-based clustering approach to be applicable to classification and regression tasks. Given an arbitrary loss function, the proposed approach, termed Supervised Fuzzy Partitioning (SFP), incorporates labels information into its objective function through a surrogate term penalizing the empirical risk. Entropy-based regularization is also employed to fuzzify the partition and to weight features, enabling the method to capture more complex patterns, identify significant features, and yield better performance facing high-dimensional data. An iterative algorithm based on block coordinate descent scheme is formulated to efficiently find a local optimum. Extensive classification experiments on synthetic, real-world, and high-dimensional datasets demonstrate that the predictive performance of SFP is competitive with state-of-the-art algorithms such as SVM and random forest. SFP has a major advantage over such methods, in that it not only leads to a flexible, nonlinear model but also can exploit any convex loss function in the training phase without compromising computational efficiency.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 97〈/p〉 〈p〉Author(s): Younghoon Kim, Hyungrok Do, Seoung Bum Kim〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Graph-based clustering is an efficient method for identifying clusters in local and nonlinear data patterns. Among the existing methods, spectral clustering is one of the most prominent algorithms. However, this method is vulnerable to noise and outliers. This study proposes a robust graph-based clustering method that removes the data nodes of relatively low density. The proposed method calculates the pseudo-density from a similarity matrix, and reconstructs it using a sparse regularization model. In this process, noise and the outer points are determined and removed. Unlike previous edge cutting-based methods, the proposed method is robust to noise while detecting clusters because it cuts out irrelevant nodes. We use a simulation and real-world data to demonstrate the usefulness of the proposed method by comparing it to existing methods in terms of clustering accuracy and robustness to noisy data. The comparison results confirm that the proposed method outperforms the alternatives.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Artificial Intelligence, Volume 276〈/p〉 〈p〉Author(s): Yuren Zhou, Xiaoyu He, Yi Xiang, Shaowei Cai〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Multi- and many-objective optimization problems have wide applications in the real world, and they have received growing attention from the evolutionary computation community. To promote the algorithm development in this area, numerous studies have been devoted to designing multi- and many-objective test problems. Most of these studies focus on handling complicated Pareto fronts (PFs), and the impact of the Pareto sets (PSs) has not been well-studied, although complicated PSs are prevalent in the real world. This paper presents a set of scalable test problems according to a new principle, which considers the geometrical properties of both PF and PS. A position function with a spherical form is proposed to introduce non-linear variable dependences to the PS, so as to simulate the variable dependencies in the real-world problems. According to the proposed principle, the first 〈em〉m〈/em〉 (i.e., the number of objectives) decision variables are used to form the surface of a unit hypersphere, while the rest variables are designed to optimize a certain distance function. A set of test problems are generated by the proposed principle, which are then used to investigate six representative algorithms. The experimental results indicate that the proposed test problems pose considerable difficulties to existing algorithms, calling for the need for designing new algorithms capable of handling complicated PF and PS simultaneously.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers in Industry, Volume 111〈/p〉 〈p〉Author(s): Igor Manojlović, Goran Švenda, Aleksandar Erdeljan, Milan Gavrić〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉System analysis and real-time operations in power distribution utilities require an accurate but compact load data model created on the basis of large number of consumers’ measurements modeled as high-dimensional time series. This paper proposes an algorithm for grouping time series with similar load patterns and extracting characteristic representatives of loads from the obtained groups, resulting in reduced load data model size. The proposed Time Series Grouping Algorithm combines dimensionality reduction, both partitional and hierarchical clustering and cluster validation to group time series into an optimal number of clusters based on simple parametric settings. The usefulness of the proposed algorithm is proven in a case study implemented in R language. The case study was conducted on real smart meter data from three distribution networks: one North American and the other two European. Results of the case study confirm that the proposed solution achieves high cluster validity and short execution time comparing to related algorithms. Therefore, the article’s main contribution is load pattern recognition support convenient for applications in distribution management systems.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Martin Pollack, Michele Pütz, Daniele L. Marchisio, Michael Oevermann, Christian Hasse〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉The evolution of polydisperse systems is governed by population balance equations. A group of efficient solution approaches are the moment methods, which do not solve for the number density function (NDF) directly but rather for a set of its moments. While this is computationally efficient, a specific challenge arises when describing the fluxes across a boundary in phase space for the disappearance of elements, the so-called zero-flux. The main difficulty is the missing NDF-information at the boundary, which most moment methods cannot provide. Relevant physical examples are evaporating droplets, soot oxidation or particle dissolution.〈/p〉 〈p〉In general, this issue can be solved by reconstructing the NDF close to the boundary. However, this was previously only achieved with univariate approaches, i.e. considering only a single internal variable. Many physical problems are insufficiently described by univariate population balance equations, e.g. droplets in sprays often require the temperature or the velocity to be internal coordinates in addition to the size.〈/p〉 〈p〉In this paper, we propose an algorithm, which provides an efficient multivariate approach to calculate the zero-fluxes. The algorithm employs the Extended Quadrature Method of Moments (EQMOM) with Beta and Gamma kernel density functions for the marginal NDF reconstruction and a polynomial or spline for the other conditional dimensions. This combination allows to reconstruct the entire multivariate NDF and based on this, expressions for the disappearance flux are derived. An algorithm is proposed for the whole moment inversion and reconstruction process. It is validated against a suite of test cases with increasing complexity. The influence of the number of kernel density functions and the configuration of the polynomials and splines on the accuracy is discussed. Finally, the associated computational costs are evaluated.〈/p〉 〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Antoine Vermeil de Conchard, Huina Mao, Romain Rumpler〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Effective treatment of unbounded domains using artificial truncating boundaries are essential in numerical simulation, e.g. using the Finite Element Method (FEM). Among these, Perfectly Matched Layers (PML) have proved to be particularly efficient and flexible. However, an efficient handling of frequency sweeps is not trivial with such absorbing layers since the formulation inherently contains coupled space- and frequency-dependent terms. Using the FEM, this may imply generating system matrices at each step of the frequency sweep. In this paper, an approximation is proposed in order to allow for efficient frequency sweeps. The performance and robustness of the proposed approximation is presented on 2D and 3D acoustic cases. A generic, robust way to truncate the acoustic domain efficiently is also proposed, tested on a range of test cases and for different frequency regions.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 97〈/p〉 〈p〉Author(s): Qiong Wang, Lu Zhang, Wenbin Zou, Kidiyo Kpalma〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we present a novel method for salient object detection in videos. Salient object detection methods based on background prior may miss salient region when the salient object touches the frame borders. To solve this problem, we propose to detect the whole salient object via the adjunction of virtual borders. A guided filter is then applied on the temporal output to integrate the spatial edge information for a better detection of the salient object edges. At last, a global spatio-temporal saliency map is obtained by combining the spatial saliency map and the temporal saliency map together according to the entropy. The proposed method is assessed on three popular datasets (Fukuchi, FBMS and VOS) and compared to several state-of-the-art methods. The experimental results show that the proposed approach outperforms the tested methods.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): Maxime Theillard, David Saintillan〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We present a new framework for the efficient simulation of the dynamics of active fluids in complex two- and three-dimensional microfluidic geometries. Focusing on the case of a suspension of microswimmers such as motile bacteria, we adopt a continuum mean-field model based on partial differential equations for the evolution of the concentration, polarization and nematic tensor fields, which are nonlinearly coupled to the Navier-Stokes equations for the fluid flow driven by internal active stresses. A level set method combined with an adaptive mesh refinement scheme on Quad-/Octree grids is used to capture complex domain shapes while refining the solution near boundaries or in the neighborhood of sharp gradients. A hybrid finite volumes/finite differences method is implemented in which the concentration field is treated using finite volumes to ensure mass conservation, while the polarization and nematic alignment fields are treated using a combination of finite differences and finite volumes for enhanced accuracy. The governing equations for these fields are solved along with the Navier-Stokes equations, which are evolved using an unconditionally stable projection solver. We illustrate the versatility and robustness of our method by analyzing spontaneous active flows in various two- and three-dimensional systems. Our results show excellent agreement with previous models and experiments and pave the way for further developments in active microfluidics.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): Jiangming Xie, M. Yvonne Ou, Liwei Xu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Poroelastic materials play an important role in biomechanical and geophysical research. In this paper, we investigate wave propagation in orthotropic poroelastic media by studying the time-domain poroelastic wave equations. Both the low frequency Biot's (LF-Biot) equations and the Biot-Johnson-Koplik-Dashen (Biot-JKD) model are considered. In LF-Biot equations, the dissipation terms are proportional to the relative velocity between the fluid and the solid by a constant. Contrast to this, the dissipation terms in the Biot-JKD model are in the form of time convolution (memory) as a result of the frequency-dependence of fluid-solid interaction at the underlying microscopic scale in the frequency domain. The dynamic tortuosity and permeability described by Darcy's law are two crucial factors in this problem, and highly linked to the viscous force. In the Biot model, the key difficulty is to handle the viscous term when the pore fluid is viscous. In the Biot-JKD model, the convolution operator involves order 1/2 shifted fractional derivatives in the time domain, which is challenging to discretize.