Fixed Point Acceleration In R,
2019
University of Oxford
Fixed Point Acceleration In R, Stuart Baumann, Margaryta Klymak
The R Journal
t A fixed point problem is one where we seek a vector, X, for a function, f, such that f(X) = X. The solution of many such problems can be accelerated by using a fixed point acceleration algorithm. With the release of the FixedPoint package there is now a number of algorithms available in R that can be used for accelerating the finding of a fixed point of a function. These algorithms include Newton acceleration, Aitken acceleration and Anderson acceleration as well as epsilon extrapolation methods and minimal polynomial methods. This paper demonstrates the use of fixed point accelerators in …
Swgee: An R Package For Analyzing Longitudinal Data With Response Missingness And Covariate Measurement Error,
2019
Shenzhen University
Swgee: An R Package For Analyzing Longitudinal Data With Response Missingness And Covariate Measurement Error, Juan Xiong, Grace Y. Yi
The R Journal
Though longitudinal data often contain missing responses and error-prone covariates, relatively little work has been available to simultaneously correct for the effects of response missingness and covariate measurement error on analysis of longitudinal data. Yi (2008) proposed a simulation based marginal method to adjust for the bias induced by measurement error in covariates as well as by missingness in response. The proposed method focuses on modeling the marginal mean and variance structures, and the missing at random mechanism is assumed. Furthermore, the distribution of covariates are left unspecified. These features make the proposed method applicable to a broad settings. In …
Title: Ipwerrory: An R Package For Estimation Of Average Treatment Effect With Misclassified Binary Outcome,
2019
University of Waterloo
Title: Ipwerrory: An R Package For Estimation Of Average Treatment Effect With Misclassified Binary Outcome, Di Shu, Grace Y. Yi
The R Journal
It has been well documented that ignoring measurement error may result in severely biased inference results. In recent years, there has been limited but increasing research on causal inference with measurement error. In the presence of misclassified binary outcome variable, Shu and Yi (2017) considered the inverse probability weighted estimation of the average treatment effect and proposed valid estimation methods to correct for misclassification effects for various settings. To expedite the application of those methods for situations where misclassification in the binary outcome variable is a real concern, we implement correction methods proposed by Shu and Yi (2017) and develop …
Ciuupi: An R Package For Computing Confidence Intervals That Utilize Uncertain Prior Information,
2019
The University of Melbourne
Ciuupi: An R Package For Computing Confidence Intervals That Utilize Uncertain Prior Information, Mainzer Kabaila, Paul Kabaila
The R Journal
We have created the R package ciuupi to compute confidence intervals that utilize uncertain prior information in linear regression. Unlike post-model-selection confidence intervals, the confidence interval that utilizes uncertain prior information (CIUUPI) implemented in this package has, to an excellent approximation, coverage probability throughout the parameter space that is very close to the desired minimum coverage probability. Furthermore, when the uncertain prior information is correct, the CIUUPI is, on average, shorter than the standard confidence interval constructed using the full linear regression model. In this paper we provide motivating examples of scenarios where the CIUUPI may be used. We then …
Robustgasp: Robust Gaussian Stochastic Process Emulation In R,
2019
University of California, Santa Barbara
Robustgasp: Robust Gaussian Stochastic Process Emulation In R, Mengyang Gu, Jesus Palomo, James O. Berger
The R Journal
Gaussian stochastic process (GaSP) emulation is a powerful tool for approximating computationally intensive computer models. However, estimation of parameters in the GaSP emulator is a challenging task. No closed-form estimator is available and many numerical problems arise with standard estimates, e.g., the maximum likelihood estimator. In this package, we implement a marginal posterior mode estimator, for special priors and parameterizations. This estimation method that meets the robust parameter estimation criteria was discussed in Gu et al. (2018); mathematical reasons are provided therein to explain why robust parameter estimation can greatly improve predictive performance of the emulator. In addition, inert inputs …
Mixedsde: A Package To Fit Mixed Stochastic Differential Equations,
2019
Université Grenoble Alpes, Université Paris Descartes 45, Université Pierre et Marie Curie 4
Mixedsde: A Package To Fit Mixed Stochastic Differential Equations, Charlotte Dion, Simone Hermann, Adeline Samson
The R Journal
Stochastic differential equations (SDEs) are useful to model continuous stochastic processes. When (independent) repeated temporal data are available, variability between the trajectories can be modeled by introducing random effects in the drift of the SDEs. These models are useful to analyze neuronal data, crack length data, pharmacokinetics, financial data, to cite some applications among other. The R package focuses on the estimation of SDEs with linear random effects in the drift. The goal is to estimate the common density of the random effects from repeated discrete observations of the SDE. The package mixedsde proposes three estimation methods: a Bayesian parametric, …
Atable: Create Tables For Clinical Trial Reports,
2019
German Center for Neurodegenerative Diseases (DZNE)
Atable: Create Tables For Clinical Trial Reports, Armin Ströbel
The R Journal
Examining distributions of variables is the first step in the analysis of a clinical trial before more specific modelling can begin. Reporting these results to stakeholders of the trial is an essential part of a statistician’s work. The atable package facilitates these steps by offering easy-to-use but still flexible functions.
