title: Ringer: Systematic Mining of Malicious Domains by Dynamic Graph Convolutional Network

text: Malicious domains are critical resources in network security, behind which attackers hide malware to launch the malicious attacks. Therefore, blocking malicious domains is the most effective and practical way to combat and reduce hostile activities. There are three limitations in previous methods over domain classification: (1) solely based on local domain features which tend to be not robust enough; (2) lack of a large number of ground truth for model-training to get high accuracy; (3) statically learning on graph which is not scalable. In this paper, we present Ringer, a scalable method to detect malicious domains by dynamic Graph Convolutional Network (GCN). Ringer first uses querying behaviors or domain-IP resolutions to construct domain graphs, on which the dynamic GCN is leveraged to learn the node representations that integrate both information about node features and graph structure. And then, these high-quality representations are further fed to the full-connected neural network for domain classification. Notably, instead of global statically learning, we adopt time-based hash to cut graphs to small ones and inductively learn the embedding of nodes according to selectively sampling neighbors. We construct a series of experiments on a large ISP over two days and compare it with state of the arts. The results demonstrate that Ringer achieves excellent performance with a high accuracy of 96.8% on average. Additionally, we find thousands of potential malicious domains by semi-supervised learning.


title: Hypergraph Grammar-Based Model of Adaptive Bitmap Compression

text: JPEG algorithm defines a sequence of steps (essential and optional) executed in order to compress an image. The first step is an optional conversion of the image color space from RBG (red-blue-green) to YCbCr (luminance and two chroma components). This step allows to discard part of chrominance information, a useful gain due to the fact, that the chrominance resolution of the human eye is much lower than the luminance resolution. In the next step, the image is divided into 8[Formula: see text]8 blocks, called MCUs (Minimum Coded Units). In this paper we present a new adaptive bitmap compression algorithm, and we compare it to the state-of-the-art of JPEG algorithms. Our algorithm utilizes hypergraph grammar model, partitioning the bitmap into a set of adaptively selected rectangles. Each rectangle approximates a bitmap using MCUs with the size selected according to the entire rectangular element. The hypergraph grammar model allows to describe the whole compression algorithm by a set of five productions. They are executed during the compression stage, and they partition the actual rectangles into smaller ones, until the required compression rate is obtained. We show that our method allows to compress bitmaps with large uniform areas in a better way than traditional JPEG algorithms do.


title: A Novel Bio-inspired Hybrid Metaheuristic for Unsolicited Bulk Email Detection

text: With the recent influx of technology, Unsolicited Bulk Emails (UBEs) have become a potential problem, leaving computer users and organizations at the risk of brand, data, and financial loss. In this paper, we present a novel bio-inspired hybrid parallel optimization algorithm (Cuckoo-Firefly-GR), which combines Genetic Replacement (GR) of low fitness individuals with a hybrid of Cuckoo Search (CS) and Firefly (FA) optimizations. Cuckoo-Firefly-GR not only employs the random walk in CS, but also uses mechanisms in FA to generate and select fitter individuals. The content- and behavior-based features of emails used in the existing works, along with Doc2Vec features of the email body are employed to extract the syntactic and semantic information in the emails. By establishing an optimal balance between intensification and diversification, and reaching global optimization using two metaheuristics, we argue that the proposed algorithm significantly improves the performance of UBE detection, by selecting the most discriminative feature subspace. This study presents significant observations from the extensive evaluations on UBE corpora of 3, 844 emails, that underline the efficiency and superiority of our proposed Cuckoo-Firefly-GR over the base optimizations (Cuckoo-GR and Firefly-GR), dense autoencoders, recurrent neural autoencoders, and several state-of-the-art methods. Furthermore, the instructive feature subset obtained using the proposed Cuckoo-Firefly-GR, when classified using a dense neural model, achieved an accuracy of [Formula: see text].


title: A Proposed Machine Learning Model for Forecasting Impact of Traffic-Induced Vibrations on Buildings

text: Traffic-induced vibrations may cause various damages to buildings located near the road, including cracking of plaster, cracks in load-bearing elements or even collapse of the whole structure. Measurements of vibrations of real buildings are costly and laborious. Therefore the aim of the research is to propose the original numerical algorithm which allows us to predict, with high probability, the negative dynamic impact of traffic-induced vibrations on the examined building. The model has been based on machine learning. Firstly, the experimental tests have been conducted on different buildings using specialized equipment taking into account six factors: distance from the building to the edge of the road, type of surface, condition of road surface, condition of the building, the absorption of soil and the type of vehicle. Then, the numerical algorithm based on machine learning (using support vector machine) has been created. The results of the conducted analysis clearly show that the method can be considered as a good tool for predicting the impact of traffic-induced vibrations on buildings, being characterized by high reliability.


title: High-Resolution Source Estimation of Volcanic Sulfur Dioxide Emissions Using Large-Scale Transport Simulations

text: High-resolution reconstruction of emission rates from different sources is essential to achieve accurate simulations of atmospheric transport processes. How to achieve real-time forecasts of atmospheric transport is still a great challenge, in particular due to the large computational demands of this problem. Considering a case study of volcanic sulfur dioxide emissions, the codes of the Lagrangian particle dispersion model MPTRAC and an inversion algorithm for emission rate estimation based on sequential importance resampling are deployed on the Tianhe-2 supercomputer. The high-throughput based parallel computing strategy shows excellent scalability and computational efficiency. Therefore, the spatial-temporal resolution of the emission reconstruction can be improved by increasing the parallel scale. In our study, the largest parallel scale is up to 1.446 million compute processes, which allows us to obtain emission rates with a resolution of 30 min in time and 100 m in altitude. By applying massive-parallel computing systems such as Tianhe-2, real-time source estimation and forecasts of atmospheric transport are becoming feasible.


title: Analysis of Ensemble Feature Selection for Correlated High-Dimensional RNA-Seq Cancer Data

text: Discovery of diagnostic and prognostic molecular markers is important and actively pursued the research field in cancer research. For complex diseases, this process is often performed using Machine Learning. The current study compares two approaches for the discovery of relevant variables: by application of a single feature selection algorithm, versus by an ensemble of diverse algorithms. These approaches are used to identify variables that are relevant discerning of four cancer types using RNA-seq profiles from the Cancer Genome Atlas. The comparison is carried out in two directions: evaluating the predictive performance of models and monitoring the stability of selected variables. The most informative features are identified using a four feature selection algorithms, namely U-test, ReliefF, and two variants of the MDFS algorithm. Discerning normal and tumor tissues is performed using the Random Forest algorithm. The highest stability of the feature set was obtained when U-test was used. Unfortunately, models built on feature sets obtained from the ensemble of feature selection algorithms were no better than for models developed on feature sets obtained from individual algorithms. On the other hand, the feature selectors leading to the best classification results varied between data sets.


