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International Conference on computational science and computational Intelligence (CSCI)

作者： Herious Cotton Tor A. Kwembe Computational Data Enabled Science and Engineering Jackson State University Jackson MS USA Department of Mathematics and Statistical Sciences Jackson State University Jackson MS USA

ISBN: (纸本)9781665458429

In this paper, we used data analytics to analyze criminal data. Prophet model, LSTM recurrent neural network model, a linear regression model, and traditional neural network model were used to predict homicide and rape in the Southeastern Cities of Memphis Tennessee, Jackson Mississippi, and New Orleans Louisiana. LSTM recurrent neural network model and traditional neural network model have smaller RMSE. Thus, LSTM recurrent neural network model and traditional neural network model performed better than the prophet and linear regression models. These promising outcomes will be significant to scholars, policymakers, and law enforcement officers.

关键词： Recurrent neural networks data analysis Law enforcement Scientific computing Urban areas Linear regression data visualization

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Automated, high throughput exploration of process–structure–property relationships using the MapReduce paradigm

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Materials Discovery 2015年 1卷 21-28页

作者： Wodo, Olga Zola, Jaroslaw Sarath Pokuri, Balaji Sesha Du, Pengfei Ganapathysubramanian, Baskar University at Buffalo Materials Design and Innovation Department Buffalo United States University at Buffalo Mechanical and Aerospace Engineering Department Buffalo United States University at Buffalo Computational and Data-Enabled Science and Engineering Program Buffalo United States University at Buffalo Computer Science and Engineering Department Buffalo United States University at Buffalo Biomedical Informatics Department Buffalo United States Iowa State University Mechanical Engineering Department Ames United States Iowa State University Electrical and Computer Engineering Department Ames United States

The microstructure of a material intimately affects the performance of a device made from this material. The microstructure, in turn, is affected by the processing pathway used to fabricate the device. This forms the process–structure–property triangle that is central to material science. There has been increasing interest to comprehensively understand and subsequently exploit process–structure–property (PSP) relationships to design processing pathways that result in tailored microstructures exhibiting optimal properties. However, unraveling process–structure–property relationships usually requires systematic and tedious combinatorial search of process and system variables to identify the microstructures that are produced. This is further complicated by the necessity to interrogate the properties of the huge set of corresponding microstructures. Motivated by this challenge, we focus on developing a generic methodology to establish and explore PSP pathways. We leverage recent advances in high performance computing (HPC) and high throughput computing (HTC) with the premise that a domain expert should be able to focus on domain specific PSP problems while the highly specialized HPC/HTC knowledge needed to approach such problems should be hidden from the domain expert. Our hypothesis is that PSP exploration can be naturally formulated in terms of a standard paradigm in cloud computing, namely the MapReduce programming model. We show how reformulating PSP exploration into a MapReduce workflow enables us to take advantage of advances in cloud computing while requiring minimal specialized knowledge of HPC. We illustrate this generic approach by exploring PSP relationships relevant to organic photovoltaics. We focus on identifying microstructural traits that correlate with specific properties of the photovoltaic process: exciton generation, exciton dissociation and charge generation. We integrate a graph-based microstructure characterization tool, and a microstructure-aware

关键词： Drift diffusion model High throughput analysis MapReduce paradigm Organic solar cells Process–structure–processing Thermal annealing

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Emergency Care Patient Prediction using Electronic Health Records (EHR) data: An End-to-End Machine Learning Pipeline

Emergency Care Patient Prediction using Electronic Health Re...

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International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Maruful Abedin Sumon Tor A. Kwemebe Venkata K. Melapu Md Mohiuddin Hasan Computational Data-Enabled Science and Engineering Jackson State University Jackson USA Department of Mathematics and Computer Science Jackson State University Jackson USA Computer Science and Engineering Jackson State University Jackson USA

Using electronic health records (EHR) data for predicting the condition of patients who are in need of emergency care is a promising application of machine learning. With the help of machine learning, complex problems of identification of patterns can be solved with ease and accuracy when proper machine learning model is implemented. This paper uses EHR data to build an end-to-end machine learning pipeline for predicting the outcome of emergency care patients. As a machine learning process, the pipeline includes data preprocessing, feature engineering, model selection, data set training and testing, evaluation, and deployment. By using EHR data in this pipeline, the model has been trained and tested on a large dataset of many patients information with different types of machine learning algorithms to improve and get the best prediction accuracy. This pipeline can aid in managing primary care patients efficiently and effectively with the implementation of best suitable machine learning algorithm at the time of urgency. By implementing the model on the EHR dataset, an accuracy of more than $75 \%$ is obtained which indicates the efficiency of the machine learning process in the process of identifying patients who need the emergency care more than the general patients when taken to the primary care or emergency.

