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Articles 68941 - 68970 of 294733

Full-Text Articles in Physical Sciences and Mathematics

Sedimentary Dna And Molecular Evidence For Early Human Occupation Of The Faroe Islands, Lorelei Curtin, William J. D’Andrea, Nicholas L. Balascio, Sabrina Shirazi, Beth Shapiro, Gregory A. De Wet, Raymond S. Bradley, Jostein Bakke Dec 2021

Sedimentary Dna And Molecular Evidence For Early Human Occupation Of The Faroe Islands, Lorelei Curtin, William J. D’Andrea, Nicholas L. Balascio, Sabrina Shirazi, Beth Shapiro, Gregory A. De Wet, Raymond S. Bradley, Jostein Bakke

Geosciences: Faculty Publications

The Faroe Islands, a North Atlantic archipelago between Norway and Iceland, were settled by Viking explorers in the mid-9th century CE. However, several indirect lines of evidence suggest earlier occupation of the Faroes by people from the British Isles. Here, we present sedimentary ancient DNA and molecular fecal biomarker evidence from a lake sediment core proximal to a prominent archaeological site in the Faroe Islands to establish the earliest date for the arrival of people in the watershed. Our results reveal an increase in fecal biomarker concentrations and the first appearance of sheep DNA at 500 CE (95% confidence interval …

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Insights Into The Origin Of The Invasive Populations Of Trioza Erytreae In Europe Using Microsatellite Markers And Mtdna Barcoding Approaches, Omar Ruíz-Rivero, Andrés Garcia-Lor, Borja Rojas-Panadero, José Carlos Franco, Fathiya M. Khamis, Kerstin Kruger, Dina Cifuentes, Pablo Bielza, Alejandro Tena, Alberto Urbaneja, Meritxell Pérez-Hedo Dec 2021

Insights Into The Origin Of The Invasive Populations Of Trioza Erytreae In Europe Using Microsatellite Markers And Mtdna Barcoding Approaches, Omar Ruíz-Rivero, Andrés Garcia-Lor, Borja Rojas-Panadero, José Carlos Franco, Fathiya M. Khamis, Kerstin Kruger, Dina Cifuentes, Pablo Bielza, Alejandro Tena, Alberto Urbaneja, Meritxell Pérez-Hedo

All Peer-Reviewed Publications

The African citrus psyllid Trioza erytreae is one of the major threats to citrus industry as the vector of the incurable disease known as huanglongbing (HLB) or citrus greening. The psyllid invaded the northwest of the Iberian Peninsula 6 years ago. The invasion alarmed citrus growers in the Mediterranean basin, the largest citrus producing area in Europe, which is still free of HLB. Before our study, no research had been carried out on the genetic diversity of T. erytreae populations that have invaded the Iberian Peninsula and the archipelagos of the Macaronesia (Madeira and the Canary Islands). In this study, …

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Pest Interceptions On Imported Fresh Fruits Into South Africa, Livhuwani R. Nnzeru, Phumudzo P. Tshikhudo, Bester T. Mudereri, Moleseng C. Moshobane Dec 2021

Pest Interceptions On Imported Fresh Fruits Into South Africa, Livhuwani R. Nnzeru, Phumudzo P. Tshikhudo, Bester T. Mudereri, Moleseng C. Moshobane

All Peer-Reviewed Publications

Global trade through imports and exports of commodities brings an excessive risk of accidental introduction of invasive alien species in many countries. South Africa imports a range of fresh fruits and other commodities to complement its local production. Despite the substantial impacts of these invasive alien species, relatively little is known about the pathways and origins by which these organisms arrive in South Africa. In this study, we analyzed the susceptibility of South Africa to introduced plant-feeding pests detected from imported consignments of fresh fruits into the country over 10 years between 2009 and 2018. The analysis was conducted from …

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Models Versus Datasets: Reducing Bias Through Building A Comprehensive Ids Benchmark, Rasheed Ahmad, Izzat Alsmadi, Wasim Alhamdani, Lo'ai A. Tawalbeh Dec 2021

Models Versus Datasets: Reducing Bias Through Building A Comprehensive Ids Benchmark, Rasheed Ahmad, Izzat Alsmadi, Wasim Alhamdani, Lo'ai A. Tawalbeh

Computer Science Faculty Publications (Archived)

Today, deep learning approaches are widely used to build Intrusion Detection Systems for securing IoT environments. However, the models’ hidden and complex nature raises various concerns, such as trusting the model output and understanding why the model made certain decisions. Researchers generally publish their proposed model’s settings and performance results based on a specific dataset and a classification model but do not report the proposed model’s output and findings. Similarly, many researchers suggest an IDS solution by focusing only on a single benchmark dataset and classifier. Such solutions are prone to generating inaccurate and biased results. This paper overcomes these …

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Pfoa Loaded Gac Fall 2021 Report, Ramiro Nava Dec 2021

Pfoa Loaded Gac Fall 2021 Report, Ramiro Nava

Water Resources Science and Technology Theses and Graduate Research Reports (Archived)

No abstract provided.