title: Biological Network Visualization for Targeted Proteomics Based on Mean First-Passage Time in Semi-Lazy Random Walks

text: Experimental data from protein microarrays or other targeted assays are often analyzed using network-based visualization and modeling approaches. Reference networks, such as a graph of known protein-protein interactions, can be used to place experimental data in the context of biological pathways, making the results more interpretable. The first step in network-based visualization and modeling involves mapping the measured experimental endpoints to network nodes, but in targeted assays many network nodes have no corresponding measured endpoints. This leads to a novel problem – given full network structure and a subset of vertices that correspond to measured protein endpoints, infer connectivity between those vertices. We solve the problem by defining a semi-lazy random walk in directed graphs, and quantifying the mean first-passage time for graph nodes. Using simulated and real networks and data, we show that the graph connectivity structure inferred by the proposed method has higher agreement with underlying biology than two alternative strategies.


title: Statistical Iterative Reconstruction Algorithm Based on a Continuous-to-Continuous Model Formulated for Spiral Cone-Beam CT

text: This paper is closely related to the originally formulated 3D statistical model-based iterative reconstruction algorithm for spiral cone-beam x-ray tomography. The concept proposed here is based on a continuous-to-continuous data model, and the reconstruction problem is formulated as a shift invariant system. This algorithm significantly improves the quality of the subsequently reconstructed images, so allowing a reduction in the x-ray dose absorbed by a patient. This form of reconstruction problem permits a reduction in the computational complexity in comparison with other model-based iterative approaches. Computer simulations have shown that the reconstruction method presented here outperforms standard FDK methods with regard to the image quality obtained and can be competitive in terms of time of calculation.


title: MeshingNet: A New Mesh Generation Method Based on Deep Learning

text: We introduce a novel approach to automatic unstructured mesh generation using machine learning to predict an optimal finite element mesh for a previously unseen problem. The framework that we have developed is based around training an artificial neural network (ANN) to guide standard mesh generation software, based upon a prediction of the required local mesh density throughout the domain. We describe the training regime that is proposed, based upon the use of a posteriori error estimation, and discuss the topologies of the ANNs that we have considered. We then illustrate performance using two standard test problems, a single elliptic partial differential equation (PDE) and a system of PDEs associated with linear elasticity. We demonstrate the effective generation of high quality meshes for arbitrary polygonal geometries and a range of material parameters, using a variety of user-selected error norms.


title: Granulation-Based Reverse Image Retrieval for Microscopic Rock Images

text: The paper presents a method of object detection on microscopic images of rocks, which makes it possible to identify images with similar structural features of the rock. These features are understood as the sizes and shapes of its components and the mutual relationships between them. The proposed detection methodology is an adaptive and unsupervised method that analyzes characteristic color clusters in the image. It achieves good detection results for rocks with clear clusters of colored objects. For the analyzed data set, the method finds in the rock image sets with high visual similarity, which translates into the geological classification of rocks at a level of above 78%. Considering the fact that the proposed method is based on segmentation that does not require any input parameters, this result should be considered satisfactory. In the authors’ opinion, this method can be used in issues of rock image search, sorting, or e.g. automatic selection of optimal segmentation techniques.


title: Scalable Signal-Based Simulation of Autonomous Beings in Complex Environments

text: Simulation of groups of autonomous beings poses a great computational challenge in terms of required time and resources. The need to simulate large environments, numerous populations of beings, and to increase the detail of models causes the need for parallelization of computations. The signal-based simulation algorithm, presented in our previous research, prove the possibility of linear scalability of such computations up to thousands of computing cores. In this paper further extensions of the signal-based models are investigated and new method for defining complex environments is presented. It allows efficient and scalable simulation of structures which cannot be defined using two dimensions, like multi-story buildings, anthills or bee hives. The solution is applied for defining a building evacuation model, which is validated using empirical data from a real-life evacuation drill.


title: Integrating Agent-Based Modelling with Copula Theory: Preliminary Insights and Open Problems

text: The paper sketches and elaborates on a framework integrating agent-based modelling with advanced quantitative probabilistic methods based on copula theory. The motivation for such a framework is illustrated on a artificial market functioning with canonical asset pricing models, showing that dependencies specified by copulas can enrich agent-based models to capture both micro-macro effects (e.g. herding behaviour) and macro-level dependencies (e.g. asset price dependencies). In doing that, the paper highlights the theoretical challenges and extensions that would complete and improve the proposal as a tool for risk analysis.


title: Retrain or Not Retrain? - Efficient Pruning Methods of Deep CNN Networks

text: Nowadays, convolutional neural networks (CNN) play a major role in image processing tasks like image classification, object detection, semantic segmentation. Very often CNN networks have from several to hundred stacked layers with several megabytes of weights. One of the possible techniques to reduce complexity and memory footprint is pruning. Pruning is a process of removing weights which connect neurons from two adjacent layers in the network. The process of finding near optimal solution with specified and acceptable drop in accuracy can be more sophisticated when DL model has higher number of convolutional layers. In the paper few approaches based on retraining and no retraining are described and compared together.


title: The Concept of System for Automated Scientific Literature Reviews Generation

text: We present a concept of system which is aimed to create a literature review of scientific articles having a small sketch of statements as the input. Key elements of the system include transformer-based BERT encoder, deep LSTM decoder and a loss function which combines auto-encoder loss and forces generated summaries to be in the input text domain. We propose to use PMC open access subset for model learning.


title: MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers

text: Multiple Myeloma (MM) is the second most frequent haematological malignancy in the world although the related pathogenesis remains unclear. The study of how gene expression profiling (GEP) is correlated with patients’ survival could be important for understanding the initiation and progression of MM. In order to aid researchers in identifying new prognostic RNA biomarkers as targets for functional cell-based studies, the use of appropriate bioinformatic tools for integrative analysis is required. The main contribution of this paper is the development of a set of functionalities, extending TCGAbiolinks package, for downloading and analysing Multiple Myeloma Research Foundation (MMRF) CoMMpass study data available at the NCI’s Genomic Data Commons (GDC) Data Portal. In this context, we present further a workflow based on the use of this new functionalities that allows to i) download data; ii) perform and plot the Array Array Intensity correlation matrix; ii) correlate gene expression and Survival Analysis to obtain a Kaplan–Meier survival plot.


title: Computational Complexity of Hierarchically Adapted Meshes

text: We show that for meshes hierarchically adapted towards singularities there exists an order of variable elimination for direct solvers that will result in time complexity not worse than [Formula: see text], where N is the number of nodes and q is the dimensionality of the singularity. In particular, we show that this formula does not change depending on the spatial dimensionality of the mesh. We also show the relationship between the time complexity and the Kolmogorov dimension of the singularity.


title: Detection of Tumoral Epithelial Lesions Using Hyperspectral Imaging and Deep Learning

text: We propose a new method for the analysis and classification of HSI images. The method uses deep learning to interpret the molecular vibrational behaviour of healthy and tumoral human epithelial tissue, based on data gathered via SWIR (short-wave infrared) spectroscopy. We analyzed samples of Melanoma, Dysplastic Nevus and healthy skin. Preliminary results show that human epithelial tissue is sensitive to SWIR to the point of making possible the differentiation between healthy and tumor tissues. We conclude that HSI-SWIR can be used to build new methods for tumor classification.