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Accuracy of a one-dimensional reduction of dynamical systems on networks

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Physical Review E 2022年第2期105卷 024305-024305页

作者： Prosenjit Kundu Hiroshi Kori Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Department of Complexity Science and Engineering The University of Tokyo Chiba 277-8561 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Resilience is an ability of a system with which the system can adjust its activity to maintain its functionality when it is perturbed. To study resilience of dynamics on networks, Gao et al. [Nature (London) 530, 307 (2016)] proposed a theoretical framework to reduce dynamical systems on networks, which are high dimensional in general, to one-dimensional dynamical systems. The accuracy of this one-dimensional reduction relies on three approximations in addition to the assumption that the network has a negligible degree correlation. In the present study, we analyze the accuracy of the one-dimensional reduction assuming networks without degree correlation. We do so mainly through examining the validity of the individual assumptions underlying the method. Across five dynamical system models, we find that the accuracy of the one-dimensional reduction hinges on the spread of the equilibrium value of the state variable across the nodes in most cases. Specifically, the one-dimensional reduction tends to be accurate when the dispersion of the node's state is small. We also find that the correlation between the node's state and the node's degree, which is common for various dynamical systems on networks, is unrelated to the accuracy of the one-dimensional reduction.

关键词： Complex systems Dynamics of networks Network resilience

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Dimension reduction of dynamical systems on networks with leading and non-leading eigenvectors of adjacency matrices

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Physical Review Research 2022年第2期4卷 023257-023257页

作者： Naoki Masuda Prosenjit Kundu Department of Mathematics State University of New York at Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Dimension reduction techniques for dynamical systems on networks are considered to promote our understanding of the original high-dimensional dynamics. One strategy of dimension reduction is to derive a low-dimensional dynamical system whose behavior approximates the observables of the original dynamical system that are weighted linear summations of the state variables at the different nodes. Recently proposed methods use the leading eigenvector of the adjacency matrix of the network as the mixture weights to obtain such observables. In the present study, we explore performances of this type of one-dimensional reductions of dynamical systems on networks when we use non-leading eigenvectors of the adjacency matrix as the mixture weights. Our theory predicts that non-leading eigenvectors can be more efficient than the leading eigenvector and enables us to select the eigenvector minimizing the error. We numerically verify that the optimal non-leading eigenvector outperforms the leading eigenvector for some dynamical systems and networks. We also argue that, despite our theory, it is practically better to use the leading eigenvector as the mixture weights to avoid misplacing the bifurcation point too distantly and to be resistant against dynamical noise.

关键词： Network resilience

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Metapopulation models imply non-Poissonian statistics of interevent times

arXiv

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arXiv 2021年

作者： dos Reis, Elohim Fonseca Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States

Interevent times in temporal contact data from humans and animals typically obey heavy-tailed distributions, and this property impacts contagion and other dynamical processes on networks. We theoretically show that distributions of interevent times heavier-tailed than exponential distributions are a consequence of the most basic metapopulation model used in epidemiology and ecology, in which individuals move from a patch to another according to the simple random walk. Our results hold true irrespectively of the network structure and also for more realistic mobility rules such as high-order random walks and the recurrent mobility patterns used for modeling human dynamics. Copyright © 2021, The Authors. All rights reserved.

关键词： Random processes

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Opinion dynamics on tie-decay networks

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Physical Review Research 2021年第2期3卷 023249-023249页

作者： Kashin Sugishita Mason A. Porter Mariano Beguerisse-Díaz Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA and Faculty of Science and Engineering Waseda University 169-8555 Tokyo Japan

In social networks, interaction patterns typically change over time. We study opinion dynamics on tie-decay networks in which tie strength increases instantaneously when there is an interaction and decays exponentially between interactions. Specifically, we formulate continuous-time Laplacian dynamics and a discrete-time DeGroot model of opinion dynamics on these tie-decay networks, and we carry out numerical computations for the continuous-time Laplacian dynamics. We examine the speed of convergence by studying the spectral gaps of combinatorial Laplacian matrices of tie-decay networks. First, we compare the spectral gaps of the Laplacian matrices of tie-decay networks that we construct from empirical data with the spectral gaps for corresponding randomized and aggregate networks. We find that the spectral gaps for the empirical networks tend to be smaller than those for the randomized and aggregate networks. Second, we study the spectral gap as a function of the tie-decay rate and time. Intuitively, we expect small tie-decay rates to lead to fast convergence because the influence of each interaction between two nodes lasts longer for smaller decay rates. Moreover, as time progresses and more interactions occur, we expect eventual convergence. However, we demonstrate that the spectral gap need not decrease monotonically with respect to the decay rate or increase monotonically with respect to time. Our results highlight the importance of the interplay between the times that edges strengthen and decay in temporal networks.

