Motbfs: An R Package For Learning Hybrid Bayesian Networks Using Mixtures Of Truncated Basis Functions,
2020
University of Almería
Motbfs: An R Package For Learning Hybrid Bayesian Networks Using Mixtures Of Truncated Basis Functions, Inmaculada Pérez-Bernabé, Ana D. Maldonado, Antonio Salmerón, Thomas D. Nielsen
The R Journal
This paper introduces MoTBFs, an R package for manipulating mixtures of truncated basis functions. This class of functions allows the representation of joint probability distributions involving discrete and continuous variables simultaneously, and includes mixtures of truncated exponentials and mixtures of polynomials as special cases. The package implements functions for learning the parameters of univariate, multivariate, and conditional distributions, and provides support for parameter learning in Bayesian networks with both discrete and continuous variables. Probabilistic inference using forward sampling is also implemented. Part of the functionality of the MoTBFs package relies on the bnlearn package, which includes functions for learning the …
Kuhn-Tucker And Multiple Discrete-Continuous Extreme Value Model Estimation And Simulation In R: The Rmdcev Package,
2020
University of Saskatchewan
Kuhn-Tucker And Multiple Discrete-Continuous Extreme Value Model Estimation And Simulation In R: The Rmdcev Package, Patrick Lloyd-Smith
The R Journal
This paper introduces the package rmdcev in R for estimation and simulation of KuhnTucker demand models with individual heterogeneity. The models supported by rmdcev are the multiple-discrete continuous extreme value (MDCEV) model and Kuhn-Tucker specification common in the environmental economics literature on recreation demand. Latent class and random parameters specifications can be implemented and the models are fit using maximum likelihood estimation or Bayesian estimation. The rmdcev package also implements demand forecasting and welfare calculation for policy simulation. The purpose of this paper is to describe the model estimation and simulation framework and to demonstrate the functionalities of rmdcev using …
Kspm: A Package For Kernel Semi-Parametric Models,
2020
Montreal university
Kspm: A Package For Kernel Semi-Parametric Models, Catherine Schramm, Sébastien Jacquemont, Karim Oualkacha, Aurélie Labbe, Celia M. T. Greenwood
The R Journal
Kernel semi-parametric models and their equivalence with linear mixed models provide analysts with the flexibility of machine learning methods and a foundation for inference and tests of hypothesis. These models are not impacted by the number of predictor variables, since the kernel trick transforms them to a kernel matrix whose size only depends on the number of subjects. Hence, methods based on this model are appealing and numerous, however only a few R programs are available and none includes a complete set of features. Here, we present the KSPM package to fit the kernel semi-parametric model and its extensions in …
Ordinalclust: An R Package To Analyze Ordinal Data,
2020
Université de Lyon
Ordinalclust: An R Package To Analyze Ordinal Data, Margot Selosse, Julien Jacques, Christophe Biernacki
The R Journal
Ordinal data are used in many domains, especially when measurements are collected from people through observations, tests, or questionnaires. ordinalClust is an innovative R package dedicated to ordinal data that provides tools for modeling, clustering, co-clustering and classifying such data. Ordinal data are modeled using the BOS distribution, which is a model with two meaningful parameters referred to as "position" and "precision". The former indicates the mode of the distribution and the latter describes how scattered the data are around the mode: the user is able to easily interpret the distribution of their data when given these two parameters. The …
A Fast And Scalable Implementation Method For Competing Risks Data With The R Package Fastcmprsk,
2020
University of Southern California
A Fast And Scalable Implementation Method For Competing Risks Data With The R Package Fastcmprsk, Eric S. Kawaguchi, Jenny I. Shen, Gang Li, Marc A. Suchard
The R Journal
Advancements in medical informatics tools and high-throughput biological experimentation make large-scale biomedical data routinely accessible to researchers. Competing risks data are typical in biomedical studies where individuals are at risk to more than one cause (type of event) which can preclude the others from happening. The Fine and Gray (1999) proportional subdistribution hazards model is a popular and well-appreciated model for competing risks data and is currently implemented in a number of statistical software packages. However, current implementations are not computationally scalable for large-scale competing risks data. We have developed an R package, fastcmprsk, that uses a novel forward-backward scan …
Fitzroy: An R Package To Encourage Reproducible Sports Analysis,
2020
University of New South Wales
Fitzroy: An R Package To Encourage Reproducible Sports Analysis, Robert Nguyen, James Day, David Warton, Oscar Lane
The R Journal