〈/p〉 〈p〉In this work, a new method of the multipoint Padé (or Rational) approximation for Stieltjes function is applied to approximate the JKD dynamic tortuosity and then an augmented system of Biot-JKD model is obtained, where the kernel of the memory term is replaced by the finite auxiliary variables satisfying a local system of ordinary differential equations. The Runge-Kutta discontinuous Galerkin (RKDG) method with the un-splitting method is used to compute the numerical solution, and numerical examples are presented to demonstrate the high order accuracy and stability of the method. Compared with the existing approaches for solving the Biot-JKD equations, the augmented system presented here require neither the storage of solution history nor the computation of the flux of the auxiliary variables.〈/p〉 〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): William C. Tyson, Gary K. Yan, Christopher J. Roy, Carl F. Ollivier-Gooch〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A higher-order accurate discretization error estimation procedure for finite-volume schemes is presented. Discretization error estimates are computed using the linearized error transport equations (ETE). ETE error estimates are applied as a correction to the primal solution. The ETE are then relinearized about the corrected primal solution, and discretization error estimates are recomputed. This process, referred to as ETE relinearization, is performed in an iterative manner to successively increase the accuracy of discretization error estimates. Under certain conditions, ETE relinearization is shown to correct error estimates, or equivalently the entire primal solution, to higher-order accuracy. In terms of computational cost, ETE relinearization has a competitive advantage over conventional higher-order discretizations when used as a form of defect correction for the primal solution. Furthermore, ETE relinearization is shown to be particularly useful for problems where the error incurred by the linearization of the ETE cannot be neglected. Results are presented for several steady-state inviscid and viscous flow problems using both structured and unstructured meshes.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): Yu Li, Richard Mikaël Slevinsky〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We present a unified treatment of the Fourier spectra of spherically symmetric nonlocal diffusion operators. We develop numerical and analytical results for the class of kernels with weak algebraic singularity as the distance between source and target tends to 0. Rapid algorithms are derived for their Fourier spectra with the computation of each eigenvalue independent of all others. The algorithms are trivially parallelizable, capable of leveraging more powerful compute environments, and the accuracy of the eigenvalues is individually controllable. The algorithms include a Maclaurin series and a full divergent asymptotic series valid for any 〈em〉d〈/em〉 spatial dimensions. Using Drummond's sequence transformation, we prove linear complexity recurrence relations for degree-graded sequences of numerators and denominators in the rational approximations to the divergent asymptotic series. These relations are important to ensure that the algorithms are efficient, and also increase the numerical stability compared with the conventional algorithm with quadratic complexity.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): Hua Shen, Matteo Parsani〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We construct a space-time conservation element and solution element (CESE) scheme for solving the compressible Euler equations on moving meshes (CESE-MM) which allow an arbitrary motion for each of the mesh points. The scheme is a direct extension of a purely Eulerian CESE scheme that was previously implemented on hybrid unstructured meshes (Shen et al. (2015) [43]). It adopts a staggered mesh in space and time such that the physical variables are continuous across the interfaces of the adjacent space-time control volumes and, therefore, a Riemann solver is not required to calculate interface fluxes or the node velocities. Moreover, the staggered mesh can significantly alleviate mesh tangles so that the time step can be kept at an acceptable level without using any rezoning operation. The discretization of the integral space-time conservation law is completely based on the physical space-time control volume, thereby satisfying the physical and geometrical conservation laws. Plenty of numerical examples are carried out to validate the accuracy and robustness of the CESE-MM scheme.〈/p〉〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 96〈/p〉 〈p〉Author(s): Zhuoyao Zhong, Lei Sun, Qiang Huo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Although Faster R-CNN based text detection approaches have achieved promising results, their localization accuracy is not satisfactory in certain cases due to their sub-optimal bounding box regression based localization modules. In this paper, we address this problem and propose replacing the bounding box regression module with a novel LocNet based localization module to improve the localization accuracy of a Faster R-CNN based text detector. Given a proposal generated by a region proposal network (RPN), instead of directly predicting the bounding box coordinates of the concerned text instance, the proposal is enlarged to create a search region so that an “In-Out” conditional probability to each row and column of this search region is assigned, which can then be used to accurately infer the concerned bounding box. Furthermore, we present a simple yet effective two-stage approach to convert the difficult multi-oriented text detection problem to a relatively easier horizontal text detection problem, which makes our approach able to robustly detect multi-oriented text instances with accurate bounding box localization. Experiments demonstrate that the proposed approach boosts the localization accuracy of Faster R-CNN based text detectors significantly. Consequently, our new text detector has achieved superior performance on both horizontal (ICDAR-2011, ICDAR-2013 and MULTILIGUL) and multi-oriented (MSRA-TD500, ICDAR-2015) text detection benchmark tasks.〈/p〉〈/div〉

Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): Giorgio Speranza, Roberto Canteri〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Data analysis and plotting is an important part of the research work accompanying any scientist. Once the experiments are concluded, generally a software allowing data reduction such as selection of background and its subtraction, peak fitting, graphical visualization is used to obtain a correct interpretation of the results. RxpsG is a public domain software with an easy user friendly interface oriented to X-ray Photoelectron Spectroscopy (XPS) and Auger Electron Spectroscopy (AES) data manipulation based on the R platform. All the features needed to analyze XPS, AES spectra are implemented and the software allows an immediate data reporting. Although the RxpsG is primarily devoted to electron and photoelectron spectral analysis, it allows any data in text format to be loaded and processed. RxpsG is a project open to contributions and implementation of new procedures. In this work we will describe the potentialities of the software and the more important features.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S2352711019300378-fx1.jpg" width="301" alt="Graphical abstract for this article" title=""〉〈/figure〉〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Artificial Intelligence, Volume 276〈/p〉 〈p〉Author(s): Federico Cerutti, Massimiliano Giacomin, Mauro Vallati〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper we illustrate the design choices that led to the development of 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈mi mathvariant="sans-serif"〉ArgSemSAT〈/mi〉〈/math〉, the winner of the preferred semantics track at the 2017 International Competition on Computational Models of Arguments (ICCMA 2017), a biennial contest on problems associated to the Dung's model of abstract argumentation frameworks, widely recognised as a fundamental reference in computational argumentation. The algorithms of 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈mi mathvariant="sans-serif"〉ArgSemSAT〈/mi〉〈/math〉 are based on multiple calls to a SAT solver to compute complete labellings, and on encoding constraints to drive the search towards the solution of decision and enumeration problems. In this paper we focus on preferred semantics (and incidentally stable as well), one of the most popular and complex semantics for identifying acceptable arguments. We discuss our design methodology that includes a systematic exploration and empirical evaluation of labelling encodings, algorithmic variations and SAT solver choices. In designing the successful 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈mi mathvariant="sans-serif"〉ArgSemSAT〈/mi〉〈/math〉, we discover that: (1) there is a labelling encoding that appears to be universally better than other, logically equivalent ones; (2) composition of different techniques such as AllSAT and enumerating stable extensions when searching for preferred semantics brings advantages; (3) injecting domain specific knowledge in the algorithm design can lead to significant improvements.〈/p〉〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 133〈/p〉 〈p〉Author(s): Weiming Hu, Guido Cervone〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉The Analog Ensemble is a statistical technique that generates probabilistic forecasts using a current deterministic prediction, a set of historical predictions, and the associated observations. It generates ensemble forecasts by first identifying the most similar past predictions to the current one, and then summarizing the corresponding observations. This is a computationally efficient solution for ensemble modeling because it does not require multiple numerical weather prediction simulations, but a single model realization.〈/p〉 〈p〉Despite this intrinsic computational efficiency, the required computation can grow very large because atmospheric models are routinely run with increasing resolutions. For example, the North American Mesoscale forecast system contains over 262 792 grid points to generate a 12 km prediction. The North American Mesoscale model generally uses a structured grid to represent the domain, despite the fact that certain physical changes occur non-uniformly across space and time. For example, temperature changes tend to occur more rapidly in mountains than plains.〈/p〉 〈p〉An evolutionary algorithm is proposed to dynamically and automatically learn the optimal unstructured grid pattern. This iterative evolutionary algorithm is guided by Darwinian evolutionary rule generation and instantiation to identify grid vertices. Analog computations are performed only at vertices. Therefore, minimizing the number of vertices and identifying their locations are paramount to optimizing the available computational resources, minimizing queue time, and ultimately achieving better results. The optimal unstructured grid is then reused to guide the predictions for a variety of applications like temperature and wind speed.〈/p〉 〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): S. Dargaville, A.G. Buchan, R.P. Smedley-Stevenson, P.N. Smith, C.C. Pain〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper describes an angular adaptivity algorithm for Boltzmann transport applications which uses P〈sub〉〈em〉n〈/em〉〈/sub〉 and filtered P〈sub〉〈em〉n〈/em〉〈/sub〉 expansions, allowing for different expansion orders across space/energy. Our spatial discretisation is specifically designed to use less memory than competing DG schemes and also gives us direct access to the amount of stabilisation applied at each node. For filtered P〈sub〉〈em〉n〈/em〉〈/sub〉 expansions, we then use our adaptive process in combination with this net amount of stabilisation to compute a spatially dependent filter strength that does not depend on 〈em〉a priori〈/em〉 spatial information. This applies heavy filtering only where discontinuities are present, allowing the filtered P〈sub〉〈em〉n〈/em〉〈/sub〉 expansion to retain high-order convergence where possible. Regular and goal-based error metrics are shown and both the adapted P〈sub〉〈em〉n〈/em〉〈/sub〉 and adapted filtered P〈sub〉〈em〉n〈/em〉〈/sub〉 methods show significant reductions in DOFs and runtime. The adapted filtered P〈sub〉〈em〉n〈/em〉〈/sub〉 with our spatially dependent filter shows close to fixed iteration counts and up to high-order is even competitive with P〈sup〉0〈/sup〉 discretisations in problems with heavy advection.