关键词： Network structure Social networks Consensus & non-consensus models

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Metapopulation models imply non-Poissonian statistics of interevent times

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Physical Review Research 2022年第1期4卷 013050-013050页

作者： Elohim Fonseca dos Reis Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo 14260-2900 New York USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo 14260-5030 New York USA Faculty of Science and Engineering Waseda University Tokyo 169-8555 Japan

Interevent times in temporal contact data from humans and animals typically obey heavy-tailed distributions, which impacts contagion and other dynamical processes on networks. We theoretically show that distributions of interevent times heavier-tailed than exponential distributions are a consequence of the most basic metapopulation model used in epidemiology and ecology, in which individuals move from one patch to another according to the simple random walk. Our results hold true irrespective of the network structure and also for more realistic mobility rules such as high-order random walks and the recurrent mobility patterns used for modeling human dynamics.

关键词： Continuous-time random walk Population dynamics Networks & random structures Social networks Diffusion & random walks Markovian processes Non-Markovian processes

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Randomizing hypergraphs preserving degree correlation and local clustering

arXiv

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arXiv 2021年

作者： Nakajima, Kazuki Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Japan Department of Mathematics State University of New York Buffalo United States Computational and Data-Enabled Science and Engineering Program State University of New York Buffalo United States Faculty of Science and Engineering Waseda University Japan

Many complex systems involve direct interactions among more than two entities and can be represented by hypergraphs, in which hyperedges encode higher-order interactions among an arbitrary number of nodes. To analyze structures and dynamics of given hypergraphs, a solid practice is to compare them with those for randomized hypergraphs that preserve some specific properties of the original hypergraphs. In the present study, we propose a family of such reference models for hypergraphs, called the hyper dk-series, by extending the so-called dk-series for dyadic networks to the case of hypergraphs. The hyper dk-series preserves up to the individual node’s degree, node’s degree correlation, node’s redundancy coefficient, and/or the hyperedge’s size depending on the parameter values. Furthermore, we numerically find that higher-order hyper dk-series more accurately preserves the shortest path length and degree distribution of the one-mode projection of the original hypergraph, which the method does not intend to preserve. We also apply the hyper dk-series to numerical simulations of epidemic spreading and evolutionary game dynamics on empirical social hypergraphs. We find that the hyperedge’s size affects these dynamics more than any of the node’s properties and that the node’s degree correlation and redundancy in the empirical hypergraphs promote cooperation. Copyright © 2021, The Authors. All rights reserved.

关键词： Redundancy

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Entropy-isomap: Manifold learning for high-dimensional dynamic processes

arXiv

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arXiv 2018年

作者： Schoeneman, Frank Chandola, Varun Napp, Nils Wodo, Olga Zola, Jaroslaw Computer Science & Engineering University at Buffalo BuffaloNY United States Computer Science & Engineering Computational and Data-enabled Science & Engineering University at Buffalo BuffaloNY United States Materials Design & Innovation University at Buffalo BuffaloNY United States Computer Science & Engineering Biomedical Informatics University at Buffalo BuffaloNY United States

Scientific and engineering processes deliver massive high-dimensional data sets that are generated as non-linear transformations of an initial state and few process parameters. Mapping such data to a low-dimensional manifold facilitates better understanding of the underlying processes, and enables their optimization. In this paper, we first show that off-the-shelf non-linear spectral dimensionality reduction methods, e.g., Isomap, fail for such data, primarily due to the presence of strong temporal correlations. Then, we propose a novel method, Entropy-Isomap, to address the issue. The proposed method is successfully applied to large data describing a fabrication process of organic materials. The resulting low-dimensional representation correctly captures process control variables, allows for low-dimensional visualization of the material morphology evolution, and provides key insights to improve the process. Copyright © 2018, The Authors. All rights reserved.

关键词： Entropy

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