title: Effect of Dataset Size on Efficiency of Collaborative Filtering Recommender Systems with Multi-clustering as a Neighbourhood Identification Strategy

text: Determination of accurate neighbourhood of an active user (a user to whom recommendations are generated) is one of the essential problems that collaborative filtering based recommender systems encounter. Properly adjusted neighbourhood leads to more accurate recommendation generated by a recommender system. In classical collaborative filtering technique, the neighbourhood is modelled by kNN algorithm, but this approach has poor scalability. Clustering techniques, although improved time efficiency of recommender systems, can negatively affect the quality (precision or accuracy) of recommendations. This article presents a new approach to collaborative filtering recommender systems that focuses on the problem of an active user’s neighbourhood modelling. Instead of one clustering scheme, it works on a set of partitions, therefore it selects the most appropriate one that models the neighbourhood precisely. This article presents the results of the experiments validating the advantage of multi-clustering approach, [Formula: see text], over the traditional methods based on single-scheme clustering. The experiments particularly focus on the effect of great size of datasets concerning overall recommendation performance including accuracy and coverage.


title: Data-Driven Approach to Inversion Analysis of Three-Dimensional Inner Soil Structure via Wave Propagation Analysis

text: Various approaches based on both computational science and data science/machine learning have been proposed with the development of observation systems and network technologies. Computation cost associated with computational science can be reduced by introducing the methods based on data science/machine learning. In the present paper, we focus on a method to estimate inner soil structure via wave propagation analysis. It is regarded as one of the parameter optimization approaches using observation data on the surface. This application is in great demand to ensure better reliability in numerical simulations. Typical optimization requires many forward analyses; thus, massive computation cost is required. We propose an approach to substitute evaluation using neural networks for most cases of forward analyses and to reduce the number of forward analyses. Forward analyses in the proposed method are used for producing the training data for a neural network; thereby they can be computed independently, and the actual elapsed time can be reduced by using a large-scale supercomputer. We demonstrated that the inner soil structure was estimated with the sufficient accuracy for practical damage evaluation. We also confirmed that the proposed method achieved estimating parameters within a shorter timeframe compared to a typical approach based on simulated annealing.


title: An Empirical Evaluation of Attention and Pointer Networks for Paraphrase Generation

text: In computer vision, one of the common practices to augment the image dataset is by creating new images using geometric transformation preserving similarity. This data augmentation was one of the most significant factors for winning the Image Net competition in 2012 with vast neural networks. Unlike in computer vision and speech data, there have not been many techniques explored to augment data in natural language processing (NLP). The only technique explored in the text data is lexical substitution, which only focuses on replacing words by synonyms. In this paper, we investigate the use of different pointer networks with the sequence-to-sequence models, which have shown excellent results in neural machine translation (NMT) and text simplification tasks, in generating similar sentences using a sequence-to-sequence model and the paraphrase dataset (PPDB). The evaluation of these paraphrases is carried out by augmenting the training dataset of IMDb movie review dataset and comparing its performance with the baseline model. To our best knowledge, this is the first study on generating paraphrases using these models with the help of PPDB dataset.


title: GCN-IA: User Profile Based on Graph Convolutional Network with Implicit Association Labels

text: Inferring multi-label user profile plays a significant role in providing individual recommendations and exact-marketing, etc. Current researches on multi-label user profile either ignore the implicit associations among labels or do not consider the user and label semantic information in the social networks. Therefore, the user profile inferred always does not take full advantage of the global information sufficiently. To solve above problem, a new insight is presented to introduce implicit association labels as the prior knowledge enhancement and jointly embed the user and label semantic information. In this paper, a graph convolutional network with implicit associations (GCN-IA) method is proposed to obtain user profile. Specifically, a probability matrix is first designed to capture the implicit associations among labels for user representation. Then, we learn user embedding and label embedding jointly based on user-generated texts, relationships and label information. On four real-world datasets in Weibo, experimental results demonstrate that GCN-IA produces a significant improvement compared with some state-of-the-art methods.


title: Challenge Collapsar (CC) Attack Traffic Detection Based on Packet Field Differentiated Preprocessing and Deep Neural Network

text: Distributed Denial of Service (DDoS) attack is one of the top cyber threats. As a kind of application layer DDoS attack, Challenge Collapsar (CC) attack has become a real headache for defenders. However, there are many researches on DDoS attack, but few on CC attack. The related works on CC attack employ rule-based and machine learning-based models, and just validate their models on the outdated public datasets. These works appear to lag behind once the attack pattern changes. In this paper, we present a model based on packet Field Differentiated Preprocessing and Deep neural network (FDPD) to address this problem. Besides, we collected a fresh dataset which contains 7.92 million packets from real network traffic to train and validate FDPD model. The experimental results show that the accuracy of this model reaches 98.55%, the [Formula: see text] value reaches 98.59%, which is 3% higher than the previous models (SVM and Random Forest-based detection model), and the training speed is increased by 17 times in the same environment. It proved that the proposed model can help defenders improve the efficiency of detecting CC attack.


title: An Adaptive Space-Filling Curve Trajectory for Ordering 3D Datasets to 1D: Application to Brain Magnetic Resonance Imaging Data for Classification

text: In this work, we develop an adaptive, near-optimal, 3-Dimensional (3D) to 1D ordering methodology for brain magnetic resonance imaging (MRI) data, using a space-filling curve (SFC) trajectory, which is adaptive to brain’s shape as captured by MRI. We present the pseudocode of the heuristics for developing the SFC trajectory. We apply this trajectory to functional MRI brain activation maps from a schizophrenia study, compress the data, obtain features, and perform classification of schizophrenia patients vs. normal controls. We compare the classification results with those of a linear ordering trajectory, which has been the traditional method for ordering 3D MRI data to 1D. We report that the adaptive SFC trajectory-based classification performance is superior than the linear ordering trajectory-based classification.


title: Reference-Based Haplotype Phasing with FPGAs

text: Haplotype phasing of individual samples is commonly carried out as a precursor step before genotype imputation to reduce the runtime complexity of the imputation step and to improve imputation accuracy. The phasing process is time-consuming and generally exceeds hours even on server-grade computing systems. Loh et al. recently introduced a fast and effective reference-based haplotype phasing software named EAGLE2 which scales linearly with the number of reference samples and variants to phase. We discovered that from the several steps of the EAGLE2 phasing process, data preparation for the internally used HapHedge data structure already consumes about half of the total runtime in general use cases. We addressed this problem by introducing a new design for reconfigurable architectures that accelerates this part of the software by a factor of up to 29 on a Xilinx Kintex UltraScale FPGA, resulting in a total speedup of the complete phasing process of almost 2 (the theoretical limit according to Amdahl’s Law) when compared to a server-grade computing system with two Intel Xeon CPUs. As a result, we reduced the EAGLE2 runtime of genome-wide phasing of 520,000 variants in 2500 samples using the 1000 Genomes Project reference panel from 68 min to 39 min on our system while maintaining quality.