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): Jingwei Hu, Shi Jin, Ruiwen Shu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The Boltzmann equation may contain uncertainties in initial/boundary data or collision kernel. To study the impact of these uncertainties, a stochastic Galerkin (sG) method was proposed in [18] and studied in the kinetic regime. When the system is close to the fluid regime (the Knudsen number is small), the method would become prohibitively expensive due to the stiff collision term. In this work, we develop efficient sG methods for the Boltzmann equation that work for a wide range of Knudsen numbers, and investigate, in particular, their behavior in the fluid regime.〈/p〉〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 96〈/p〉 〈p〉Author(s): Chunfeng Song, Yongzhen Huang, Yan Huang, Ning Jia, Liang Wang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Gait recognition is one of the most important techniques for human identification at a distance. Most current gait recognition frameworks consist of several separate steps: silhouette segmentation, feature extraction, feature learning, and similarity measurement. These modules are mutually independent with each part fixed, resulting in a suboptimal performance in challenging conditions. In this paper, we integrate those steps into one framework, i.e., an end-to-end network for gait recognition, named 〈strong〉GaitNet〈/strong〉. It is composed of two convolutional neural networks: one corresponds to gait segmentation, and the other corresponds to classification. The two networks are modeled in one joint learning procedure which can be trained jointly. This strategy greatly simplifies the traditional step-by-step manner and is thus much more efficient for practical applications. Moreover, joint learning can automatically adjust each part to fit the global optimal objective, leading to obvious performance improvement over separate learning. We evaluate our method on three large scale gait datasets, including CASIA-B, SZU RGB-D Gait and a newly built database with complex dynamic outdoor backgrounds. Extensive experimental results show that the proposed method is effective and achieves the state-of-the-art results. The code and data will be released upon request.〈/p〉〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 96〈/p〉 〈p〉Author(s): Chuan-Xian Ren, Xiao-Lin Xu, Zhen Lei〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Person re-identification (re-ID) is to match different images of the same pedestrian. It has attracted increasing research interest in pattern recognition and machine learning. Traditionally, person re-ID is formulated as a metric learning problem with binary classification output. However, higher order relationship, such as triplet closeness among the instances, is ignored by such pair-wise based metric learning methods. Thus, the discriminative information hidden in these data is insufficiently explored. This paper proposes a new structured loss function to push the frontier of the person re-ID performance in realistic scenarios. The new loss function introduces two margin parameters. They operate as bounds to remove positive pairs of very small distances and negative pairs of large distances. A trade-off coefficient is assigned to the loss term of negative pairs to alleviate class-imbalance problem. By using a linear function with the margin-based objectives, the gradients 〈em〉w.r.t.〈/em〉 weight matrices are no longer dependent on the iterative loss values in a multiplicative manner. This makes the weights update process robust to large iterative loss values. The new loss function is compatible with many deep learning architectures, thus, it induces new deep network with pair-pruning regularization for metric learning. To evaluate the performance of the proposed model, extensive experiments are conducted on benchmark datasets. The results indicate that the new loss together with the ResNet-50 backbone has excellent feature representation ability for person re-ID.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Qing Pan, Timon Rabczuk, Gang Xu, Chong Chen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We investigate the isogeometric analysis for surface PDEs based on the extended Loop subdivision approach. The basis functions consisting of quartic box-splines corresponding to each subdivided control mesh are utilized to represent the geometry exactly, and construct the solution space for dependent variables as well, which is consistent with the concept of isogeometric analysis. The subdivision process is equivalent to the 〈em〉h〈/em〉-refinement of NURBS-based isogeometric analysis. The performance of the proposed method is evaluated by solving various surface PDEs, such as surface Laplace-Beltrami harmonic/biharmonic/triharmonic equations, which are defined on the limit surfaces of extended Loop subdivision for different initial control meshes. Numerical experiments show that the proposed method has desirable performance in terms of the accuracy, convergence and computational cost for solving the above surface PDEs defined on both open and closed surfaces. The proposed approach is proved to be second-order accuracy in the sense of 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈msup〉〈mrow〉〈mi〉L〈/mi〉〈/mrow〉〈mrow〉〈mn〉2〈/mn〉〈/mrow〉〈/msup〉〈/math〉-norm with theoretical and/or numerical results, which is also outperformed over the standard linear finite element by several numerical comparisons.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Arthur E.P. Veldman〈/p〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 97〈/p〉 〈p〉Author(s): Shuzhao Li, Huimin Yu, Roland Hu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Person attributes are often exploited as mid-level human semantic information to help promote the performance of person re-identification task. In this paper, unlike most existing methods simply taking the attribute learning as a classification problem, we perform it in a different way with the motivation that attributes are related to specific local regions, which refers to the perceptual ability of attributes. We utilize the process of attribute detection to generate corresponding attribute-part detectors, whose invariance to many influences like poses and camera views can be guaranteed. With detected local part regions, our model extracts local part features to handle the body part misalignment problem, which is another major challenge for person re-identification. The local descriptors are further refined by fused attribute information to eliminate interferences caused by detection deviation. Finally, the refined local feature works together with a holistic-level feature to constitute our final feature representation. Extensive experiments on two popular benchmarks with attribute annotations demonstrate the effectiveness of our model and competitive performance compared with state-of-the-art algorithms.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S003132031930319X-fx1.jpg" width="301" alt="Graphical abstract for this article" title=""〉〈/figure〉〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 97〈/p〉 〈p〉Author(s): Xin Wei, Hui Wang, Bryan Scotney, Huan Wan〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Face recognition has achieved great success owing to the fast development of deep neural networks in the past few years. Different loss functions can be used in a deep neural network resulting in different performance. Most recently some loss functions have been proposed, which have advanced the state of the art. However, they cannot solve the problem of 〈em〉margin bias〈/em〉 which is present in class imbalanced datasets, having the so-called long-tailed distributions. In this paper, we propose to solve the margin bias problem by setting a minimum margin for all pairs of classes. We present a new loss function, Minimum Margin Loss (MML), which is aimed at enlarging the margin of those overclose class centre pairs so as to enhance the discriminative ability of the deep features. MML, together with Softmax Loss and Centre Loss, supervises the training process to balance the margins of all classes irrespective of their class distributions. We implemented MML in Inception-ResNet-v1 and conducted extensive experiments on seven face recognition benchmark datasets, MegaFace, FaceScrub, LFW, SLLFW, YTF, IJB-B and IJB-C. Experimental results show that the proposed MML loss function has led to new state of the art in face recognition, reducing the negative effect of margin bias.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Massimiliano Ferronato, Andrea Franceschini, Carlo Janna, Nicola Castelletto, Hamdi A. Tchelepi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This work discusses a general approach for preconditioning the block Jacobian matrix arising from the discretization and linearization of coupled multiphysics problem. The objective is to provide a fully algebraic framework that can be employed as a starting point for the development of specialized algorithms exploiting unique features of the specific problem at hand. The basic idea relies on approximately computing an operator able to decouple the different processes, which can then be solved independently one from the other. In this work, the decoupling operator is computed by extending the theory of block sparse approximate inverses. The proposed approach is implemented for two multiphysics applications, namely the simulation of a coupled poromechanical system and the mechanics of fractured media. The numerical results obtained in experiments taken from real-world examples are used to analyze and discuss the properties of the preconditioner.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 16 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Edoardo Zoni, Yaman Güçlü〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉A common strategy in the numerical solution of partial differential equations is to define a uniform discretization of a tensor-product multi-dimensional logical domain, which is mapped to a physical domain through a given coordinate transformation. By extending this concept to a multi-patch setting, simple and efficient numerical algorithms can be employed on relatively complex geometries. The main drawback of such an approach is the inherent difficulty in dealing with singularities of the coordinate transformation.〈/p〉 〈p〉This work suggests a comprehensive numerical strategy for the common situation of disk-like domains with a singularity at a unique pole, where one edge of the rectangular logical domain collapses to one point of the physical domain (for example, a circle). We present robust numerical methods for the solution of Vlasov-like hyperbolic equations coupled to Poisson-like elliptic equations in such geometries. We describe a semi-Lagrangian advection solver that employs a novel set of coordinates, named pseudo-Cartesian coordinates, to integrate the characteristic equations in the whole domain, including the pole, and a finite element elliptic solver based on globally 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈msup〉〈mrow〉〈mi mathvariant="script"〉C〈/mi〉〈/mrow〉〈mrow〉〈mn〉1〈/mn〉〈/mrow〉〈/msup〉〈/math〉 smooth splines (Toshniwal et al., 2017). The two solvers are tested both independently and on a coupled model, namely the 2D guiding-center model for magnetized plasmas, equivalent to a vorticity model for incompressible inviscid Euler fluids. The numerical methods presented show high-order convergence in the space discretization parameters, uniformly across the computational domain, without effects of order reduction due to the singularity. Dedicated tests show that the numerical techniques described can be applied straightforwardly also in the presence of point charges (equivalently, point-like vortices), within the context of particle-in-cell methods.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 16 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Maria Giuseppina Chiara Nestola, Barna Becsek, Hadi Zolfaghari, Patrick Zulian, Dario De Marinis, Rolf Krause, Dominik Obrist〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉We present a novel framework inspired by the Immersed Boundary Method for predicting the fluid-structure interaction of complex structures immersed in laminar, transitional and turbulent flows.