The importance of reproducibility, and the related issue of open access to data, has received a lot of recent attention. Momentum on these issues is gathering in the sports analytics community. While Australian Rules football (AFL) is the leading commercial sport in Australia, unlike popular international sports, there has been no mechanism for the public to access comprehensive statistics on players and teams. Expert commentary currently relies heavily on data that isn’t made readily accessible and this produces an unnecessary barrier for the development of an inclusive sports analytics community. We present the R package fitzRoy to provide easy access …
Tulip: A Toolbox For Linear Discriminant Analysis With Penalties,
2020
Florida State University
Tulip: A Toolbox For Linear Discriminant Analysis With Penalties, Yuqing Pan, Qing Mai, Xin Zhang
The R Journal
Linear discriminant analysis (LDA) is a powerful tool in building classifiers with easy computation and interpretation. Recent advancements in science technology have led to the popularity of datasets with high dimensions, high orders and complicated structure. Such datasetes motivate the generalization of LDA in various research directions. The R package TULIP integrates several popular high-dimensional LDA-based methods and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification. Functions are included for model fitting, cross validation and prediction. In addition, motivated by datasets with diverse sources of predictors, we further include functions for covariate adjustment. Our package is …
Comparing Multiple Survival Functions With Crossing Hazards In R,
2020
Academia Sinica
Comparing Multiple Survival Functions With Crossing Hazards In R, Hsin-Wen Chang, Pei-Yuan Tsai, Jen-Tse Kao, Guo-You Lan
The R Journal
It is frequently of interest in time-to-event analysis to compare multiple survival functions nonparametrically. However, when the hazard functions cross, tests in existing R packages do not perform well. To address the issue, we introduce the package survELtest, which provides tests for comparing multiple survival functions with possibly crossing hazards. Due to its powerful likelihood ratio formulation, this is the only R package to date that works when the hazard functions cross. We illustrate the use of the procedures in survELtest by applying them to data from randomized clinical trials and simulated datasets. We show that these methods lead …
The Biglasso Package: A Memory- And Computation-Efficient Solver For Lasso Model Fitting With Big Data In R,
2020
University of Iowa
The Biglasso Package: A Memory- And Computation-Efficient Solver For Lasso Model Fitting With Big Data In R, Yaohui Zeng, Patrick Breheny
The R Journal
Penalized regression models such as the lasso have been extensively applied to analyzing high-dimensional data sets. However, due to memory limitations, existing R packages like glmnet and ncvreg are not capable of fitting lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are increasingly seen in many areas such as genetics, genomics, biomedical imaging, and high-frequency finance. In this research, we implement an R package called biglasso that tackles this challenge. biglasso utilizes memory-mapped files to store the massive data on the disk, only reading data into memory when necessary during model fitting, and is thus able to handle out-of-core computation …
Exploring Information For Quantum Machine Learning Models,
2020
University of Louisville
Exploring Information For Quantum Machine Learning Models, Michael Telahun
Electronic Theses and Dissertations
Quantum computing performs calculations by using physical phenomena and quantum mechanics principles to solve problems. This form of computation theoretically has been shown to provide speed ups to some problems of modern-day processing. With much anticipation the utilization of quantum phenomena in the field of Machine Learning has become apparent. The work here develops models from two software frameworks: TensorFlow Quantum (TFQ) and PennyLane for machine learning purposes. Both developed models utilize an information encoding technique amplitude encoding for preparation of states in a quantum learning model. This thesis explores both the capacity for amplitude encoding to provide enriched state …
Robust, Fine-Grained Occupancy Estimation Via Combined Camera & Wifi Indoor Localization,
2020
Singapore Management University
Robust, Fine-Grained Occupancy Estimation Via Combined Camera & Wifi Indoor Localization, Anuradha Ravi, Archan Misra
Research Collection School Of Computing and Information Systems
We describe the development of a robust, accurate and practically-validated technique for estimating the occupancy count in indoor spaces, based on a combination of WiFi & video sensing. While fusing these two sensing-based inputs is conceptually straightforward, the paper demonstrates and tackles the complexity that arises from several practical artefacts, such as (i) over-counting when a single individual uses multiple WiFi devices and under-counting when the individual has no such device; (ii) corresponding errors in image analysis due to real-world artefacts, such as occlusion, and (iii) the variable errors in mapping image bounding boxes (which can include multiple possible types …