title: Link Prediction by Analyzing Temporal Behavior of Vertices

text: Complexity and dynamics are challenging properties of real-world social networks. Link prediction in dynamic social networks is an essential problem in social network analysis. Although different methods have been proposed to enhance the performance of link prediction, these methods need significant improvement in accuracy. In this study, we focus on the temporal behavior of social networks to predict potential future interactions. We examine the evolving pattern of vertices of a given network [Formula: see text] over time. We introduce a time-varying score function to evaluate the activeness of vertices that uses the number of new interactions and the number of frequent interactions with existing connections. To consider the impact of timestamps of the interactions, the score function engages a time difference of the current time and the time of the interaction occurred. Many existing studies ignored the weight of the link in the given network [Formula: see text], which brings the time-varied details of the links. We consider two additional objective functions in our model: a weighted shortest distance between any two nodes and a weighted common neighbor index. We used Multi-Layer Perceptron (MLP), a deep learning architecture as a classifier to predict the link formation in the future and define our model as a binary classification problem. To evaluate our model, we train and test with six real-world dynamic networks and compare it with state-of-the-art methods as well as classic methods. The results confirm that our proposed method outperforms most of the state-of-the-art methods.


title: Hidden Markov Models and Their Application for Predicting Failure Events

text: We show how Markov mixed membership models (MMMM) can be used to predict the degradation of assets. We model the degradation path of individual assets, to predict overall failure rates. Instead of a separate distribution for each hidden state, we use hierarchical mixtures of distributions in the exponential family. In our approach the observation distribution of the states is a finite mixture distribution of a small set of (simpler) distributions shared across all states. Using tied-mixture observation distributions offers several advantages. The mixtures act as a regularization for typically very sparse problems, and they reduce the computational effort for the learning algorithm since there are fewer distributions to be found. Using shared mixtures enables sharing of statistical strength between the Markov states and thus transfer learning. We determine for individual assets the trade-off between the risk of failure and extended operating hours by combining a MMMM with a partially observable Markov decision process (POMDP) to dynamically optimize the policy for when and how to maintain the asset.


title: A Block Preconditioner for Scalable Large Scale Finite Element Incompressible Flow Simulations

text: We present a block preconditioner, based on the algebraic multigrid method, for solving systems of linear equations, that arise in incompressible flow simulations performed by the stabilized finite element method. We select a set of adjustable parameters for the preconditioner and show how to tune the parameters in order to obtain fast convergence of the standard GMRES solver in which the preconditioner is employed. Additionally, we show some details of the parallel implementation of the preconditioner and the achieved scalability of the solver in large scale parallel incompressible flow simulations.


title: Optimization and Local Time Stepping of an ADER-DG Scheme for Fully Anisotropic Wave Propagation in Complex Geometries

text: We present an extension of the earthquake simulation software SeisSol to support seismic wave propagation in fully triclinic anisotropic materials. To our best knowledge, SeisSol is one of the few open-source codes that offer this feature for simulations at petascale performance and beyond. We employ a Discontinuous Galerkin (DG) method with arbitrary high-order derivative (ADER) time stepping. Here, we present a novel implementation of fully physical anisotropy with a two-sided Godunov flux and local time stepping. We validate our implementation on various benchmarks and present convergence analysis with respect to analytic solutions. An application example of seismic waves scattering around the Zugspitze in the Bavarian Alps demonstrates the capabilities of our implementation to solve geophysics problems fast.


title: Data Assimilation in Volcano Deformation Using Fast Finite Element Analysis with High Fidelity Model

text: Estimation of the inner state of volcanoes are important for understanding the mechanism of eruption and reduction of disaster risk. With the improvement in observation networks, data assimilation of internal magma state using time-history crustal deformation data observed at the surface is expected to be suitable for solving such problems. Using finite-element methods capable of modeling complex geometry is desirable for modeling the three-dimensional heterogeneous crust structure, and nonlinear time-history analysis is required for considering the change in material properties due to the movement of magma. Thus, many cases of large-scale finite-element analysis is required, and the computational cost incurred is expected to become a bottleneck. As a basic study towards data assimilation of internal magma state considering change in material properties of the crust, we demonstrated that many case analyses of volcano deformation problems can be conducted in a reasonable time frame by development of a crustal deformation analysis method accelerated by GPUs. For verification of the data assimilation method, we estimated the magma trend in an actual three-dimensional heterogeneous crust structure without temporal change in material properties. We confirmed that the magma movement trend can be reproduced using the model considering crust heterogeneity, while models disregarding three-dimensional crust structure resulted in wrong estimations. Thus, we can see that using finite-element methods capable of modeling three-dimensional heterogeneity for crustal deformation analysis is important for accurate magma state estimation.


title: Interval Methods for Seeking Fixed Points of Recurrent Neural Networks

text: The paper describes an application of interval methods to train recurrent neural networks and investigate their behavior. The HIBA_USNE multithreaded interval solver for nonlinear systems and algorithmic differentiation using ADHC are used. Using interval methods, we can not only train the network, but precisely localize all stationary points of the network. Preliminary numerical results for continuous Hopfield-like networks are presented.


title: Deep Low-Density Separation for Semi-supervised Classification

text: Given a small set of labeled data and a large set of unlabeled data, semi-supervised learning (ssl) attempts to leverage the location of the unlabeled datapoints in order to create a better classifier than could be obtained from supervised methods applied to the labeled training set alone. Effective ssl imposes structural assumptions on the data, e.g. that neighbors are more likely to share a classification or that the decision boundary lies in an area of low density. For complex and high-dimensional data, neural networks can learn feature embeddings to which traditional ssl methods can then be applied in what we call hybrid methods. Previously-developed hybrid methods iterate between refining a latent representation and performing graph-based ssl on this representation. In this paper, we introduce a novel hybrid method that instead applies low-density separation to the embedded features. We describe it in detail and discuss why low-density separation may better suited for ssl on neural network-based embeddings than graph-based algorithms. We validate our method using in-house customer survey data and compare it to other state-of-the-art learning methods. Our approach effectively classifies thousands of unlabeled users from a relatively small number of hand-classified examples.


title: Reduction of Numerical Errors in Zernike Invariants Computed via Complex-Valued Integral Images

text: Floating-point arithmetics may lead to numerical errors when numbers involved in an algorithm vary strongly in their orders of magnitude. In the paper we study numerical stability of Zernike invariants computed via complex-valued integral images according to a constant-time technique from [2], suitable for object detection procedures. We indicate numerically fragile places in these computations and identify their cause, namely—binomial expansions. To reduce numerical errors we propose piecewise integral images and derive a numerically safer formula for Zernike moments. Apart from algorithmic details, we provide two object detection experiments. They confirm that the proposed approach improves accuracy of detectors based on Zernike invariants.


title: Creating Digital Activity Schedules to Promote Independence and Engagement

text: Photographic activity schedules have been demonstrated to be effective in helping individuals with autism and other developmental disabilities learn how to complete both simple and complex sequences of activities without prompting from adults. Although the majority of research studies demonstrating the effectiveness of activity schedules have used schedule books composed of static printed pictures attached to physical pages, recently researchers have begun to demonstrate the effectiveness of technology-based activity schedules. In the current article, we provide a task analysis for creating both simple and complex digital activity schedules using Google Slides, a freely available, web-based technology that operates on a variety of digital platforms. We also provide suggestions for how behavior analysts can train parents to use this technology with their children using telehealth procedures.