〈/p〉 〈p〉The key elements of the proposed fluid-structure interaction framework are 1) the solution of elastodynamics equations for the structure, 2) the use of a high-order Navier–Stokes solver for the flow, and 3) the variational transfer (〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈msup〉〈mrow〉〈mi〉L〈/mi〉〈/mrow〉〈mrow〉〈mn〉2〈/mn〉〈/mrow〉〈/msup〉〈/math〉-projection) for coupling the solid and fluid subproblems.〈/p〉 〈p〉The dynamic behavior of a deformable structure is simulated in a finite element framework by adopting a fully implicit scheme for its temporal integration. It allows for mechanical constitutive laws including inhomogeneous and fiber-reinforced materials.〈/p〉 〈p〉The Navier–Stokes equations for the incompressible flow are discretized with high-order finite differences which allow for the direct numerical simulation of laminar, transitional and turbulent flows.〈/p〉 〈p〉The structure and the flow solvers are coupled by using an 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈msup〉〈mrow〉〈mi〉L〈/mi〉〈/mrow〉〈mrow〉〈mn〉2〈/mn〉〈/mrow〉〈/msup〉〈/math〉-projection method for the transfer of velocities and forces between the fluid grid and the solid mesh. This strategy allows for the numerical solution of coupled large scale problems based on nonconforming structured and unstructured grids. The transfer between fluid and solid limits the convergence order of the flow solver close to the fluid-solid interface.〈/p〉 〈p〉The framework is validated with the Turek–Hron benchmark and a newly proposed benchmark modelling the flow-induced oscillation of an inert plate. A three-dimensional simulation of an elastic beam in transitional flow is provided to show the solver's capability of coping with anisotropic elastic structures immersed in complex fluid flow.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 16 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Min Chai, Kun Luo, Changxiao Shao, Haiou Wang, Jianren Fan〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper proposes a finite difference discretization method for simulations of heat and mass transfer with Robin boundary conditions on irregular domains. The level set method is utilized to implicitly capture the irregular evolving interface, and the ghost fluid method to address variable discontinuities on the interface. Special care has been devoted to providing ghost values that are restricted by the Robin boundary conditions. Specifically, it is done in two steps: 1) calculate the normal derivative in cells adjacent to the interface by reconstructing a linear polynomial system; 2) successively extrapolate the normal derivative and the ghost value in the normal direction using a linear partial differential equation approach. This method produces second-order accurate solutions for both the Poisson and heat equations with Robin boundary conditions, and first-order accurate solutions for the Stefan problems. The solution gradients are of first-order accuracy, as expected. It is easy to implement in three-dimensional configurations, and can be straightforwardly generalized into higher-order variants. The method thus represents a promising tool for practical heat and mass transfer problems involving Robin boundary conditions.〈/p〉〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 133〈/p〉 〈p〉Author(s): AbdAllah A. Youssef, Abeeb A. Awotunde〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉The existence of free-flow regions (fractures, vugs and caves) inside porous media at multiple-scales affects the flow paths in aquifers and underground reservoirs. To simulate advection-dominant phenomena with a reasonable level of accuracy, the Stokes-Brinkman and the Darcy-Stokes models have been separately proposed to model the simultaneous flow of fluids in the free-flow regions and the porous regions of karst reservoirs. However, the computational cost associated with the use of Stokes-Brinkman is much higher when compared with the use of the Darcy's model while Darcy-Stokes requires the implementation of the interface conditions.〈/p〉 〈p〉This paper introduces an approximation technique that assigns different permeability values to the grids in the free-flow region. The technique provides a way for computing apparent permeability values for different grid locations in the free-flow regions. This method makes it possible for the Darcy's model to closely approximate the Stokes-Brinkman's model. This proposed approach makes it possible to replace the Stokes-Brinkman model with the Darcy's model without significant loss of accuracy in modelling flood fronts. The values of the apparent permeabilities of the grids inside the free-flow region and the surrounding porous media are calculated from the analytical solution of Stokes-Brinkman's equation. Four examples are presented to illustrate the effectiveness of this technique. In the first three examples, the principal axes of the cave (free-flow region) are aligned with those of the porous media. The fourth example consists of a more complex scenario in which the principal axes of the cave are not in alignment with those of the porous media.〈/p〉 〈/div〉 〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 133〈/p〉 〈p〉Author(s): Shuai Han, Heng Li, Mingchao Li, Xiaochun Luo〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉One of the most widely accepted field methods used by geological engineers to measure rock surface strengths is by striking a rock with a geological hammer and using the emitted sound frequencies to determine strength. While the method is a convenient, it is also subjective. To this end, we propose a new method of measurement based on spectrograms using deep convolutional networks. The spectrograms collected through striking rocks with a geological hammer is the input variable to a deep learning model, which is the Inception-v3 model in this study and has a 93% classification accuracy. We then introduced a probability matrix and an error correction model to estimate the surface strength of rocks from the classification results. The experimental results show our method has high potential to underpin the implementation of efficient and objective measurements of rock surface strength in the field.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): Uwe Graichen, Roland Eichardt, Jens Haueisen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉SpharaPy is a Python implementation of a new approach for spatial harmonic analysis (SPHARA). SPHARA extends the classical spatial Fourier analysis to non-uniformly positioned samples on arbitrary surfaces in 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si1.svg"〉〈msup〉〈mrow〉〈mi mathvariant="double-struck"〉R〈/mi〉〈/mrow〉〈mrow〉〈mn〉3〈/mn〉〈/mrow〉〈/msup〉〈/math〉. The basis functions (BF) used by SPHARA are determined by the eigenanalysis of the discrete Laplace–Beltrami operator, which is defined on a triangular mesh specified by the spatial sampling points. The SpharaPy Python toolbox provides classes and functions to compute the SPHARA BF for data analysis and synthesis as well as classes to design and apply spatial filters. An illustrative example of applying the SpharaPy package in the field of biosignal processing using electroencephalography data is presented.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Weidong Li, Wei Li, Pai Song, Hao Ji〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper presents an efficient, low memory cost, implicit finite volume lattice Boltzmann method (FVLBM) based on conservation moments acceleration for steady nearly incompressible flows. In the proposed scheme, not as the conventional implicit schemes, both the micro lattice Boltzmann equations (LBE) and the associated conservation moment equations are solved by the matrix-free, lower-upper symmetric Gauss-Seidel scheme (LUSGS) and the conservation moment equations are used to predict equilibrium distribution functions at the new time, which eliminates the storage of the Jacobian matrix of the collision term in the implicit LBE system and provides a driving force for the fast convergence of the LBE. Moreover, by utilizing the projection matrix and the collision invariant, we can construct the fluxes of the moment equations efficiently from the fluxes of the LBE and avoid the time-consuming reconstruction procedure for obtaining the fluxes of the moment equations. To demonstrate the accuracy and high efficiency of the proposed scheme, comparison studies of simulation results of several two-dimensional testing cases by the present scheme and an explicit FVLBM are conducted and numerical results show that the proposed implicit scheme can be as accurate as its explicit counterpart with 1 or 2 orders times speedup.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Laurent Muscat, Guillaume Puigt, Marc Montagnac, Pierre Brenner〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper addresses how two time integration schemes, the Heun's scheme for explicit time integration and the second-order Crank-Nicolson scheme for implicit time integration, can be coupled spatially. This coupling is the prerequisite to perform a coupled Large Eddy Simulation/Reynolds Averaged Navier-Stokes computation in an industrial context, using the implicit time procedure for the boundary layer (RANS) and the explicit time integration procedure in the LES region. The coupling procedure is designed in order to switch from explicit to implicit time integrations as fast as possible, while maintaining stability. After introducing the different schemes, the paper presents the initial coupling procedure adapted from a published reference and shows that it can amplify some numerical waves. An alternative procedure, studied in a coupled time/space framework, is shown to be stable and with spectral properties in agreement with the requirements of industrial applications. The coupling technique is validated with standard test cases, ranging from one-dimensional to three-dimensional flows.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers in Industry, Volume 111〈/p〉 〈p〉Author(s): Jaspreet Singh Walia, Heikki Hämmäinen, Kalevi Kilkki, Seppo Yrjölä〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Factories become increasingly dependent on network connectivity. The next generation of mobile communications, 5G, will enable better flexibility and service quality through network slicing. Network slicing is a means of creating logically separated use case specific virtual networks over the same physical network. However, there is a lack of techno-economic research related to the management of network slices. Network slice management needs to take into account the multiple network domains, business actors and value networks involved in a vertical such as smart factories. The key for network slices to succeed where other resource reservation and quality of service technologies have previously failed is with well-defined feasible management models and strategies. In this paper, we focus on network slice management, possible strategies and implications for a smart factory. We study a state-of-the-art electronics assembly factory in Finland to find existing need for network slicing and missing capabilities to support smart factory use cases. Next, we define use case specific network slices and develop a network slice management model based on 5G specifications. The model allows for distribution of network functions between business actors over multiple network domains. The value network analysis method is utilized to develop alternative configurations that constitute the network slicing strategies facilitated by the model. Factory managers can decide on the most feasible strategy based on traditional factors such as make or buy, security, and level of automation. The strategies also differ in their technical applicability to different use cases. A feasibility study reveals the strategic differences from factory, local network operator and large mobile network operator perspectives.〈/p〉〈/div〉

Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): Carlo Bianchi, Amanda D. Smith〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The Localized Actual Meteorological Year File Creator (LAF) application provides web-based access to real meteorological data and processes it into a weather file suitable for building energy modeling. Building energy consumption is affected by what is inside the building (such as occupants, appliances, HVAC systems, etc.) and by what is outside: the weather conditions the building is exposed to. However, freely available weather data files are limited to a number of specific locations, usually airports, which are often located away from city centers where buildings are concentrated. The authors have developed a new tool that supports the investigation and quantification of micro-climate conditions on building energy consumption. LAF is built on the Python open-source programming language and has a Graphical User Interface (GUI) that allows users to create custom weather data files for building energy simulations. Many sets of actual meteorological year weather data for long time periods are publicly available online (such as the MesoWest database) for thousands of locations in the US. LAF selects weather data according to user specifications and automatically processes it through an API across multiple weather stations and multiple time periods. The user may easily select a specific location, time frame, and time step that best meets their needs. This article presents a useful tool for energy modelers, building designers and operators to assist with building performance analysis and optimization.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): Benjamin Stadlbauer, Andrea Cossettini, José A. Morales E., Daniel Pasterk, Paolo Scarbolo, Leila Taghizadeh, Clemens Heitzinger, Luca Selmi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Massively parallel nanosensor arrays fabricated with low-cost CMOS technology represent powerful platforms for biosensing in the Internet-of-Things (IoT) and Internet-of-Health (IoH) era. They can efficiently acquire “big data” sets of dependable calibrated measurements, representing a solid basis for statistical analysis and parameter estimation.〈/p〉 〈p〉In this paper we propose Bayesian estimation methods to extract physical parameters and interpret the statistical variability in the measured outputs of a dense nanocapacitor array biosensor. Firstly, the physical and mathematical models are presented. Then, a simple 1D-symmetry structure is used as a validation test case where the estimated parameters are also known 〈em〉a-priori〈/em〉. Finally, we apply the methodology to the simultaneous extraction of multiple physical and geometrical parameters from measurements on a CMOS pixelated nanocapacitor biosensor platform.〈/p〉 〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Meiling Zhao, Na Zhu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We study the problem of electromagnetic scattering by multiple open cavities embedded in an infinite ground plane with high wave numbers. The problem can be described by a series of Helmholtz equations with coupled boundary conditions. We develop a sixth-order finite difference scheme to discretize the coupled Helmholtz equations. By Gaussian elimination in the vertical direction and Fourier transform in the horizontal direction, we can reduce the multiple cavity scattering problem to an aperture linear system. However, in the situation of high wave numbers, the condition number of the coefficient matrix of the reduced linear system is especially large and the system tends to be ill-conditioned. The convergence histories of most iterative methods become oscillating which consume considerable computations and memory spaces. In order to overcome the difficulty caused by high wave numbers, we develop an efficient preconditioned iterative method based on the Krylov subspace, which greatly improves the eigenvalue distributions and reduces the number of iterations. Numerical experiments show the validity and efficiency of the proposed sixth-order fast preconditioned algorithm for solving the scattering by multiple cavities with high wave numbers.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): YuFeng Shi, Yan Guo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we propose an alternative formulation of conservative fifth order finite difference compact Weighted Essentially Non-Oscillatory (WENO) schemes to solve compressible Euler equations. Comparing with the classical conservative finite difference Compact-WENO scheme, its reconstruction procedure is applied to the point values rather than the traditional flux functions, then the HLLC (Harten, Lax and van Leer) and the local Lax-Friedrichs flux functions can be used to compute the interface fluxes in this framework. To maintain positivity of density and pressure, the parametrized positivity satisfying flux limiter is coupled with the proposed scheme for problems with extreme conditions. A number of testing cases including Titarev-Toro problem, the planar Sedov blast-wave problem, Riemann problems, double Mach reflection problem, shock diffraction problem and Kelvin-Helmholtz instability problem are presented to demonstrate the high resolution of the proposed compact scheme.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): Alexandre Noll Marques, Jean-Christophe Nave, Rodolfo Ruben Rosales〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We introduce a technique that simplifies the problem of imposing jump conditions on interfaces that are not aligned with a computational grid in the context of the 〈em〉Correction Function Method〈/em〉 (CFM). The CFM offers a general framework to solve Poisson's equation in the presence of discontinuities to high order of accuracy, while using a compact discretization stencil. A key concept behind the CFM is enforcing the jump conditions in a least squares sense. This concept requires computing integrals over sections of the interface, which is a challenge in 3-D when only an implicit representation of the interface is available (e.g., the zero contour of a level set function). The technique introduced here is based on a new formulation of the least squares procedure that relies only on integrals over domains that are amenable to simple quadrature after local coordinate transformations. We incorporate this technique into a fourth order accurate implementation of the CFM, and show examples of solutions to Poisson's equation with imposed jump conditions computed in 2-D and 3-D.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): Qian Kong, Yan-Fei Jing, Ting-Zhu Huang, Heng-Bin An〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The main aim of this paper is to develop two algorithms based on the Scheduled Relaxation Jacobi (SRJ) method (Yang and Mittal (2014) [7]) for solving problems arising from the finite-difference discretization of elliptic partial differential equations on large grids. These two algorithms are the Alternating Anderson-Scheduled Relaxation Jacobi (AASRJ) method by utilizing Anderson mixing after each SRJ iteration cycle and the Minimal Residual Scheduled Relaxation Jacobi (MRSRJ) method by minimizing residual after each SRJ iteration cycle, respectively. Through numerical experiments, we show that AASRJ is competitive with the optimal version of the SRJ method (Adsuara et al. (2017) [9]) in most problems we considered here, and MRSRJ outperforms SRJ in all cases. The properties of AASRJ and MRSRJ are demonstrated. Both of them are promising strategies for solving large, sparse linear systems while maintaining the simplicity of the Jacobi method.〈/p〉〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 96〈/p〉 〈p〉Author(s): Mahsa Taheri, Zahra Moslehi, Abdolreza Mirzaei, Mehran Safayani〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Measuring distance among data point pairs is a necessary step among numerous counts of algorithms in machine learning, pattern recognition and data mining. In the local perspective, the emphasis of all existing supervised metric learning algorithms is to shrink similar data points and to separate dissimilar ones in the local neighborhoods. This provides learning more appropriate distance metric in dealing with the within-class multi modal data. In this article, a new supervised local metric learning method named 〈em〉Self-Adaptive Local Metric Learning Method〈/em〉 (〈em〉SA-LM〈sup〉2〈/sup〉〈/em〉) has been proposed. The contribution of this method is in five aspects. First, in this method, learning an appropriate metric and defining the radius of local neighborhood are integrated in a joint formulation. Second, unlike the traditional approaches, SA-LM〈sup〉2〈/sup〉 learns the parameter of local neighborhood automatically thorough its formulation. As a result, it is a parameter free method, where it does not require any parameters that would need to be tuned. Third, SA-LM〈sup〉2〈/sup〉 is formulated as a SemiDefinite Program (SDP) with a global convergence guarantee. Fourth, this method does not need the similar set 〈em〉S〈/em〉, the focus here is on local areas’ data points and their separation from dissimilar ones. Finally, results of SA-LM〈sup〉2〈/sup〉 are less influenced by noisy input data points than the other compared global and local algorithms. Results obtained from different experiments indicate the outperformance of this algorithm over its counterparts.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Karim Shawki, George Papadakis〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We propose a preconditioner that can accelerate the rate of convergence of the Multiple Shooting Shadowing (MSS) method [1]. This recently proposed method can be used to compute derivatives of time-averaged objectives (also known as sensitivities) to system parameter(s) for chaotic systems. We propose a block diagonal preconditioner, which is based on a partial singular value decomposition of the MSS constraint matrix. The preconditioner can be computed using matrix-vector products only (i.e. it is matrix-free) and is fully parallelised in the time domain. Two chaotic systems are considered, the Lorenz system and the 1D Kuramoto Sivashinsky equation. Combination of the preconditioner with a regularisation method leads to tight bracketing of the eigenvalues to a narrow range. This combination results in a significant reduction in the number of iterations, and renders the convergence rate almost independent of the number of degrees of freedom of the system, and the length of the trajectory that is used to compute the time-averaged objective. This can potentially allow the method to be used for large chaotic systems (such as turbulent flows) and optimal control applications. The singular value decomposition of the constraint matrix can also be used to quantify the effect of the system condition on the accuracy of the sensitivities. In fact, neglecting the singular modes affected by noise, we recover the correct values of sensitivity that match closely with those obtained with finite differences for the Kuramoto Sivashinsky equation in the light turbulent regime. We notice a similar improvement for the Lorenz system as well.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): Jian Yu, Chao Yan, Zhenhua Jiang, Wu Yuan, Shusheng Chen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉An adaptive non-intrusive reduced basis (RB) method based on Gaussian process regression (GPR) is proposed for parametrized compressible flows. Adaptivity is pursued in the offline stage. The reduced basis by proper orthogonal decomposition (POD) is constructed iteratively to achieve a specified tolerance. For GPR, active data selection is used at each iteration, with standard deviation as the error indicator. To improve accuracy for shock-dominated flows, a properly designed simplified problem (SP) is considered as input of the regression models in addition to using parameters directly. Furthermore, a surrogate error model is constructed to serve as an efficient error estimator for the GPR models. Several two- and three-dimensional cases are conducted, including the inviscid nozzle flow, the inviscid NACA0012 airfoil flow and the inviscid M6 wing flow. For all the cases, the trained models are able to make efficient predictions with reasonable accuracy in the online stage. The SP-based approach is observed to result in biased sampling towards transonic regions. The regression models are further applied in sensitivity analysis, from which the solution of the two-dimensional cases are shown to be significantly more sensitive to input parameters than the wing flow. This is consistent to the comparison of convergence histories between the parameter-based and the SP-based models. For cases of high sensitivity, the SP-based approach is superior and can help to significantly reduce the number of required snapshots to achieve a prescribed tolerance.