title: Universal Measure for Medical Image Quality Evaluation Based on Gradient Approach

text: In this paper, a new universal measure of medical images quality is proposed. The measure is based on the analysis of the image by using gradient methods. The number of isolated peaks in the examined image, as a function of the threshold value, is the basis of the assessment of the image quality. It turns out that for higher quality images the curvature of the graph of the said function has a higher value for lower threshold values. On the basis of the observed property, a new method of no-reference image quality assessment has been created. The experimental verification confirmed the method efficiency. The correlation between the arrangement depending on the image quality done by an expert and by using the proposed method is equal to 0.74. This means that the proposed method gives a correlation of higher than the best methods described in the literature. The proposed measure is useful to maximize the image quality while minimizing the time of medical examination.


title: Hybrid Text Feature Modeling for Disease Group Prediction Using Unstructured Physician Notes

text: Existing Clinical Decision Support Systems (CDSSs) largely depend on the availability of structured patient data and Electronic Health Records (EHRs) to aid caregivers. However, in case of hospitals in developing countries, structured patient data formats are not widely adopted, where medical professionals still rely on clinical notes in the form of unstructured text. Such unstructured clinical notes recorded by medical personnel can also be a potential source of rich patient-specific information which can be leveraged to build CDSSs, even for hospitals in developing countries. If such unstructured clinical text can be used, the manual and time-consuming process of EHR generation will no longer be required, with huge person-hours and cost savings. In this article, we propose a generic ICD9 disease group prediction CDSS built on unstructured physician notes modeled using hybrid word embeddings. These word embeddings are used to train a deep neural network for effectively predicting ICD9 disease groups. Experimental evaluation showed that the proposed approach outperformed the state-of-the-art disease group prediction model built on structured EHRs by 15% in terms of AUROC and 40% in terms of AUPRC, thus proving our hypothesis and eliminating dependency on availability of structured patient data.


title: An Information-Theoretic and Dissipative Systems Approach to the Study of Knowledge Diffusion and Emerging Complexity in Innovation Systems

text: The paper applies information theory and the theory of dissipative systems to discuss the emergence of complexity in an innovation system, as a result of its adaptation to an uneven distribution of the cognitive distance between its members. By modelling, on one hand, cognitive distance as noise, and, on the other hand, the inefficiencies linked to a bad flow of information as costs, we propose a model of the dynamics by which a horizontal network evolves into a hierarchical network, with some members emerging as intermediaries in the transfer of knowledge between seekers and problem-solvers. Our theoretical model contributes to the understanding of the evolution of an innovation system by explaining how the increased complexity of the system can be thermodynamically justified by purely internal factors. Complementing previous studies, we demonstrate mathematically that the complexity of an innovation system can increase not only to address the complexity of the problems that the system has to solve, but also to improve the performance of the system in transferring the knowledge needed to find a solution.


title: Different Strategies of Fitting Logistic Regression for Positive and Unlabelled Data

text: In the paper we revisit the problem of fitting logistic regression to positive and unlabelled data. There are two key contributions. First, a new light is shed on the properties of frequently used naive method (in which unlabelled examples are treated as negative). In particular we show that naive method is related to incorrect specification of the logistic model and consequently the parameters in naive method are shrunk towards zero. An interesting relationship between shrinkage parameter and label frequency is established. Second, we introduce a novel method of fitting logistic model based on simultaneous estimation of vector of coefficients and label frequency. Importantly, the proposed method does not require prior estimation, which is a major obstacle in positive unlabelled learning. The method is superior in predicting posterior probability to both naive method and weighted likelihood method for several benchmark data sets. Moreover, it yields consistently better estimator of label frequency than other two known methods. We also introduce simple but powerful representation of positive and unlabelled data under Selected Completely at Random assumption which yields straightforwardly most properties of such model.


title: A Comparison of Generalized Stochastic Milevsky-Promislov Mortality Models with Continuous Non-Gaussian Filters

text: The ability to precisely model mortality rates [Formula: see text] plays an important role from the economic point of view in healthcare. The aim of this article is to propose a comparison of the estimation of the mortality rates based on a class of stochastic Milevsky-Promislov mortality models. We assume that excitations are modeled by second, fourth and sixth order polynomials of outputs from a linear non-Gaussian filter. To estimate the model parameters we use the first and second moments of [Formula: see text]. The theoretical values obtained in both cases were compared with theoretical [Formula: see text] based on a classical Lee-Carter model. The obtained results confirm the usefulness of the switched model based on the continuous non-Gaussian processes used for modeling [Formula: see text].