A Probabilistic Model Of The Bitcoin Blockchain,
2019
Singapore Management University
A Probabilistic Model Of The Bitcoin Blockchain, Marc Jourdan, Sebastien Blandin, Laura Wynter, Pralhad Deshpande
Research Collection School Of Computing and Information Systems
The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity. In this work, we introduce a complete probabilistic model of the Bitcoin Blockchain, setting the basis for follow-up AI applications on Bitcoin transactions. We first formulate a set of conditional dependencies induced by the Bitcoin protocol at the block level and derive a corresponding fully observed graphical model of a Bitcoin block. We then extend the model to include hidden entity attributes such as the functional category of the associated logical agent and derive asymptotic bounds on the privacy properties …
Classifying Challenging Behaviors In Autism Spectrum Disorder With Neural Document Embeddings,
2019
Chapman University
Classifying Challenging Behaviors In Autism Spectrum Disorder With Neural Document Embeddings, Abigail Atchison
Computational and Data Sciences (MS) Theses
The understanding and treatment of challenging behaviors in individuals with Autism Spectrum Disorder is paramount to enabling the success of behavioral therapy; an essential step in this process being the labeling of challenging behaviors demonstrated in therapy sessions. These manifestations differ across individuals and within individuals over time and thus, the appropriate classification of a challenging behavior when considering purely qualitative factors can be unclear. In this thesis we seek to add quantitative depth to this otherwise qualitative task of challenging behavior classification. We do so through the application of natural language processing techniques to behavioral descriptions extracted from the …
Arecibo Message,
2019
CUNY La Guardia Community College
Arecibo Message, Joshua P. Tan
Open Educational Resources
This two week assignment asks students to interpret and analyze the 1974 Arecibo Message sent by Drake and Sagan. Week 1 introduces the concepts behind the construction of the message and engages with a critical analysis of the architecture and the contents of the message. Week 2 asks students to develop software in a Jupyter Notebook (available for free from the Anaconda Python Distribution) to interpret messages that were similar to those produced by Drake and Sagan.
Real-Time Rfi Mitigation In Radio Astronomy,
2019
Washington University in St. Louis
Real-Time Rfi Mitigation In Radio Astronomy, Emily Ramey, Nick Joslyn, Richard Prestage, Michael Lam, Luke Hawkins, Tim Blattner, Mark Whitehead
Senior Honors Papers / Undergraduate Theses
As the use of wireless technology has increased around the world, Radio Frequency Interference (RFI) has become more and more of a problem for radio astronomers. Preventative measures exist to limit the presence of RFI, and programs exist to remove it from saved data, but the use of algorithms to detect and remove RFI as an observation is occurring is much less common. Such a method would be incredibly useful for observations in which the data must undergo several rounds of processing before being saved, as in pulsar timing studies. Strategies for real-time mitigation have been discussed and tested with …
The Fluid Representations Of Networks Estimating Liquid Viscosity,
2019
NTT Communication Science Laboratories
The Fluid Representations Of Networks Estimating Liquid Viscosity, Jan Jaap R. Van Assen, Shin'ya Nishida, Roland W. Fleming
MODVIS Workshop
No abstract provided.