title: An introduction to the (postponed) 5th edition of the International Workshop on Functional and Operatorial Statistics

text: This volume is composed by a set of short papers corresponding to some of the contributions that were sent to be presented at the fifth edition of the International Workshop on Functional and Operatorial Statistics (IWFOS). This fifth edition was to be held in June 2020 in Brno (Czech Republic), but had to be postponed as a consequence of the health crisis caused by the COVID-19 pandemic. The aim of this introduction is to make a fast presentation of these contributions by putting them into the recent trends in Functional Data Analysis and related fields.


title: Replication in the Mononuclear Phagocyte System (MPS) as a Determinant of Hantavirus Pathogenicity

text: Members of different virus families including Hantaviridae cause viral hemorrhagic fevers (VHFs). The decisive determinants of hantavirus-associated pathogenicity are still enigmatic. Pathogenic hantavirus species, such as Puumala virus (PUUV), Hantaan virus (HTNV), Dobrava-Belgrade virus (DOBV), and Sin Nombre virus (SNV), are associated with significant case fatality rates. In contrast, Tula virus (TULV) only sporadically causes mild disease in immunocompetent humans and Prospect Hill virus (PHV) so far has not been associated with any symptoms. They are thus defined here as low pathogenic/apathogenic hantavirus species. We found that productive infection of cells of the mononuclear phagocyte system (MPS), such as monocytes and dendritic cells (DCs), correlated well with the pathogenicity of hantavirus species tested. HTNV (intermediate case fatality rates) replicated more efficiently than PUUV (low case fatality rates) in myeloid cells, whereas low pathogenic/apathogenic hantavirus species did not produce any detectable virus titers. Analysis of PHPUV, a reassortant hantavirus derived from a pathogenic (PUUV) and an apathogenic (PHV) hantavirus species, indicated that the viral glycoproteins are not decisive for replication in MPS cells. Moreover, blocking acidification of endosomes with chloroquine decreased the number of TULV genomes in myeloid cells suggesting a post-entry block for low pathogenic/apathogenic hantavirus species in myeloid cells. Intriguingly, pathogenic but not low pathogenic/apathogenic hantavirus species induced conversion of monocytes into inflammatory DCs. The proinflammatory programming of MPS cells by pathogenic hantavirus species required integrin signaling and viral replication. Our findings indicate that the capacity to replicate in MPS cells is a prominent feature of hantaviral pathogenicity.


Transformations Towards a New Era in Small Scale Forestry: Introduction to the Small-Scale Forestry Special Issue


Shocks to the System


title: The Chaos after the Storm

text: In a world increasingly vulnerable to extremes—be they related to resource scarcity, economic shocks, social disturbances, or other issues—a greater emphasis is being placed on our resilience and ability to recover. This Voices asks: what, where, and how should post-disaster support be improved to facilitate recovery?


Gasteditorial


title: It’s not a cold war: competition and cooperation in US–China relations

text: For nearly 4 years now, the United States and China have experienced increased conflict and tension and diminished cooperation. This has been a costly trend for both countries, as it has led to heightened costs and forsaken potential gain. Heightened US–China security conflict and tension may well be inescapable. The United States and China are engaged in transformative power transition with implications for the balance of power in East Asia and for their respective national security interests. As the power transition continues to develop, it will necessarily contribute to increased great power competition. But the power transition neither precludes moderation in security and economic competition nor cooperation on common interests in bilateral and global affairs. Leadership and policy choices matter in international politics. The current trend toward unmitigated conflict in US–China relations is neither inevitable nor irreversible. Pragmatic leaders that focus on their respective country’s national interests can contribute to constrained conflict and greater cooperation in US–China relations.


title: Radial Basis Function Approximation Optimal Shape Parameters Estimation

text: Radial basis functions (RBF) are widely used in many areas especially for interpolation and approximation of scattered data, solution of ordinary and partial differential equations, etc. The RBF methods belong to meshless methods, which do not require tessellation of the data domain, i.e. using Delaunay triangulation, in general. The RBF meshless methods are independent of a dimensionality of the problem solved and they mostly lead to a solution of a linear system of equations. Generally, the approximation is formed using the principle of unity as a sum of weighed RBFs. These two classes of RBFs: global and local, mostly having a shape parameter determining the RBF behavior. In this contribution, we present preliminary results of the estimation of a vector of “optimal” shape parameters, which are different for each RBF used in the final formula for RBF approximation. The preliminary experimental results proved, that there are many local optima and if an iteration process is to be used, no guaranteed global optima are obtained. Therefore, an iterative process, e.g. used in partial differential equation solutions, might find a local optimum, which can be far from the global optima.


title: Multi-agent Reinforcement Learning Using Simulated Quantum Annealing

text: With quantum computers still under heavy development, already numerous quantum machine learning algorithms have been proposed for both gate-based quantum computers and quantum annealers. Recently, a quantum annealing version of a reinforcement learning algorithm for grid-traversal using one agent was published. We extend this work based on quantum Boltzmann machines, by allowing for any number of agents. We show that the use of quantum annealing can improve the learning compared to classical methods. We do this both by means of actual quantum hardware and by simulated quantum annealing.


title: Prediction of the Free Jet Noise Using Quasi-gas Dynamic Equations and Acoustic Analogy

text: The paper is focused on the numerical simulation of acoustic properties of the free jets from circle nozzle at low and moderate Reynolds numbers. The near-field of compressible jet flow is calculated using developed regularized (quasi-gas dynamic) algorithms solver QGDFoam. Acoustic noise is computed for jets with M = 0.9, Re = 3600 and M = 2.1, Re = 70000 parameters. The acoustic pressure in far field is predicted using the Ffowcs Williams and Hawkings analogy implemented in the libAcoustics library based on the OpenFOAM software package. The determined properties of the flow and acoustic fields are compared with experimental data. The flow structures are characterized by the development of the Kelvin-Helmholtz instability waves, which lead to energy outflux in the radial direction. Their further growth is accompanied by the formation of large and small-scale eddies leading to the generation of acoustic noise. The results showed that for selected jets the highest levels of generated noise is obtained at angles around 30° which agrees well with experimental data.


title: Hybrid Quantum Annealing Heuristic Method for Solving Job Shop Scheduling Problem

text: Scheduling problems have attracted the attention of researchers and practitioners for several decades. The quality of different methods developed to solve these problems on classical computers have been collected and compared in various benchmark repositories. Recently, quantum annealing has appeared as promising approach to solve some scheduling problems. The goal of this paper is to check experimentally if and how this approach can be applied for solving a well-known benchmark of the classical Job Shop Scheduling Problem. We present the existing capabilities provided by the D-Wave 2000Q quantum annealing system in the light of this benchmark. We have tested the quantum annealing system features experimentally, and proposed a new heuristic method as a proof-of-concept. In our approach we decompose the considered scheduling problem into a set of smaller optimization problems which fit better into a limited quantum hardware capacity. We have tuned experimentally various parameters of limited fully-connected graphs of qubits available in the quantum annealing system for the heuristic. We also indicate how new improvements in the upcoming D-Wave quantum processor might potentially impact the performance of our approach.