title: From Population to Subject-Specific Reference Intervals

text: In clinical practice, normal values or reference intervals are the main point of reference for interpreting a wide array of measurements, including biochemical laboratory tests, anthropometrical measurements, physiological or physical ability tests. They are historically defined to separate a healthy population from unhealthy and therefore serve a diagnostic purpose. Numerous cross-sectional studies use various classical parametric and nonparametric approaches to calculate reference intervals. Based on a large cross-sectional study (N = 60,799), we compute reference intervals for subpopulations (e.g. males and females) which illustrate that subpopulations may have their own specific and more narrow reference intervals. We further argue that each healthy subject may actually have its own reference interval (subject-specific reference intervals or SSRIs). However, for estimating such SSRIs longitudinal data are required, for which the traditional reference interval estimating methods cannot be used. In this study, a linear quantile mixed model (LQMM) is proposed for estimating SSRIs from longitudinal data. The SSRIs can help clinicians to give a more accurate diagnosis as they provide an interval for each individual patient. We conclude that it is worthwhile to develop a dedicated methodology to bring the idea of subject-specific reference intervals to the preventive healthcare landscape.


docid: ovsklbpy

title: Dynamic Classifier Selection for Data with Skewed Class Distribution Using Imbalance Ratio and Euclidean Distance

text: Imbalanced data analysis remains one of the critical challenges in machine learning. This work aims to adapt the concept of Dynamic Classifier Selection (dcs) to the pattern classification task with the skewed class distribution. Two methods, using the similarity (distance) to the reference instances and class imbalance ratio to select the most confident classifier for a given observation, have been proposed. Both approaches come in two modes, one based on the k-Nearest Oracles (knora) and the other also considering those cases where the classifier makes a mistake. The proposed methods were evaluated based on computer experiments carried out on [Image: see text] datasets with a high imbalance ratio. The obtained results and statistical analysis confirm the usefulness of the proposed solutions.


title: Fusion Learning: A One Shot Federated Learning

text: Federated Learning is an emerging distributed machine learning technique which does not require the transmission of data to a central server to build a global model. Instead, individual devices build their own models, and the model parameters are transmitted. The server constructs a global model using these parameters, which is then re-transmitted back to the devices. The major bottleneck of this approach is the communication overhead as all the devices need to transmit their model parameters at regular intervals. Here we present an interesting and novel alternative to federated learning known as Fusion Learning, where the distribution parameters of the client’s data along with its local model parameters are sent to the server. The server regenerates the data from these distribution parameters and fuses all the data from multiple devices. This combined dataset is now used to build a global model that is transmitted back to the individual devices. Our experiments show that the accuracy achieved through this approach is in par with both a federated setup and a centralized framework needing only one round of communication to the central server.


title: Quantifying Overfitting Potential in Drug Binding Datasets

text: In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias and detect overfitting. We compare it to a slightly revised version and introduce a new weighted metric. We find that the new metrics allow to quantify overfitting while not overly limiting training data and produce models with greater predictive value.


title: The Challenge of Onboard SAR Processing: A GPU Opportunity

text: Data acquired by a Synthetic Aperture Radar (SAR), onboard a satellite or an airborne platform, must be processed to produce a visible image. For this reason, data must be transferred to the ground station and processed through a time/computing-consuming focusing algorithm. Thanks to the advances in avionic technology, now GPUs are available for onboard processing, and an opportunity for SAR focusing opened. Due to the unavailability of avionic platforms for this research, we developed a GPU-parallel algorithm on commercial off-the-shelf graphics cards, and with the help of a proper scaling factor, we projected execution times for the case of an avionic GPU. We evaluated performance using ENVISAT (Environmental Satellite) ASAR Image Mode level 0 on both NVIDIA Kepler and Turing architectures.


title: An Agent-Based Simulation of the Spread of Dengue Fever

text: Vector-borne diseases (VBDs) account for more than 17% of all infectious diseases, causing more than 700,000 annual deaths. Lack of a robust infrastructure for timely collection, reporting, and analysis of epidemic data undermines necessary preparedness and thus posing serious health challenges to the general public. By developing a simulation framework that models population dynamics and the interactions of both humans and mosquitoes, we may enable epidemiologists to analyze and forecast the transmission and spread of an infectious disease in specific areas. We extend the traditional SEIR (Susceptible, Exposed, Infectious, Recovered) mathematical model and propose an Agent-based model to analyze the interactions between the host and the vector using: (i) our proposed algorithm to compute vector density, based on the reproductive behavior of the vector; and (ii) agent interactions to simulate transmission of virus in a spatio-temporal environment, and forecast the spread of the disease in a given area over a period of time. Our simulation results identify several expected dengue cases and their direction of spread, which can help in detecting epidemic outbreaks. Our proposed framework provides visualization and forecasting capabilities to study the epidemiology of a certain region and aid public health departments in emergency preparedness.