Simplicity Diffexpress: A Bespoke Cloud-Based Interface For Rna-Seq Differential Expression Modeling And Analysis,
2019
NSilico Life Science Ltd. and School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
Simplicity Diffexpress: A Bespoke Cloud-Based Interface For Rna-Seq Differential Expression Modeling And Analysis, Cintia C. Palu, Marcelo Ribeiro-Alves, Yanxin Wu, Brendan Lawlor, Pavel V. Baranov, Brian Kelly, Paul Walsh
Department of Computer Science Publications
One of the key challenges for transcriptomics-based research is not only the processing of large data but also modeling the complexity of features that are sources of variation across samples, which is required for an accurate statistical analysis. Therefore, our goal is to foster access for wet lab researchers to bioinformatics tools, in order to enhance their ability to explore biological aspects and validate hypotheses with robust analysis. In this context, user-friendly interfaces can enable researchers to apply computational biology methods without requiring bioinformatics expertise. Such bespoke platforms can improve the quality of the findings by allowing the researcher to …
Fluid Transport In Porous Media For Engineering Applications,
2019
University of New Mexico
Fluid Transport In Porous Media For Engineering Applications, Eric M. Benner
Chemical and Biological Engineering ETDs
This doctoral dissertation presents three topics in modeling fluid transport through porous media used in engineering applications. The results provide insights into the design of fuel cell components, catalyst and drug delivery particles, and aluminum- based materials. Analytical and computational methods are utilized for the modeling of the systems of interest. Theoretical analysis of capillary-driven transport in porous media show that both geometric and evaporation effects significantly change the time dependent behavior of liquid imbibition and give a steady state flux into the medium. The evaporation–capillary number is significant in determining the time-dependent behavior of capillary flows in porous media. …
Depressiongnn: Depression Prediction Using Graph Neural Network On Smartphone And Wearable Sensors,
2019
University of Connecticut
Depressiongnn: Depression Prediction Using Graph Neural Network On Smartphone And Wearable Sensors, Param Bidja
Honors Scholar Theses
Depression prediction is a complicated classification problem because depression diagnosis involves many different social, physical, and mental signals. Traditional classification algorithms can only reach an accuracy of no more than 70% given the complexities of depression. However, a novel approach using Graph Neural Networks (GNN) can be used to reach over 80% accuracy, if a graph can represent the depression data set to capture differentiating features. Building such a graph requires 1) the definition of node features, which must be highly correlated with depression, and 2) the definition for edge metrics, which must also be highly correlated with depression. In …
The Effects Of Finite Precision On The Simulation Of The Double Pendulum,
2019
James Madison University
The Effects Of Finite Precision On The Simulation Of The Double Pendulum, Rebecca Wild
Senior Honors Projects, 2010-2019
We use mathematics to study physical problems because abstracting the information allows us to better analyze what could happen given any range and combination of parameters. The problem is that for complicated systems mathematical analysis becomes extremely cumbersome. The only effective and reasonable way to study the behavior of such systems is to simulate the event on a computer. However, the fact that the set of floating-point numbers is finite and the fact that they are unevenly distributed over the real number line raises a number of concerns when trying to simulate systems with chaotic behavior. In this research we …
Modeling A Chaotic Billiard: The Bunimovich Stadium,
2019
James Madison University
Modeling A Chaotic Billiard: The Bunimovich Stadium, Randal Shoemaker
Senior Honors Projects, 2010-2019
The Bunimovich stadium is a chaotic dynamical system in which a single particle, known as a billiard, moves indefinitely within a barrier without loss of momentum. Mathematicians and physicists have been interested in its properties since it was discovered to be chaotic in the 1970’s [5] [3] [4]. The Bunimovich stadium is actively researched [9]. This thesis and its accompanying software, the Bunimovich Stadia Evolution Viewer (BSEV), present a novel visual representation of the the chaotic dynamical system. The goal for the software is to provide insights into the stadium’s properties to aid researchers. This tool allows one to visualize …
Visualization And Machine Learning Techniques For Nasa’S Em-1 Big Data Problem,
2019
Southern Methodist University
Visualization And Machine Learning Techniques For Nasa’S Em-1 Big Data Problem, Antonio P. Garza Iii, Jose Quinonez, Misael Santana, Nibhrat Lohia
SMU Data Science Review
In this paper, we help NASA solve three Exploration Mission-1 (EM-1) challenges: data storage, computation time, and visualization of complex data. NASA is studying one year of trajectory data to determine available launch opportunities (about 90TBs of data). We improve data storage by introducing a cloud-based solution that provides elasticity and server upgrades. This migration will save $120k in infrastructure costs every four years, and potentially avoid schedule slips. Additionally, it increases computational efficiency by 125%. We further enhance computation via machine learning techniques that use the classic orbital elements to predict valid trajectories. Our machine learning model decreases trajectory …
Automate Nuclei Detection Using Neural Networks,
2019
Southern Methodist University
Automate Nuclei Detection Using Neural Networks, Jonathan Flores, Thejas Prasad, Jordan Kassof, Robert Slater
SMU Data Science Review
Nuclei identification is a pivotal first step in many areas of biomedical research. Pathologists often observe images containing microscopic nuclei as part of their day to day jobs. During research, pathologists must identify nuclei characteristics from microscopic images such as: volume of nuclei, size, density and individual position within image. The pathology field can benefit from image detection enhancements done through the use of computer image segmentation techniques. This research presents methods that can be used to identify all the cell nuclei contained in images. Multiple techniques were experimented with such as edge detection and Convolutional Neural Networks with U-Net …
Multimodal Review Generation For Recommender Systems,
2019
Singapore Management University
Multimodal Review Generation For Recommender Systems, Quoc Tuan Truong, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Key to recommender systems is learning user preferences, which are expressed through various modalities. In online reviews, for instance, this manifests in numerical rating, textual content, as well as visual images. In this work, we hypothesize that modelling these modalities jointly would result in a more holistic representation of a review towards more accurate recommendations. Therefore, we propose Multimodal Review Generation (MRG), a neural approach that simultaneously models a rating prediction component and a review text generation component. We hypothesize that the shared user and item representations would augment the rating prediction with richer information from review text, while sensitizing …