Static And Dynamical Properties Of Multiferroics,
2020
University of Arkansas, Fayetteville
Static And Dynamical Properties Of Multiferroics, Sayed Omid Sayedaghaee
Graduate Theses and Dissertations
Since the silicon industrial revolution in the 1950s, a lot of effort was dedicated to the research and development activities focused on material and solid-state sciences. As a result, several cutting-edge technologies are emerging including the applications of functional materials in the design and enhancement of novel devices such as sensors, highly capable data storage media, actuators, transducers, and several other types of electronic tools. In the last two decades, a class of functional materials known as multiferroics has captured significant attention because of providing a huge potential for new designs due to possessing multiple ferroic order parameters at the …
Variational Bayesian Inference For Crowdsourcing Predictions,
2020
Singapore Management University
Variational Bayesian Inference For Crowdsourcing Predictions, Desmond Cai, Duc Thien Nguyen, Shiau Hong Lim, Laura Wynter
Research Collection School Of Computing and Information Systems
Crowdsourcing has emerged as an effective means for performing a number of machine learning tasks such as annotation and labelling of images and other data sets. In most early settings of crowdsourcing, the task involved classification, that is assigning one of a discrete set of labels to each task. Recently, however, more complex tasks have been attempted including asking crowdsource workers to assign continuous labels, or predictions. In essence, this involves the use of crowdsourcing for function estimation. We are motivated by this problem to drive applications such as collaborative prediction, that is, harnessing the wisdom of the crowd to …
Prediction Of Nocturia In Live Alone Elderly Using Unobtrusive In-Home Sensors,
2020
Singapore Management University
Prediction Of Nocturia In Live Alone Elderly Using Unobtrusive In-Home Sensors, Barry Nuqoba, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Nocturia, or the need to void (or urinate) one or more times in the middle of night time sleeping, represents a significant economic burden for individuals and healthcare systems. Although it can be diagnosed in the hospital, most people tend to regard nocturia as a usual event, resulting in underreported diagnosis and treatment. Data from self-reporting via a voiding diary may be irregular and subjective especially among the elderly due to memory problems. This study aims to detect the presence of nocturia through passive in-home monitoring to inform intervention (e.g., seeking diagnosis and treatment) to improve the physical and mental …
Time-Varying Parameter System Modeling Method Based On Zonotope-Ellipsoid Double Filtering,
2020
Engineering Research Center of Internet of Things Technology Applications (Ministry of Education),Jiangnan University,Wuxi 214122,China;
Time-Varying Parameter System Modeling Method Based On Zonotope-Ellipsoid Double Filtering, Ziyun Wang, Peiyu Wang, Yacong Zhan
Journal of System Simulation
Abstract: The traditional system modeling method using zonotopes as the feasible parameter sets islikely to increase the computational complexity of the algorithm due to the increasing dimensions of the zonotope shape matrix. This paper proposes a time-varying parameter modeling systems method based on zonotope-ellipsoid double filtering technique. Considering the time-varying parameters, a zonotope with the minimum volume is obtained during the iterations of the intersection with the constraint strip. After transforming the shape matrix of the zonotope, the dimensionality reduction is performed, instead of directly finding the row sum of the extended shape matrix, to reduce the algorithm conservativeness originated …
Research On Configurable Simulation Integration Technology For Equipment Software,
2020
1. The Second System Design of the Second Research Academy of CASIC,Beijing 100854,China; ;
Research On Configurable Simulation Integration Technology For Equipment Software, Yuanyuan Wang, Yuxin Duan, Guangzhao Song
Journal of System Simulation
Abstract: The problems of the traditional distributed digital simulation system are analyzed. The method of constructing the digital simulation system based on the equipment software is studied. The configurable simulation integration middleware is designed. The overall structure of the configurable middleware is designed and the design ideas of each module are briefly summarized. An example of a digital simulation system with networked transformation of equipment shows that the middleware can be effectively and flexibly configured and can provide support for constructing digital simulation system with software, which can be promoted and used in other simulation systems.