docid: fideirto

title: Asynchronous Actor-Based Approach to Multiobjective Hierarchical Strategy

text: Hierarchical Genetic Strategy (HGS) is a general-purpose optimization metaheuristic based on multi-deme evolutionary-like optimization, while demes are parts of adaptive dynamically changing tree. The paper focuses on adaptation of the classic HGS algorithm for multi-criteria optimization problems, coupling the HGS with Particle Swarm Optimization demes. The main contribution of the paper is showing the efficacy and efficiency of the actor-based implementation of this metaheuristic algorithm.


title: Simulation of Neurotransmitter Flow in Three Dimensional Model of Presynaptic Bouton

text: In this paper a geometrical model for simulation of the nerve impulses inside the presynaptic bouton is designed. The neurotransmitter flow is described by using partial differential equation with nonlinear term. The bouton is modeled as a distorted geosphere and the mitochondrion inside it as a highly modified cuboid. The quality of the mesh elements is examined. The changes of the amount of neurotransmitter during exocytosis are simulated.


title: Detecting the Most Insightful Parts of Documents Using a Regularized Attention-Based Model

text: Every individual text or document is generated for specific purpose(s). Sometime, the text is deployed to convey a specific message about an event or a product. Other occasions, it may be communicating a scientific breakthrough, development or new model and so on. Given any specific objective, the creators and the users of documents may like to know which part(s) of the documents are more influential in conveying their specific messages or achieving their objectives. Understanding which parts of a document has more impact on the viewer’s perception would allow the content creators to design more effective content. Detecting the more impactful parts of a content would help content users, such as advertisers, to concentrate their efforts more on those parts of the content and thus to avoid spending resources on the rest of the document. This work uses a regularized attention-based method to detect the most influential part(s) of any given document or text. The model uses an encoder-decoder architecture based on attention-based decoder with regularization applied to the corresponding weights.


title: Interval Adjoint Significance Analysis for Neural Networks

text: Optimal neural network architecture is a very important factor for computational complexity and memory footprints of neural networks. In this regard, a robust pruning method based on interval adjoints significance analysis is presented in this paper to prune irrelevant and redundant nodes from a neural network. The significance of a node is defined as a product of a node’s interval width and an absolute maximum of first-order derivative of that node’s interval. Based on the significance of nodes, one can decide how much to prune from each layer. We show that the proposed method works effectively on hidden and input layers by experimenting on famous and complex datasets of machine learning. In the proposed method, a node is removed based on its significance and bias is updated for remaining nodes.


title: Classification of Lung Diseases Using Deep Learning Models

text: In this paper we address the problem of medical data scarcity by considering the task of detection of pulmonary diseases from chest X-Ray images using small volume datasets with less than thousand samples. We implemented three deep convolutional neural networks (VGG16, ResNet-50, and InceptionV3) pre-trained on the ImageNet dataset and assesed them in lung disease classification tasks using transfer learning approach. We created a pipeline that segmented chest X-Ray (CXR) images prior to classifying them and we compared the performance of our framework with the existing ones. We demonstrated that pre-trained models and simple classifiers such as shallow neural networks can compete with the complex systems. We also validated our framework on the publicly available Shenzhen and Montgomery lung datasets and compared its performance to the currently available solutions. Our method was able to reach the same level of accuracy as the best performing models trained on the Montgomery dataset however, the advantage of our approach is in smaller number of trainable parameters. Furthermore, our InceptionV3 based model almost tied with the best performing solution on the Shenzhen dataset despite being computationally less expensive.


title: Learning Functions Using Data-Dependent Regularization: Representer Theorem Revisited

text: We introduce a data-dependent regularization problem which uses the geometry structure of the data to learn functions from incomplete data. We show another proof of the standard representer theorem when introducing the problem. At the end of the paper, two applications in image processing are used to illustrate the function learning framework.


title: Using Machine Learning in Accuracy Assessment of Knowledge-Based Energy and Frequency Base Likelihood in Protein Structures

text: Many aspects of the study of protein folding and dynamics have been affected by the accumulation of data about native protein structures and recent advances in machine learning. Computational methods for predicting protein structures from their sequences are now heavily based on machine learning tools and on approaches that extract knowledge and rules from data using probabilistic models. Many of these methods use scoring functions to determine which structure best fits a native protein sequence. Using computational approaches, we obtained two scoring functions: knowledge-based energy and likelihood of base frequency, and we compared their accuracy in measuring the sequence structure fit. We compared the machine learning models’ accuracy of predictions for knowledge-based energy and likelihood values to validate our results, showing that likelihood is a more accurate scoring function than knowledge-based energy.


