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Numerical Analysis and Scientific Computing Commons™

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2019

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Articles 1051 - 1060 of 1060

Full-Text Articles in Numerical Analysis and Scientific Computing

Description Of Motor Control Using Inverse Models, Anton Sobinov Jan 2019

Description Of Motor Control Using Inverse Models, Anton Sobinov

Graduate Theses, Dissertations, and Problem Reports (ETD)

Humans can perform complicated movements like writing or running without giving them much thought. The scientific understanding of principles guiding the generation of these movements is incomplete. How the nervous system ensures stability or compensates for injury and constraints – are among the unanswered questions today. Furthermore, only through movement can a human impose their will and interact with the world around them. Damage to a part of the motor control system can lower a person’s quality of life. Understanding how the central nervous system (CNS) forms control signals and executes them helps with the construction of devices and rehabilitation …

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Classification Of Stars From Redshifted Stellar Spectra Utilizing Machine Learning, Michael J. Brice Jan 2019

Classification Of Stars From Redshifted Stellar Spectra Utilizing Machine Learning, Michael J. Brice

All Master's Theses

The classification of stellar spectra is a fundamental task in stellar astrophysics. There have been many explorations into the automated classification of stellar spectra but few that involve the Sloan Digital Sky Survey (SDSS). Stellar spectra from the SDSS are applied to standard classification methods such as K-Nearest Neighbors, Random Forest, and Support Vector Machine to automatically classify the spectra. Stellar spectra are high dimensional data and the dimensionality is reduced using standard Feature Selection methods such as Chi-Squared and Fisher score and with domain-specific astronomical knowledge because classifiers work in low dimensional space. These methods are utilized to classify …

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A Method Of Evaluation Of High-Performance Computing Batch Schedulers, Jeremy Stephen Futral Jan 2019

A Method Of Evaluation Of High-Performance Computing Batch Schedulers, Jeremy Stephen Futral

UNF Graduate Theses and Dissertations

According to Sterling et al., a batch scheduler, also called workload management, is an application or set of services that provide a method to monitor and manage the flow of work through the system [Sterling01]. The purpose of this research was to develop a method to assess the execution speed of workloads that are submitted to a batch scheduler. While previous research exists, this research is different in that more complex jobs were devised that fully exercised the scheduler with established benchmarks. This research is important because the reduction of latency even if it is miniscule can lead to massive …

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Modeling Location-Based Social Network Data With Area Attraction And Neighborhood Competition, Thanh Nam Doan, Ee-Peng Lim Jan 2019

Modeling Location-Based Social Network Data With Area Attraction And Neighborhood Competition, Thanh Nam Doan, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Modeling user check-in behavior helps us gain useful insights about venues as well as the users visiting them. These insights are important in urban planning and recommender system applications. Since check-in behavior is the result of multiple factors, this paper focuses on studying two venue related factors, namely, area attraction and neighborhood competition. The former refers to the ability of a spatial area covering multiple venues to collectively attract check-ins from users, while the latter represents the extent to which a venue can compete with other venues in the same area for check-ins. We first embark on empirical studies to …

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Online Burst Events Detection Oriented Real-Time Microblog Message Stream, Guozhong Dong, Jun Gao, Liang Huang, Chunlei Shi Jan 2019

Online Burst Events Detection Oriented Real-Time Microblog Message Stream, Guozhong Dong, Jun Gao, Liang Huang, Chunlei Shi

Research Collection School Of Computing and Information Systems

The rapid spread of microblog messages and sensitivity of unexpected events make microblog become the public opinion center of burst events. Online burst events detection oriented real-time microblog message stream has become an important research problem in the field of microblog public opinion. Because of the large amount of real-time microblog message stream and irregular language of microblog message, it is important to process real-time microblog message stream and detect burst events accurately. In this paper, an online burst events detection framework is proposed. In this framework, abnormal messages are detected based on sliding time window and two-level hash table. …

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Semi-Supervised Deep Embedded Clustering, Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, Zenglin Xu Jan 2019

Semi-Supervised Deep Embedded Clustering, Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, Zenglin Xu

Research Collection School Of Computing and Information Systems

Clustering is an important topic in machine learning and data mining. Recently, deep clustering, which learns feature representations for clustering tasks using deep neural networks, has attracted increasing attention for various clustering applications. Deep embedded clustering (DEC) is one of the state-of-theart deep clustering methods. However, DEC does not make use of prior knowledge to guide the learning process. In this paper, we propose a new scheme of semi-supervised deep embedded clustering (SDEC) to overcome this limitation. Concretely, SDEC learns feature representations that favor the clustering tasks and performs clustering assignments simultaneously. In contrast to DEC, SDEC incorporates pairwise constraints …

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Template-Based Math Word Problem Solvers With Recursive Neural Networks, Lei Wang, Dongxiang Zhang, Jipeng Zhang, Xing Xu, Lianli Gao, Bing Tian Dai, Heng Tao Shen Jan 2019

Template-Based Math Word Problem Solvers With Recursive Neural Networks, Lei Wang, Dongxiang Zhang, Jipeng Zhang, Xing Xu, Lianli Gao, Bing Tian Dai, Heng Tao Shen

Research Collection School Of Computing and Information Systems

The design of automatic solvers to arithmetic math word problems has attracted considerable attention in recent years and a large number of datasets and methods have been published. Among them, Math23K is the largest data corpus that is very helpful to evaluate the generality and robustness of a proposed solution. The best performer in Math23K is a seq2seq model based on LSTM to generate the math expression. However, the model suffers from performance degradation in large space of target expressions. In this paper, we propose a template-based solution based on recursive neural network for math expression construction. More specifically, we …

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Distributed Multi-Label Learning On Apache Spark, Jorge Gonzalez Lopez Jan 2019

Distributed Multi-Label Learning On Apache Spark, Jorge Gonzalez Lopez

Theses and Dissertations

This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes …

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Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia Jan 2019

Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia

Research Collection School Of Computing and Information Systems

Extracting aspect terms and opinion terms are two fundamental tasks in opinion mining. The recent success of deep learning has inspired various neural network architectures, which have been shown to achieve highly competitive performance in these two tasks. However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints. To this end, we first apply a multi-task learning framework to implicitly capture the relations between the two tasks, and then propose a global inference method by explicitly modelling several syntactic …

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Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles Jan 2019

Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles

Computer Science Faculty Publications

We describe our approach for automatically generating presentation slides for scientific papers using deep neural networks. Such slides can help authors have a starting point for their slide generation process. Extractive summarization techniques are applied to rank and select important sentences from the original document. Previous work identified important sentences based only on a limited number of features that were extracted from the position and structure of sentences in the paper. Our method extends previous work by (1) extracting a more comprehensive list of surface features, (2) considering semantic or meaning of the sentence, and (3) using context around the …

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