title: PDPNN: Modeling User Personal Dynamic Preference for Next Point-of-Interest Recommendation

text: Next Point of Interest (POI) recommendation is an important aspect of information feeds for Location Based Social Networks (LSBNs). The boom in LSBN platforms such as Foursquare, Twitter, and Yelp has motivated a considerable amount of research focused on POI recommendations within the last decade. Inspired by the success of deep neural networks in many fields, researchers are increasingly interested in using neural networks such as Recurrent Neural Network (RNN) to make POI recommendation. Compared to traditional methods like Factorizing Personalized Markov Chain (FPMC) and Tensor Factorization (TF), neural network methods show great improvement in general sequences prediction. However, the user’s personal preference, which is crucial for personalized POI recommendation, is not addressed well in existing works. Moreover, the user’s personal preference is dynamic rather than static, which can guide predictions in different temporal and spatial contexts. To this end, we propose a new deep neural network model called Personal Dynamic Preference Neural Network(PDPNN). The core of the PDPNN model includes two parts: one part learns the user’s personal long-term preferences from the historical trajectories, and the other part learns the user’s short-term preferences from the current trajectory. By introducing a similarity function that evaluates the similarity between spatiotemporal contexts of user’s current trajectory and historical trajectories, PDPNN learns the user’s personal dynamic preference from user’s long-term and short-term preferences. We conducted experiments on three real-world datasets, and the results show that our model outperforms current well-known methods.


title: Morphing Numerical Simulation of Incompressible Flows Using Seamless Immersed Boundary Method

text: In this paper, we proposed the morphing simulation method on the Cartesian grid in order to realize flow simulations for shape optimization with lower cost and versatility. In conventional morphing simulations, a simulation is performed while deforming a model shape and the computational grid using the boundary fitting grid. However, it is necessary to deform the computational grid each time, and it is difficult to apply to a model with complicated shape. The present method does not require grid regeneration or deformation. In order to apply the present method to models with various shapes on the Cartesian grid, the seamless immersed boundary method (SIBM) is used. Normally, when the SIBM is applied to a deformed object, the velocity condition on the boundary is imposed by the moving velocity of the boundary. In the present method, the velocity condition is imposed by zero velocity even if the object is deformed because the purpose of the present morphing simulation is to obtain simulation results for a stationary object. In order to verify the present method, two-dimensional simulations for the flow around an object were performed. In order to obtain drag coefficients of multiple models, the object was deformed in turn from the initial model to each model in the present morphing simulation. By using the present method, the drag coefficients for some models could be obtained by one simulation. It is concluded that the flow simulation for shape optimization can be performed very easily by using the present morphing simulation method.


title: Open Boundary Modeling in Molecular Dynamics with Machine Learning

text: Molecular-continuum flow simulations combine molecular dynamics (MD) and computational fluid dynamics for multiscale considerations. A specific challenge in these simulations arises due to the “open MD boundaries” at the molecular-continuum interface: particles close to these boundaries do not feel any forces from outside which results in unphysical behavior and incorrect thermodynamic pressures. In this contribution, we apply neural networks to generate approximate boundary forces that reduce these artefacts. We train our neural network with force-distance pair values from periodic MD simulations and use this network to later predict boundary force contributions in non-periodic MD systems. We study different training strategies in terms of MD sampling and training for various thermodynamic state points and report on accuracy of the arising MD system. We further discuss computational efficiency of our approach in comparison to existing boundary force models.


title: ArtPDGAN: Creating Artistic Pencil Drawing with Key Map Using Generative Adversarial Networks

text: A lot of researches focus on image transfer using deep learning, especially with generative adversarial networks (GANs). However, no existing methods can produce high quality artistic pencil drawings. First, artists do not convert all the details of the photos into the drawings. Instead, artists tend to use strategies to magnify some special parts of the items and cut others down. Second, the elements in artistic drawings may not be located precisely. What’s more, the lines may not relate to the features of the items strictly. To address above challenges, we propose ArtPDGAN, a novel GAN based framework that combines an image-to-image network to generate key map. And then, we use the key map as an important part of input to generate artistic pencil drawings. The key map can show the key parts of the items to guide the generator. We use a paired and unaligned artistic drawing dataset containing high-resolution photos of items and corresponding professional artistic pencil drawings to train ArtPDGAN. Results of our experiments show that the proposed framework performs excellently against existing methods in terms of similarity to artist’s work and user evaluations.


title: Parallel Shared-Memory Isogeometric Residual Minimization (iGRM) for Three-Dimensional Advection-Diffusion Problems

text: In this paper, we present a residual minimization method for three-dimensional isogeometric analysis simulations of advection-diffusion equations. First, we apply the implicit time integration scheme for the three-dimensional advection-diffusion equation. Namely, we utilize the Douglas-Gunn time integration scheme. Second, in every time step, we apply the residual minimization method for stabilization of the numerical solution. Third, we use isogeometric analysis with B-spline basis functions for the numerical discretization. We perform alternating directions splitting of the resulting system of linear equations, so the computational cost of the sequential LU factorization is linear [Formula: see text]. We test our method on the three-dimensional simulation of the advection-diffusion problem. We parallelize the solver for shared-memory machine using the GALOIS framework.


title: Projective Integration for Moment Models of the BGK Equation

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


title: Distributions of a General Reduced-Order Dependence Measure and Conditional Independence Testing

text: We study distributions of a general reduced-order dependence measure and apply the results to conditional independence testing and feature selection. Experiments with Bayesian Networks indicate that using the introduced test in the Grow and Shrink algorithm instead of Conditional Mutual Information yields promising results for Markov Blanket discovery in terms of F measure.


title: Simulation Methodology for Electron Transfer in CMOS Quantum Dots

text: The construction of quantum computer simulators requires advanced software which can capture the most significant characteristics of the quantum behavior and quantum states of qubits in such systems. Additionally, one needs to provide valid models for the description of the interface between classical circuitry and quantum core hardware. In this study, we model electron transport in semiconductor qubits based on an advanced CMOS technology. Starting from 3D simulations, we demonstrate an order reduction and the steps necessary to obtain ordinary differential equations on probability amplitudes in a multi-particle system. We compare numerical and semi-analytical techniques concluding this paper by examining two case studies: the electron transfer through multiple quantum dots and the construction of a Hadamard gate simulated using a numerical method to solve the time-dependent Schrödinger equation and the tight-binding formalism for a time-dependent Hamiltonian.