title: Tree Based Advanced Relative Expression Analysis

text: This paper presents a new concept for biomarker discovery and gene expression data classification that rises from the Relative Expression Analysis (RXA). The basic idea of RXA is to focus on simple ordering relationships between the expression of small sets of genes rather than their raw values. We propose a paradigm shift as we extend RXA concept to tree-based Advanced Relative Expression Analysis (ARXA). The main contribution is a decision tree with splitting nodes that consider relative fraction comparisons between multiple gene pairs. In addition, to face the enormous computational complexity of RXA, the most time-consuming part which is scoring all possible gene pairs in each splitting node is parallelized using GPU. This way the algorithm allows searching for more tailored interactions between sub-groups of genes in a reasonable time. Experiments carried out on 8 cancer-related datasets show not only significant improvement in accuracy and speed of our approach in comparison to various RXA solutions but also new interesting patterns between subgroups of genes.


title: Hybrid SWAN for Fast and Efficient Practical Wave Modelling - Part 2

text: In the Netherlands, for coastal and inland water applications, wave modelling with SWAN on structured computational grids has become a main ingredient. However, computational times are relatively high. Benchmarks showed that the MPI version of SWAN is not that efficient as the OpenMP version within a single node. Therefore, in a previous paper [5] a hybrid version of SWAN was proposed for computations on structured computational grids. It combines the efficiency of the OpenMP version on shared memory with the capability of the MPI version to distribute memory over nodes. In the current paper we extend this approach by an improved implementation, verification of the model performance with a testbed, and extensive benchmarks of its parallel performance. With these benchmarks for important real life applications we show the significance of this hybrid version. We optimize the approach and illustrate the behavior for larger number of nodes. Parallel I/O will be subject of future research.


docid: zscbnitg

title: Bootstrap Bias Corrected Cross Validation Applied to Super Learning

text: Super learner algorithm can be applied to combine results of multiple base learners to improve quality of predictions. The default method for verification of super learner results is by nested cross validation; however, this technique is very expensive computationally. It has been proposed by Tsamardinos et al., that nested cross validation can be replaced by resampling for tuning hyper-parameters of the learning algorithms. The main contribution of this study is to apply this idea to verification of super learner. We compare the new method with other verification methods, including nested cross validation. Tests were performed on artificial data sets of diverse size and on seven real, biomedical data sets. The resampling method, called Bootstrap Bias Correction, proved to be a reasonably precise and very cost-efficient alternative for nested cross validation.


title: Design of Loss Functions for Solving Inverse Problems Using Deep Learning

text: Solving inverse problems is a crucial task in several applications that strongly affect our daily lives, including multiple engineering fields, military operations, and/or energy production. There exist different methods for solving inverse problems, including gradient based methods, statistics based methods, and Deep Learning (DL) methods. In this work, we focus on the latest. Specifically, we study the design of proper loss functions for dealing with inverse problems using DL. To do this, we introduce a simple benchmark problem with known analytical solution. Then, we propose multiple loss functions and compare their performance when applied to our benchmark example problem. In addition, we analyze how to improve the approximation of the forward function by: (a) considering a Hermite-type interpolation loss function, and (b) reducing the number of samples for the forward training in the Encoder-Decoder method. Results indicate that a correct design of the loss function is crucial to obtain accurate inversion results.


title: Testing the Significance of Interactions in Genetic Studies Using Interaction Information and Resampling Technique

text: Interaction information is a model-free, non-parametric measure used for detection of interaction among variables. It frequently finds interactions which remain undetected by standard model-based methods. However in the previous studies application of interaction information was limited by lack of appropriate statistical tests. We study a challenging problem of testing the positiveness of interaction information which allows to confirm the statistical significance of the investigated interactions. It turns out that commonly used chi-squared test detects too many spurious interactions when the dependence between the variables (e.g. between two genetic markers) is strong. To overcome this problem we consider permutation test and also propose a novel HYBRID method that combines permutation and chi-squared tests and takes into account dependence between studied variables. We show in numerical experiments that, in contrast to chi-squared based test, the proposed method controls well the actual significance level and in many situations detects interactions which are undetected by standard methods. Moreover HYBRID method outperforms permutation test with respect to power and computational efficiency. The method is applied to find interactions among Single Nucleotide Polymorphisms as well as among gene expression levels of human immune cells.


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].


docid: owascwvp

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.


docid: vpyeiifd

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.


docid: bgbkgdbo

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.


docid: lbueohsd

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.


docid: xljrlido

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.


docid: bvfsznob

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.


docid: clkgcdzh

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.