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): T. Allen, M. Zerroukat〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉With the increasing resolution of numerical weather prediction models the slopes of orographic features such as hills and mountains is becoming much better resolved with the consequence that the gradients encountered are becoming ever steeper. The commonly used terrain following coordinate, which actually becomes singular at 90°, is becoming more problematic due to the resulting stiffness making the elliptic boundary value problem difficult to solve. Even when a solution is obtainable the feedback of the pressure correction onto the momentum will generally cause instabilities and/or unphysical solutions. This paper uses an alternative representation of the orography, namely the immersed boundary method, in a semi-implicit-semi-Lagrangian model of the inviscid compressible Euler equations. The results show that the method not only performs well but, due to the chosen method of implementation, that the computational overhead of the geometry can be decoupled from implicit treatment of the vertical gravity wave component.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 31 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): François Forgues, Lucian Ivan, Alexandre Trottier, James G. M〈sup〉c〈/sup〉Donald〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The accurate prediction of multiphase flow when particles are differentiated by a set of “internal” variables, such as size or temperature, can pose modelling and numerical challenges. Although Lagrangian particle methods can provide predictive simulations of a wide spectrum of complex multiphase problems, they can become prohibitively expensive as the number of particles becomes large. Alternatively, Eulerian approaches have the potential to improve the computational efficiency of multiphase flows, but classical methods produce modelling artifacts or do not properly treat the local statistical dependence between the particle velocities and internal variables, or between the internal variables themselves. In this paper an extension is proposed of the classical Gaussian ten-moment model from gaskinetic theory to a model for the treatment of a dilute particle phase with an arbitrary number of internal variables based on an entropy-maximization argument. Unlike previous formulations, this new model provides a set of first-order robustly-hyperbolic balance laws that include a direct treatment for the local statistical variance of each variable, as well as the covariance between the internal variables or the internal variables and particle velocity. A study of the wave speeds of the general hyperbolic system is presented. To demonstrate an example application, the model is then specialized for polydisperse flows that are subject to viscous fluid drag as well as gravitational and buoyancy forces. The complete eigenstructure of this fifteen-moment polydisperse Gaussian model (PGM) is presented and the PGM is shown to maintain a physically realizable distribution function for all admissible initial conditions. Finally, several illustrative low-dimensional problems are studied to demonstrate the predictive capabilities of the new model.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 97〈/p〉 〈p〉Author(s): Zheng Ma, Jun Cheng, Dapeng Tao〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Wearable/portable brain-computer interfaces (BCIs) for the long-term end use are a focus of recent BCI research. A challenge is how to update the BCI to meet changes in electroencephalography (EEG) signals, since the resource are so limited that retraining of traditional well-performed models, such as a support vector machine, is nearly impossible. To cope with this challenge, less-demanding adaptive online learning can be considered. We investigated an adaptive projected sub-gradient method (APSM) that is originated from the set theoretic estimation formulation and the projections onto convex sets theory. APSM provides a unifying framework for both adaptive classification and regression tasks. Coefficients of APSM are adjusted online as data arrive sequentially, with a regularization constraint made by projections onto a fixed closed ball. We extended the general APSM to a shrinkage form, where shrinkage closed balls were used instead of the original fixed one, expecting a more controllable fading effect and better adaptability. The convergence of shrinkage APSM was proved. It was also demonstrated that as shrinkage factor approached to 1, the limit point of shrinkage APSM would approach to the optimal solution with the least norm, which could be especially beneficial for generalization of the classifier. The performance of the proposed method was evaluated, and compared with those of the general APSM, the incremental support vector machine, and the passive aggressive algorithm, through an event-related potential-based BCI experiment. Results showed the advantage of the proposed method over the others on both the online classification performance and the easiness of tuning. Our study revealed the effectiveness of the proposed method for adaptive EEG classification, making it a promising tool for on-device training and updating of wearable/portable BCIs, as well as for application in other related fields, such as EEG-based biometrics.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 16 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): S. Danilov, A. Kutsenko〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Computational wave branches are common to linearized shallow water equations discretized on triangular meshes. It is demonstrated that for standard finite-volume discretizations these branches can be traced back to the structure of the unit cell of triangular lattice, which includes two triangles with a common edge. Only subsets of similarly oriented triangles or edges possess the translational symmetry of unit cell. As a consequence, discrete degrees of freedom placed on triangles or edges are geometrically different, creating an internal structure inside unit cells. It implies a possibility of oscillations inside unit cells seen as computational branches in the framework of linearized shallow water equations, or as grid-scale noise generally.〈/p〉 〈p〉Adding dissipative operators based on smallest stencils to discretized equations is needed to control these oscillations in solutions. A review of several finite-volume discretization is presented with focus on computational branches and dissipative operators.〈/p〉 〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Claudio Canuto, Sandra Pieraccini, Dongbin Xiu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A non-intrusive bifidelity strategy [1] is applied to the computation of statistics of a quantity of interest (QoI) which depends in a non-smooth way upon the stochastic parameters. The procedure leverages the accuracy of a high-fidelity model and the efficiency of a low-fidelity model, obtained through the use of different levels of numerical resolution, to pursue a high quality approximation of the statistics with a moderate number of high-fidelity simulations. The method is applied first to synthetic test cases with outputs exhibiting either a continuous or a discontinuous behaviour, then to the realistic simulation of a flow in an underground network of fractures [2], whose stochastic geometry outputs a non-smooth QoI. In both applications, the results highlight the efficacy of the approach in terms of error decay versus the number of computed high-fidelity solutions, even when the QoI lacks smoothness. For the underground simulation problem, the observed gain in computational cost is at least of one order of magnitude.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 14 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences〈/p〉 〈p〉Author(s): Suihong Song, Jiagen Hou, Luxing Dou, Zezhang Song, Shuang Sun〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Identification of wireline log shapes is of great significance for the research of paleogeography and paleosedimentology. Generally, this work is manually finished by geologists through careful observation of the morphological features of log curves. This process is subjective, tedious and may introduce artefacts depending on the bias of the interpreters. Therefore, we developed a model which exceeds the average performance of experienced geologists in identifying basic log shapes. The key to the excellent performance of the model is a large annotated dataset and a specially designed recurrent neural network. The dataset contains 2676 SP log segments of four basic log shapes (bell, funnel, egg and cylinder shape). The model has 8 layers, and the core is a layer of LSTM blocks which is designed to grasp the morphological features of the log curve. A committee of four experienced geologists annotated a gold standard test dataset, on which we compared the performances of the model and three other independent geologists. It turned out that the model exceeded the average performance of the three geologists in most circumstances, so the model is believed a powerful “AI” to free geologists from such heavy physical labour of log shape identification and maintain a higher accuracy. The research for this problem is still at the initial stage and expecting more work and ideas to further improve the robustness of the model. So, we also put forward some ideas for improving the current network and enlarging our dataset.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 97〈/p〉 〈p〉Author(s): Franco Manessi, Alessandro Rozza, Mario Manzo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In many different classification tasks it is required to manage structured data, which are usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the vertices/edges of each graph may change over time. The goal is to exploit existing neural network architectures to model datasets that are best represented with graph structures that change over time. To the best of the authors’ knowledge, this task has not been addressed using these kinds of architectures. Two novel approaches are proposed, which combine Long Short-Term Memory networks and Graph Convolutional Networks to learn long short-term dependencies together with graph structure. The advantage provided by the proposed methods is confirmed by the results achieved on four real world datasets: an increase of up to 12 percentage points in Accuracy and F1 scores for vertex-based semi-supervised classification and up to 2 percentage points in Accuracy and F1 scores for graph-based supervised classification.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Samuel H. Rudy, Steven L. Brunton, J. Nathan Kutz〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The analysis of high-dimensional dynamical systems generally requires the integration of simulation data with experimental measurements. Experimental data often has substantial amounts of measurement noise that compromises the ability to produce accurate dimensionality reduction, parameter estimation, reduced order models, and/or balanced models for control. Data assimilation attempts to overcome the deleterious effects of noise by producing a set of algorithms for state estimation from noisy and possibly incomplete measurements. Indeed, methods such as Kalman filtering and smoothing are vital tools for scientists in fields ranging from electronics to weather forecasting. In this work we develop a novel framework for smoothing data based on known or partially known nonlinear governing equations. The method yields superior results to current techniques when applied to problems with known deterministic dynamics. By exploiting the numerical time-stepping constraints of the deterministic system, an optimization formulation can readily extract the noise from the nonlinear dynamics in a principled manner. The superior performance is due in part to the fact that it optimizes global state estimates. We demonstrate the efficiency and efficacy of the method on a number of canonical examples, thus demonstrating its viability for the wide range of potential applications stated above.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers in Industry, Volume 111〈/p〉 〈p〉Author(s): Victor Verboeket, Harold Krikke〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Additive Manufacturing (AM) is one of the advanced computer technologies that digitizes supply chains (SCs). This article presents a structured literature review on the impact of AM on SC design based on 67 peer-reviewed articles. We address the business settings in which AM operates, AM SC design choices, and AM SC performance outputs. We use the SCOR model to structure this article.〈/p〉 〈p〉Our results show that while AM is currently used for many applications it is mainly used for small, low demand and geometrically complex products. SC design involves making choices related to SC configurations, supplier relationships, AM production, installation of a return channel, IT system requirements and human resources. The main SC performance outputs influenced by AM are (reduced) SC cost and (improved) SC responsiveness. Many bottlenecks prevent AM being used to its full potential.〈/p〉 〈p〉This article contributes to the AM SC design knowledge base by developing a conceptual framework, including 18 propositions, and a research agenda identifying relevant AM SC design research themes. Moreover, a roadmap is presented describing the steps to a full roll-out of AM for manufacturing purposes, in which bottlenecks (serving as thresholds) need to be eliminated in order to trigger a series of disrupting SC effects. We expect that AM will eventually lead to SC disruption, and has the potential to largely replace conventional manufacturing for mainstream products.〈/p〉 〈/div〉

Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): Keyvan Zare-Rami, Yong-Rak Kim〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Studying the behavior of particle/fiber embedded composites has been a common and challenging problem in mechanics of materials area. Analysis of these materials can be effectively conducted by computational simulations such as finite element (FE) analyses. Creating a model that represents the actual microstructure of the composite is crucial to obtain a trustable result, but is often labor-intensive. Microstructure Inelastic Damage Analysis Software (MIDAS) Virtual Tester Preprocessor (MIDAS-VT-Pre) was developed to facilitate construction of two-dimensional microstructure FE models of particle/fiber embedded composites. MIDAS-VT-Pre is able to insert automatically cohesive zone interface elements in the mesh structure in order to simulate crack initiation and propagation. This program is tailored to generate the FE model of standard mechanical test configurations that are frequently used in laboratory settings. The output of this program includes mesh structure and boundary conditions. This information can be used to run FE simulation (i.e. virtual testing) using common FE software such as ABAQUS.〈/p〉〈/div〉

Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): Cristian Pop, Alexandru Popa〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We present a new application, developed mostly from scratch, serving as a fast and efficient web crawler, with added network visualization and content analysis tools. It can be used to perform experimental research in a number of fields, including web graph analysis, basic text comparison or even testing out sociological theories, depending on the user’s inclination. A use case is provided, where the application is applied to Romanian news websites, from which several interesting observations can be drawn. The application itself and its code are released under a GPL license, and can be used by other researchers as-is (for use cases similar to our own), or expanded upon by interested developers.〈/p〉〈/div〉

Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): Blair R. Drummond, Christian J.G. Tessier, Mathieu F. Dextraze, Corrie J.B. daCosta〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Analysis of single-channel activity provides unparalleled insight into ion channel structure, function, and mechanism. An essential step in the analysis pipeline is the assignment and sorting of bursts of activity that can be attributed to a single ion channel. Here we present 〈em〉scbursts〈/em〉, an R package for the analysis and sorting of single-channel bursts. Using 〈em〉scbursts〈/em〉, idealized data from a variety of single-channel analysis software packages can be read directly into R, and parsed into bursts according to a user-defined critical closed duration. Bursts can be analyzed and sorted based on a variety of parameters, including burst open probability, and the number of openings per burst. Sorted bursts can then be exported in a standard format for downstream kinetic fitting. We provide examples illustrating the implementation and utility of 〈em〉scbursts〈/em〉, as well as how 〈em〉scbursts〈/em〉 brings the statistical prowess and versatility of R to single-channel analysis.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): M. Ganesh, S.C. Hawkins, D. Volkov〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We describe an efficient algorithm for reconstruction of the electromagnetic parameters of an unbounded dielectric medium from noisy cross section data induced by a point source in 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈msup〉〈mrow〉〈mi mathvariant="double-struck"〉R〈/mi〉〈/mrow〉〈mrow〉〈mn〉3〈/mn〉〈/mrow〉〈/msup〉〈/math〉. The efficiency of our Bayesian inverse algorithm for the parameters is based on developing an offline high order forward stochastic model and also an associated deterministic dielectric media Maxwell solver. Underlying the inverse/offline approach is our high order fully discrete Galerkin algorithm for solving an equivalent surface integral equation reformulation that is stable for all frequencies. The efficient algorithm includes approximating the likelihood distribution in the Bayesian model by a decomposed fast generalized polynomial chaos (gPC) model as a surrogate for the forward model. Offline construction of the gPC model facilitates fast online evaluation of the posterior distribution of the dielectric medium parameters. Parallel computational experiments demonstrate the efficiency of our deterministic, forward stochastic, and inverse dielectric computer models.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 8 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Jérôme Droniou, Matej Medla, Karol Mikula〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉We develop and analyse finite volume methods for the Poisson problem with boundary conditions involving oblique derivatives. We design a generic framework, for finite volume discretisations of such models, in which internal fluxes are not assumed to have a specific form, but only to satisfy some (usual) coercivity and consistency properties. The oblique boundary conditions are split into a normal component, which directly appears in the flux balance on control volumes touching the domain boundary, and a tangential component which is managed as an advection term on the boundary. This advection term is discretised using a finite volume method based on a centred discretisation (to ensure optimal rates of convergence) and stabilised using a vanishing boundary viscosity. A convergence analysis, based on the 3rd Strang Lemma [9], is conducted in this generic finite volume framework, and yields the expected 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈mi mathvariant="script"〉O〈/mi〉〈mo stretchy="false"〉(〈/mo〉〈mi〉h〈/mi〉〈mo stretchy="false"〉)〈/mo〉〈/math〉 optimal convergence rate in discrete energy norm.〈/p〉 〈p〉We then describe a specific choice of numerical fluxes, based on a generalised hexahedral meshing of the computational domain. These fluxes are a corrected version of fluxes originally introduced in [29]. We identify mesh regularity parameters that ensure that these fluxes satisfy the required coercivity and consistency properties. The theoretical rates of convergence are illustrated by an extensive set of 3D numerical tests, including some conducted with two variants of the proposed scheme. A test involving real-world data measuring the disturbing potential in Earth gravity modelling over Slovakia is also presented.〈/p〉 〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 133〈/p〉 〈p〉Author(s): Luísa Vieira Lucchese, Leonardo Romero Monteiro, Edith Beatriz Camano Schettini, Jorge Hugo Silvestrini〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉It is well-known that deposits of turbidity currents can significantly change bathymetry. The deposit of a current can alter sedimentation that happens afterwards, changing the deposit shape of a turbidity current. Direct Numerical Simulations of tridisperse turbidity currents are performed considering a rectangular channel and finite-release initial condition. The results are successfully compared to numerical and experimental results. We developed the Evolutive Deposit Method (EDM), that calculates the deposited volume and updates the topography based on the accumulated deposit for a given period of time. Entrainment is not considered. EDM has an original mathematical formulation. Topography update occurs at every 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si90.svg"〉〈mi〉τ〈/mi〉〈/math〉 and is based on two surfaces: 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si87.svg"〉〈mi〉Ψ〈/mi〉〈/math〉 and 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si86.svg"〉〈mi〉Γ〈/mi〉〈/math〉. 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si87.svg"〉〈mi〉Ψ〈/mi〉〈/math〉 is a reference surface that can only assume integer mesh nodes, and defines the location of the solid represented by Immersed Boundary Method. 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si86.svg"〉〈mi〉Γ〈/mi〉〈/math〉 is a signed surface in which the deposit is integrated, and that is also fed by the rounding errors of 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si87.svg"〉〈mi〉Ψ〈/mi〉〈/math〉. It is observed that the error caused by not considering the changes on topography due to deposit increases with time. For the case with initial flat terrain, the turbidity current front is the same whether considering the update or not. We also performed two simulations of turbidity currents propagating over the deposits produced by a previous current. In one case, the bathymetry was updated during both the first and the second events, and, in another, only changes on bathymetry between the simulations were considered. Results show that the order of magnitude of the relative deposit error of not considering bathymetry update remains the same order for both the first and the second consecutive events.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): Magnus Svärd, Jan Nordström〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉We consider constant-coefficient initial-boundary value problems, with a first or second derivative in time and a highest spatial derivative of order 〈em〉q〈/em〉, and their semi-discrete finite difference approximations. With an internal truncation error of order 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈mi〉p〈/mi〉〈mo〉≥〈/mo〉〈mn〉1〈/mn〉〈/math〉, and a boundary error of order 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"〉〈mi〉r〈/mi〉〈mo〉≥〈/mo〉〈mn〉0〈/mn〉〈/math〉, we prove that the convergence rate is: 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"〉〈mi mathvariant="normal"〉min〈/mi〉〈mo〉⁡〈/mo〉〈mo stretchy="false"〉(〈/mo〉〈mi〉p〈/mi〉〈mo〉,〈/mo〉〈mi〉r〈/mi〉〈mo linebreak="badbreak" linebreakstyle="after"〉+〈/mo〉〈mi〉q〈/mi〉〈mo stretchy="false"〉)〈/mo〉〈/math〉.〈/p〉 〈p〉The assumptions needed for these results to hold are: i) The continuous problem is linear and well-posed (with a smooth solution). ii) The numerical scheme is consistent, nullspace consistent, nullspace invariant, and energy stable. These assumptions are often satisfied for Summation-By-Parts schemes.〈/p〉 〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Vincent Mons, Qi Wang, Tamer A. Zaki〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Various ensemble-based variational (EnVar) data assimilation (DA) techniques are developed to reconstruct the spatial distribution of a scalar source in a turbulent channel flow resolved by direct numerical simulation (DNS). In order to decrease the computational cost of the DA procedure and improve its performance, Kriging-based interpolation is combined with EnVar DA, which enables the consideration of relatively large ensembles with moderate computational resources. The performance of the proposed Kriging-EnVar (KEnVar) DA scheme is assessed and favorably compared to that of standard EnVar and adjoint-based variational DA in various scenarios. Sparse regularization is implemented in the framework of EnVar DA in order to better tackle the case of concentrated scalar emissions. The problem of optimal sensor placement is also addressed, and it is shown that significant improvement in the quality of the reconstructed source can be obtained without supplementary computational cost once the ensemble required by the DA procedure is formed.〈/p〉〈/div〉