title: Simulation Based Exploration of Bacterial Cross Talk Between Spatially Separated Colonies in a Multispecies Biofilm Community

text: We present a simple mesoscopic model for bacterial cross-talk between growing biofilm colonies. The simulation setup mimics a novel microfludic biofilm growth reactor which allows a 2D description. The model is a stiff quasilinear system of diffusion-reaction equations with simultaneously a super-diffusion singularity and a degeneracy (as in the porous medium equation) that leads to the formation of sharp interfaces with finite speed of propagation and gradient blow up. We use a finite volume method with arithmetic flux averaging, and a time adaptive stiff time integrator. We find that signal and nutrient transport between colonies can greatly control and limit biofilm response to induction signals, leading to spatially heterogeneous biofilm behavior.


title: NUMA-Awareness as a Plug-In for an Eventify-Based Fast Multipole Method

text: Following the trend towards Exascale, today’s supercomputers consist of increasingly complex and heterogeneous compute nodes. To exploit the performance of these systems, research software in HPC needs to keep up with the rapid development of hardware architectures. Since manual tuning of software to each and every architecture is neither sustainable nor viable, we aim to tackle this challenge through appropriate software design. In this article, we aim to improve the performance and sustainability of FMSolvr, a parallel Fast Multipole Method for Molecular Dynamics, by adapting it to Non-Uniform Memory Access architectures in a portable and maintainable way. The parallelization of FMSolvr is based on Eventify, an event-based tasking framework we co-developed with FMSolvr. We describe a layered software architecture that enables the separation of the Fast Multipole Method from its parallelization. The focus of this article is on the development and analysis of a reusable NUMA module that improves performance while keeping both layers separated to preserve maintainability and extensibility. By means of the NUMA module we introduce diverse NUMA-aware data distribution, thread pinning and work stealing policies for FMSolvr. During the performance analysis the modular design of the NUMA module was advantageous since it facilitates combination, interchange and redesign of the developed policies. The performance analysis reveals that the runtime of FMSolvr is reduced by [Formula: see text] from 1.48 ms to 1.16 ms through these policies.


title: Risk Profiles of Financial Service Portfolio for Women Segment Using Machine Learning Algorithms

text: Typically, women are scored with a lower financial risk than men. However, the understanding of variables and indicators that lead to such results, are not fully understood. Furthermore, the stochastic nature of the data makes it difficult to generate a suitable profile to offer an adequate financial portfolio to the women segment. As the amount, variety, and speed of data increases, so too does the uncertainty inherent within, leading to a lack of confidence in the results. In this research, machine learning techniques are used for data analysis. In this way, faster, more accurate results are obtained than in traditional models (such as statistical models or linear programming) in addition to their scalability.


title: Revisiting Old Combinatorial Beasts in the Quantum Age: Quantum Annealing Versus Maximal Matching

text: This paper experimentally investigates the behavior of analog quantum computers such as commercialized by D-Wave when confronted to instances of the maximum cardinality matching problem specifically designed to be hard to solve by means of simulated annealing. We benchmark a D-Wave “Washington” (2X) with 1098 operational qubits on various sizes of such instances and observe that for all but the most trivially small of these it fails to obtain an optimal solution. Thus, our results suggest that quantum annealing, at least as implemented in a D-Wave device, falls in the same pitfalls as simulated annealing and therefore provides additional evidences suggesting that there exist polynomial-time problems that such a machine cannot solve efficiently to optimality.


title: Simulations of Quantum Finite Automata

text: This paper presents a Python library to simulate different kinds of quantum finite automata on a classical computer. The library also provides tools for language generation and visual representation of simulation results. We have conducted experiments to measure the time complexity of the simulation in a function of the automaton size, alphabet size and word length. Examples of library usage are also provided.


title: An Expanded Mixed Finite Element Method for Space Fractional Darcy Flow in Porous Media

text: In this paper an expanded mixed formulation is introduced to solve the two dimensional space fractional Darcy flow in porous media. By introducing an auxiliary vector, we derive a new mixed formulation and the well-possedness of the formulation can be established. Then the locally mass-conservative expanded mixed finite element method is applied for the solution. Numerical results are shown to verify the efficiency of the proposed algorithm.


title: Design of Short Codes for Quantum Channels with Asymmetric Pauli Errors

text: One of the main problems in quantum information systems is the presence of errors due to noise. Many quantum error correcting codes have been designed to deal with generic errors. In this paper we construct new stabilizer codes able to correct a given number [Formula: see text] of generic Pauli [Formula: see text] and [Formula: see text] errors, plus a number [Formula: see text] of Pauli errors of a specified type (e.g., [Formula: see text] errors). These codes can be of interest when the quantum channel is asymmetric, i.e., when some types of error occur more frequently than others. For example, we design a [[9, 1]] quantum error correcting code able to correct up to one generic qubit error plus one [Formula: see text] error in arbitrary positions. According to a generalized version of the quantum Hamming bound, it is the shortest code with this error correction capability.


title: APE: A Command-Line Tool and API for Automated Workflow Composition

text: Automated workflow composition is bound to take the work with scientific workflows to the next level. On top of today’s comprehensive eScience infrastructure, it enables the automated generation of possible workflows for a given specification. However, functionality for automated workflow composition tends to be integrated with one of the many available workflow management systems, and is thus difficult or impossible to apply in other environments. Therefore we have developed APE (the Automated Pipeline Explorer) as a command-line tool and API for automated composition of scientific workflows. APE is easily configured to a new application domain by providing it with a domain ontology and semantically annotated tools. It can then be used to synthesize purpose-specific workflows based on a specification of the available workflow inputs, desired outputs and possibly additional constraints. The workflows can further be transformed into executable implementations and/or exported into standard workflow formats. In this paper we describe APE v1.0 and discuss lessons learned from applications in bioinformatics and geosciences.


title: High Accuracy Terrain Reconstruction from Point Clouds Using Implicit Deformable Model

text: Few previous works have studied the modeling of forest ground surfaces from LiDAR point clouds using implicit functions. [10] is a pioneer in this area. However, by design this approach proposes over-smoothed surfaces, in particular in highly occluded areas, limiting its ability to reconstruct fine-grained terrain surfaces. This paper presents a method designed to finely approximate ground surfaces by relying on deep learning to separate vegetation from potential ground points, filling holes by blending multiple local approximations through the partition of unity principle, then improving the accuracy of the reconstructed surfaces by pushing the surface towards the data points through an iterative convection model.


title: A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities

text: We develop a novel computational method for evaluating the extreme excursion probabilities arising from random initialization of nonlinear dynamical systems. The method uses excursion probability theory to formulate a sequence of Bayesian inverse problems that, when solved, yields the biasing distribution. Solving multiple Bayesian inverse problems can be expensive; more so in higher dimensions. To alleviate the computational cost, we build machine-learning-based surrogates to solve the Bayesian inverse problems that give rise to the biasing distribution. This biasing distribution can then be used in an importance sampling procedure to estimate the extreme excursion probabilities.