Generalized Zero-Inflated Binomial Distribution Model Aimed At Air Quality Data Analysis,
2020
1.School of Mathematics and Computer,Tongling University,Tongling 244061,China; ;3.The University Key Laboratory of Intelligent Perception and Computing of Anhui Province,Anqing 246133,China; ;
Generalized Zero-Inflated Binomial Distribution Model Aimed At Air Quality Data Analysis, Benyue Su, Pengpeng Xu, Sheng Min
Journal of System Simulation
Abstract: For the problem of the quality monitoring and counting of excessive gas emissions in chemical industry parks, a generalized zero-inflated binomial distribution model is constructed. Statistics show that the times of number of excessive gas emissions has a typical zero-inflated feature. The traditional zero-inflated Poisson model and negative binomial regression model and so on will underestimate the probability of zero inflation. A generalized zero-inflated binomial distribution model is constructed by extending the traditional binomial regression model to a more general form. This model satisfies the characteristic that the expectation is less than the variance, and better solves the problems …
Hyper-Heuristic De Algorithm For Solving Zero-Wait Fermentation Process Schedulinge,
2020
Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Wuxi 214122,China;
Hyper-Heuristic De Algorithm For Solving Zero-Wait Fermentation Process Schedulinge, Shen Peng, Wang Yan, Zhicheng Ji, Jianhua Zhang
Journal of System Simulation
Abstract: A class of zero-wait fermentation process scheduling issues with batch process characteristics are researched. In order to solve the problem of easy deterioration in the process, a super heuristic difference algorithm is proposed, and the maximum makespan is minimized as the optimization goal. The algorithm is divided into two layers. The upper layer is an improved adaptive differential evolution algorithm to select and sort the heuristic operations in lower layer. The lower layer is combined and sorted into a new algorithm to operate on the problem domain, adding simulated annealing algorithm to avoid falling into local optimization. The method …
Modeling And Relevance Analysis Of Urban Epidemic Transmission And Work Resumption Intensity,
2020
1.School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 610031,China; ;2.National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Southwest Jiaotong University,Chengdu 610031,China; ;3.National United laboratory of Comprehensive Transportation,Southwest Jiaotong University,Chengdu 610031,China;
Modeling And Relevance Analysis Of Urban Epidemic Transmission And Work Resumption Intensity, Gan Mi, Yunyi Tian, Wenchang Zhang, Xihan Zhao
Journal of System Simulation
Abstract: On the basis of multi-source big data, the model for analyzing the population migration changes and manpower gaps caused by the COVID-19 epidemic in 34 typical cities across the country is constructed , and the work resumption intensity of other cities is predicited by using the migration base constructed. The SEIR model is used to estimate the basic reproduction number in each city since the simulate results show that it can emulate the transmission trend of this epidemic accurately, and the retrospective matrix analysis of the work resumption intensity is combined with the manpower gap to summarize the anti-epidemic …
Research On Dynamic Flexible Job Shop Scheduling Problem Based On Dynamic Interaction Layer,
2020
Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Wuxi 214122,China;
Research On Dynamic Flexible Job Shop Scheduling Problem Based On Dynamic Interaction Layer, Zhang Xiang, Wang Yan, Zhicheng Ji
Journal of System Simulation
Abstract: In order to quickly response to the unforeseen circumstances in flexible job shop, a dynamic flexible job shop scheduling model is constructed, which takes the overall production time and the completion time of emergency orders as the optimization objectives. For the model, a dynamic interaction layer (DIL) model, which has a better performance on DFJSP, is proposed to replace the scroll window. Particle swarm genetic hybrid algorithm (PSGA) is designed to combine the particle swarm optimization algorithm with the genetic algorithm to enhance the ability of local search. Aiming at the unexpected urgent orders in flexible job shop, …