Description: 〈p〉Publication date: 15 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 397〈/p〉 〈p〉Author(s): Jie Ding, Zhongming Wang, Shenggao Zhou〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉To study ion transport in electrolyte solutions, we propose numerical methods for a modified Poisson–Nernst–Planck model with ionic steric effects (SPNP). Positivity preserving schemes based on harmonic-mean approximations are proposed on a nonuniform mesh for the spatial discretization of the SPNP equations. Both explicit and semi-implicit discretization are considered in time. Numerical analysis shows that explicit forward Euler and semi-implicit trapezoidal discretization lead to schemes that maintain fully discrete solution positivity under a constraint on a mesh ratio, while the semi-implicit backward Euler discretization maintain fully discrete solution positivity 〈em〉unconditionally〈/em〉. We further study the condition numbers of the matrix associated with the semi-implicit backward Euler discretization, and establish an upper bound on condition numbers, indicating that the developed discretization based on harmonic-mean approximations effectively solves the issue of the presence of large condition numbers when using the Slotboom-type variables. Further numerical simulations confirm the analysis results on accuracy, positivity, and bounded condition numbers. The proposed schemes are also applied to study practical applications, such as the impact of self and cross steric interactions on ion distribution and rectifying behavior in a slit-shaped nanopore with surface charges. Possible extensions of the numerical methods to other modified PNP models with ion correlations and steric effects are also discussed.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Chinmay S. Kulkarni, Pierre F.J. Lermusiaux〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We propose a novel numerical methodology to compute the advective transport and diffusion-reaction of tracer quantities. The tracer advection occurs through flow map composition and is super-accurate, yielding numerical solutions almost devoid of compounding numerical errors, while allowing for direct parallelization in the temporal direction. It is computed by implicitly solving the characteristic evolution through a modified transport partial differential equation and domain decomposition in the temporal direction, followed by composition with the known initial condition. This advection scheme allows a rigorous computation of the spatial and temporal error bounds, yields an accuracy comparable to that of Lagrangian methods, and maintains the advantages of Eulerian schemes. We further show that there exists an optimal value of the composition timestep that yields the minimum total numerical error in the computations, and derive the expression for this value. We develop schemes for the addition of tracer diffusion, reaction, and source terms, and for the implementation of boundary conditions. Finally, the methodology is applied in three flow examples, namely an analytical reversible swirl flow, an idealized flow exiting a strait undergoing sudden expansion, and a realistic ocean flow in the Bismarck sea. New benchmark problems for advection-diffusion-reaction schemes are developed and used to compare and contrast results with those of classic schemes. The results highlight the theoretical properties of the methodology as well as its efficiency, super-accuracy with minimal numerical errors, and applicability in realistic simulations.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 398〈/p〉 〈p〉Author(s): Immo Huismann, Jörg Stiller, Jochen Fröhlich〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉High-order methods gain increased attention in computational fluid dynamics. However, due to the time step restrictions arising from the semi-implicit time stepping for the incompressible case, the potential advantage of these methods depends critically on efficient elliptic solvers. Due to the operation counts of operators scaling with the polynomial degree 〈em〉p〈/em〉 times the number of degrees of freedom 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈msub〉〈mrow〉〈mi〉n〈/mi〉〈/mrow〉〈mrow〉〈mi mathvariant="normal"〉DOF〈/mi〉〈/mrow〉〈/msub〉〈/math〉, the runtime of the best available multigrid solvers scales with 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"〉〈mi mathvariant="script"〉O〈/mi〉〈mrow〉〈mo stretchy="true"〉(〈/mo〉〈mi〉p〈/mi〉〈mo〉⋅〈/mo〉〈msub〉〈mrow〉〈mi〉n〈/mi〉〈/mrow〉〈mrow〉〈mi mathvariant="normal"〉DOF〈/mi〉〈/mrow〉〈/msub〉〈mo stretchy="true"〉)〈/mo〉〈/mrow〉〈/math〉. This scaling with 〈em〉p〈/em〉 significantly lowers the applicability of high-order methods to high orders. While the operators for residual evaluation can be linearized when using static condensation, 〈span〉Schwarz〈/span〉-type smoothers require their inverses on fixed subdomains. No explicit inverse is known in the condensed case and matrix-matrix multiplications scale with 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"〉〈mi〉p〈/mi〉〈mo〉⋅〈/mo〉〈msub〉〈mrow〉〈mi〉n〈/mi〉〈/mrow〉〈mrow〉〈mi mathvariant="normal"〉DOF〈/mi〉〈/mrow〉〈/msub〉〈/math〉. This paper derives a matrix-free explicit inverse for the static condensed operator in a cuboidal, Cartesian subdomain. It scales with 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.svg"〉〈msup〉〈mrow〉〈mi〉p〈/mi〉〈/mrow〉〈mrow〉〈mn〉3〈/mn〉〈/mrow〉〈/msup〉〈/math〉 per element, i.e. 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈msub〉〈mrow〉〈mi〉n〈/mi〉〈/mrow〉〈mrow〉〈mi mathvariant="normal"〉DOF〈/mi〉〈/mrow〉〈/msub〉〈/math〉 globally, and allows for a linearly scaling additive 〈span〉Schwarz〈/span〉 smoother, yielding a 〈em〉p〈/em〉-multigrid cycle with an operation count of 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si6.svg"〉〈mi mathvariant="script"〉O〈/mi〉〈mrow〉〈mo stretchy="true"〉(〈/mo〉〈msub〉〈mrow〉〈mi〉n〈/mi〉〈/mrow〉〈mrow〉〈mi mathvariant="normal"〉DOF〈/mi〉〈/mrow〉〈/msub〉〈mo stretchy="true"〉)〈/mo〉〈/mrow〉〈/math〉. The resulting solver uses fewer than four iterations for all polynomial degrees to reduce the residual by ten orders and has a runtime scaling linearly with 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈msub〉〈mrow〉〈mi〉n〈/mi〉〈/mrow〉〈mrow〉〈mi mathvariant="normal"〉DOF〈/mi〉〈/mrow〉〈/msub〉〈/math〉 for polynomial degrees at least up to 48. Furthermore the runtime is less than one microsecond per unknown over wide parameter ranges when using one core of a CPU, leading to time-stepping for the incompressible 〈span〉Navier-Stokes〈/span〉 equations using as much time for explicitly treated convection terms as for the elliptic solvers.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 97〈/p〉 〈p〉Author(s): Ying Liu, Konstantinos Tountas, Dimitris A. Pados, Stella N. Batalama, Michael J. Medley〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉High-dimensional data usually exhibit intrinsic low-rank structures. With tremendous amount of streaming data generated by ubiquitous sensors in the world of Internet-of-Things, fast detection of such low-rank pattern is of utmost importance to a wide range of applications. In this work, we present an 〈em〉L〈/em〉〈sub〉1〈/sub〉-subspace tracking method to capture the low-rank structure of streaming data. The method is based on the 〈em〉L〈/em〉〈sub〉1〈/sub〉-norm principal-component analysis (〈em〉L〈/em〉〈sub〉1〈/sub〉-PCA) theory that offers outlier resistance in subspace calculation. The proposed method updates the 〈em〉L〈/em〉〈sub〉1〈/sub〉-subspace as new data are acquired by sensors. In each time slot, the conformity of each datum is measured by the 〈em〉L〈/em〉〈sub〉1〈/sub〉-subspace calculated in the previous time slot and used to weigh the datum. Iterative weighted 〈em〉L〈/em〉〈sub〉1〈/sub〉-PCA is then executed through a refining function. The superiority of the proposed 〈em〉L〈/em〉〈sub〉1〈/sub〉-subspace tracking method compared to existing approaches is demonstrated through experimental studies in various application fields.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Artificial Intelligence, Volume 276〈/p〉 〈p〉Author(s): Arnold Filtser, Nimrod Talmon〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We consider distributed elections, where there is a center and 〈em〉k〈/em〉 sites. In such distributed elections, each voter has preferences over some set of candidates, and each voter is assigned to exactly one site such that each site is aware only of the voters assigned to it. The center is able to directly communicate with all sites. We are interested in designing communication-efficient protocols, allowing the center to maintain a candidate which, with arbitrarily high probability, is guaranteed to be a winner, or at least close to being a winner. We consider various single-winner voting rules, such as variants of Approval voting and scoring rules, tournament-based voting rules, and several round-based voting rules. For the voting rules we consider, we show that, using communication which is logarithmic in the number of voters, it is possible for the center to maintain such approximate winners; that is, upon a query at any time the center can immediately return a candidate which is guaranteed to be an approximate winner with high probability. We complement our protocols with lower bounds. Our results are theoretical in nature and relate to various scenarios, such as aggregating customer preferences in online shopping websites or supermarket chains and collecting votes from different polling stations of political elections.〈/p〉〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 133〈/p〉 〈p〉Author(s): Sithara Kanakaraj, Madhu S. Nair, Saidalavi Kalady〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Enhancing Synthetic Aperture Radar (SAR) images for sharp and detailed images through super-resolution (SR) is of great significance in many remote sensing applications. Due to the inherent resolution limitations and the cost incurred for further development of sensor devices, image enhancements through image processing techniques have become more popular. However, in these techniques, the soft edges present in the images are not reconstructed completely, thereby marring the clarity of the generated image. This paper presents a method to restore more details in the image by adaptively adjusting the measurement noise covariance and process noise covariance into the intensity estimation framework for SAR image super-resolution. For this, an Adaptive Importance Sampling Unscented Kalman Filter (ISUKF) framework is implemented using the covariance matching technique. Experimental results indicate that the super-resolution using ISUKF framework performs better, regarding denoising and feature preservation, when accounted for the measurement and process noise covariance. It also outperforms the other recent and standard SR techniques covered in the literature.〈/p〉〈/div〉

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Pattern Recognition, Volume 96〈/p〉 〈p〉Author(s): Yuming Fang, Xiaoqiang Zhang, Feiniu Yuan, Nevrez Imamoglu, Haiwen Liu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Image saliency detection has been widely explored in recent decades, but computational modeling of visual attention for video sequences is limited due to complicated temporal saliency extraction and fusion of spatial and temporal saliency. Inspired by Gestalt theory, we introduce a novel spatiotemporal saliency detection model in this study. First, we compute spatial and temporal saliency maps by low-level visual features. And then we merge these two saliency maps for spatiotemporal saliency prediction of video sequences. The spatial saliency map is calculated by extracting three kinds of features including color, luminance, and texture, while the temporal saliency map is computed by extracting motion features estimated from video sequences. A novel adaptive entropy-based uncertainty weighting method is designed to fuse spatial and temporal saliency maps to predict the final spatiotemporal saliency map by Gestalt theory. The Gestalt principle of similarity is used to estimate spatial uncertainty from spatial saliency, while temporal uncertainty is computed from temporal saliency by the Gestalt principle of common fate. Experimental results on three large-scale databases show that our method can predict visual saliency more accurately than the state-of-art spatiotemporal saliency detection algorithms.〈/p〉〈/div〉