title: Smart Fire Alarm System with Person Detection and Thermal Camera

text: Fire alarm is crucial for safety of life and property in many scenes. A good fire alarm system should be small-sized, low-cost and effective to prevent fire accidents from happening. In this paper we introduce a smart fire alarm system used in kitchen as a representative scenario. The system captures both thermal and optical videos for temperature monitoring and person detection, which are further used to predict potential fire accident and avoid false alarm. Thermal videos are used to record the temperature change in region-of-interests, for example, cookware. YOLOv3-tiny algorithm is modified for person detection and can be iteratively improved with the hard examples gathered by the system. To implement the system on an edge device instead of a server, we propose a high-efficiency neural network inference computing framework called TuringNN. Comprehensive rules enable the system to appropriately respond to different situations. The proposed system has been proved effective in both experiments and numerous cases in complex practical applications.


title: A Bluff-and-Fix Algorithm for Polynomial Chaos Methods

text: Stochastic Galerkin methods can be used to approximate the solution to a differential equation in the presence of uncertainties represented as stochastic inputs or parameters. The strategy is to express the resulting stochastic solution using [Formula: see text] terms of a polynomial chaos expansion and then derive and solve a deterministic, coupled system of PDEs with standard numerical techniques. One of the critical advantages of this approach is its provable convergence as M increases. The challenge is that the solution to the M system cannot easily reuse an already-existing computer solution to the [Formula: see text] system. We present a promising iterative strategy to address this issue. Numerical estimates of the accuracy and efficiency of the proposed algorithm (bluff-and-fix) demonstrate that it can be more effective than using monolithic methods to solve the whole M + 1 system directly.


title: Normal Grouping Density Separation (NGDS): A Novel Object-Driven Indoor Point Cloud Partition Method

text: Precise segmentation/partition is an essential part of many point cloud processing strategies. In the state-of-the-art methods, either the number of clusters or expected supervoxel resolution needs to be carefully selected before segmentation. This makes these processes semi-supervised. The proposed Normal Grouping- Density Separation (NGDS) strategy, relying on both grouping normal vectors into cardinal directions and density-based separation, produces clusters of better (according to use quality measures) quality than current state-of-the-art methods for widely applied object-annotated indoor benchmark dataset. The method reaches, on average, lower under-segmentation error than VCCS (by 45.9pp), Lin et al. (by 14.8pp), and SSP (by 26.2pp). Another metric - achievable segmentation accuracy - yields 92.1% across the tested dataset what is higher value than VCCS (by 14pp), Lin et al. (by 3.8pp), and SSP (by 10.3pp). The experiment carried out indicates superiority of the proposed method as a partition/segmentation algorithm - a process being usually a preprocessing stage of many object detection workflows.


title: Numerical Investigation of Solute Transport in Fractured Porous Media Using the Discrete Fracture Model

text: In this paper, we investigate flow with solute transport in fractured porous media. The system of the governing equations consists of the continuity equation, Darcy’s law, and concentration equation. A discrete-fracture model (DFM) has been developed to describe the problem under consideration. The multiscale time-splitting method was used to handle different sizes of time-step for different physics, such as pressure and concentration. Some numerical examples are presented to show the efficiency of the multi-scale time-splitting approach.


title: Finding Points of Importance for Radial Basis Function Approximation of Large Scattered Data

text: Interpolation and approximation methods are used in many fields such as in engineering as well as other disciplines for various scientific discoveries. If the data domain is formed by scattered data, approximation methods may become very complicated as well as time-consuming. Usually, the given data is tessellated by some method, not necessarily the Delaunay triangulation, to produce triangular or tetrahedral meshes. After that approximation methods can be used to produce the surface. However, it is difficult to ensure the continuity and smoothness of the final interpolant along with all adjacent triangles. In this contribution, a meshless approach is proposed by using radial basis functions (RBFs). It is applicable to explicit functions of two variables and it is suitable for all types of scattered data in general. The key point for the RBF approximation is finding the important points that give a good approximation with high precision to the scattered data. Since the compactly supported RBFs (CSRBF) has limited influence in numerical computation, large data sets can be processed efficiently as well as very fast via some efficient algorithm. The main advantage of the RBF is, that it leads to a solution of a system of linear equations (SLE) Ax = b. Thus any efficient method solves the systems of linear equations that can be used. In this study is we propose a new method of determining the importance points on the scattered data that produces a very good reconstructed surface with higher accuracy while maintaining the smoothness of the surface.


title: Wind Field Parallelization Based on Python Multiprocessing to Reduce Forest Fire Propagation Prediction Uncertainty

text: Forest fires provoke significant loses from the ecological, social and economical point of view. Furthermore, the climate emergency will also increase the occurrence of such disasters. In this context, forest fire propagation prediction is a key tool to fight against these natural hazards efficiently and mitigate the damages. However, forest fire spread simulators require a set of input parameters that, in many cases, cannot be measured and must be estimated indirectly introducing uncertainty in forest fire propagation predictions. One of such parameters is the wind. It is possible to measure wind using meteorological stations and it is also possible to predict wind using meteorological models such as WRF. However, wind components are highly affected by the terrain topography introducing a large degree of uncertainty in forest fire spread predictions. Therefore, it is necessary to introduce wind field models that estimate wind speed and direction at very high resolution to reduce such uncertainty. Such models are time consuming models that are usually executed under strict time constrains. So, it is critical to minimize the execution time, taking into account the fact that in many cases it is not possible to execute the model on a supercomputer, but must be executed on commodity hardware available on the field or at control centers. This work introduces a new parallelization approach for wind field calculation based on Python multiprocessing to accelerate wind field evaluation. The results show that the new approach reduces execution time using a single personal computer.


title: Boosting Group-Level Synergies by Using a Shared Modeling Framework

text: Modern software engineering has established sophisticated tools and workflows that enable distributed development of high-quality software. Here, we present our experiences in adopting these workflows to collectively develop, maintain, and use research software, specifically: a modeling framework for complex and evolving systems. We exemplify how sharing this modeling framework within our research group helped conveying software engineering best practices, fostered cooperation, and boosted synergies. Together, these experiences illustrate that the adoption of modern software engineering workflows is feasible in the dynamically changing academic context, and how these practices facilitate reliability, reproducibility, reusability, and sustainability of research software, ultimately improving the quality of the resulting scientific output.


title: A Posteriori Error Estimation via Differences of Numerical Solutions

text: In this work we address the problem of the estimation of the approximation error that arise at a discretization of the partial differential equations. For this we take advantage of the ensemble of numerical solutions obtained by independent numerical algorithms. To obtain the approximation error, the differences between numerical solutions are treated in the frame of the Inverse Problem that is posed in the variational statement with the zero order regularization. In this work we analyse the ensemble of numerical results that is obtained by five OpenFOAM solvers for the inviscid compressible flow around a cone at zero angle of attack. We present the comparison of approximation errors that are obtained by the Inverse Problem, and the exact error that is computed as the difference of numerical solutions and a high precision solution.


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

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


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

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


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

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


title: An Ontological Approach to Knowledge Building by Data Integration

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


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

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


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

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


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

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


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

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


title: Multi Axes Sliding Mesh Approach for Compressible Viscous Flows

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


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

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


title: Learning Ergodic Averages in Chaotic Systems

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


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

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


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

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


title: Ontology-Driven Edge Computing

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


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

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


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

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


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

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


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

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


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

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


title: Computational Science vs. Zombies

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


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

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