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Efficacy Of Sediment Contaminant Remediation Of The Benthos In A Segment Of The Southern Branch Of The Elizabeth River, Colton Martin Dec 2021

Efficacy Of Sediment Contaminant Remediation Of The Benthos In A Segment Of The Southern Branch Of The Elizabeth River, Colton Martin

Biological Sciences Theses & Dissertations

The bottom sediment of the Southern Branch of the Elizabeth River, a tributary of Chesapeake Bay, was historically contaminated with hydrocarbons from industrial sources especially wood treatment facilities. The Elizabeth River Project selected a section of the bottom off Money Point in the Southern Branch for a sediment contaminant remediation effort. Prior to initiation of remediation efforts, a survey occurred in summer 2010 to characterize the ecological condition of the benthic communities off Money Point compared to benthic communities of a benthic region across the channel and northwest of Money Point near Blows Creek. That study characterized the benthos of …

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Fast Reconfiguration Of Robot Swarms With Uniform Control Signals, David Caballero, Angel A. Cantu, Timothy Gomez, Austin Luchsinger, Robert Schweller, Tim Wylie Dec 2021

Fast Reconfiguration Of Robot Swarms With Uniform Control Signals, David Caballero, Angel A. Cantu, Timothy Gomez, Austin Luchsinger, Robert Schweller, Tim Wylie

Computer Science Faculty Publications

This paper investigates a simplified model of robot motion planning where particles on a board respond to global signals, causing them to move uniformly in a particular direction. We consider two types of movement commands: (1) Steps, which cause particles to move one unit distance in the given direction, and (2) Tilts, which cause particles to move maximally in the given direction. Under the overarching theme of reconfiguring robot swarms, we look at the problem of assembling general shapes both within systems that exclusively use step commands and systems that exclusively use tilt commands. We derive upper and …

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Explainable Transfer-Learning And Knowledge Distillation For Fast And Accurate Head-Pose Estimation, Nima Aghli Dec 2021

Explainable Transfer-Learning And Knowledge Distillation For Fast And Accurate Head-Pose Estimation, Nima Aghli

Theses and Dissertations

Head-pose estimation from facial images is an important research topic in computer-vision. It has many applications in detecting the focus of attention, monitoring driver behavior, and human-computer interaction. As with other computer-vision topics, recent research on head-pose estimation has been focused on using deep convolutional neural networks (CNNs). Although deeper networks improve prediction accuracy, they suffer from dependency on expensive hardware such as GPUs to perform real-time inference. As a result, CNN model compression becomes an important concept. In this work, we propose a novel CNN compression method by combing weight pruning and knowledge distillation. Additionally, we improve the state-of-the-art …

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Equivalence: A Covariantly Constant Problem In General Relativity, Jaren Hobbs Dec 2021

Equivalence: A Covariantly Constant Problem In General Relativity, Jaren Hobbs

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

In studying the space-time structures described by Einstein’s theory of general relativity, it is often useful to identify particular properties referred to as geometrical invariants. These are attributes of the space-times which do not change regardless of the underlying coordinate systems used to study them. This project is part of a larger effort to catalogue space-times studied in general relativity. Specifically, computational software was used to identify structures known as covariantly constant vector fields.

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Development And Optimization Of A Denitrification System To Promote Biological Nutrient Removal In A Subtropical Estuary, Abigail Gering Dec 2021

Development And Optimization Of A Denitrification System To Promote Biological Nutrient Removal In A Subtropical Estuary, Abigail Gering

Theses and Dissertations

Eutrophication impacts coastal ecosystems globally and triggers phytoplankton and algal blooms, leading to an increase in oxygen demand in the water column and sediments due to various biogeochemical processes. Naturally, biological nitrification, denitrification and anammox processes help to remove nitrogen predominately by nitrifying and denitrifying bacteria in the sediments. Eutrophic systems have increased nutrient and organic matter loading and are prone to the accumulation of ‘muddy’, fine-grained, organic-rich sediments (locally termed ‘muck’) that may not be able to support high enough populations of these bacteria, and therefore, may not adequately support nitrification/denitrification processes that would otherwise remove nitrogen. To decrease …

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An Assessment Of Image-Cloaking Techniques Against Automated Face Recognition For Biometric Privacy, Brandon Scott Ledford Dec 2021

An Assessment Of Image-Cloaking Techniques Against Automated Face Recognition For Biometric Privacy, Brandon Scott Ledford

Theses and Dissertations

Over the past two decades, Americans have aggressively increased the amount of facial data uploaded to the internet primarily via social media. This data is largely unprotected due to the dire lack of existing regulations protecting users from large scale face recognition in the United States, where the value of data trade is in the tens of billions. In its current state, facial privacy in the United States depends on American corporations opting not to collect the public data, an option rarely chosen. Much research has been done in the area of suppressing recognition abilities, giving users the ability to …

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Unveiling Migration Patterns Using Data And Network Science, Firas Aswad Dec 2021

Unveiling Migration Patterns Using Data And Network Science, Firas Aswad

Theses and Dissertations

Many real-world phenomena have recently been witnessed, motivating scientists to provide a comprehensive understanding of worldwide networks, such as the human migration phenomenon. Human migration research is multidisciplinary and has yielded work in social sciences, physics, and the new field of smart city designing and planning based on big data analytics. The importance of the research comes from the impacts on both countries, sources, and distention. Impacts occur at several levels such as economy, city planning, politics, and law enforcement, leading to changes in demographics. Thus, studies on human migration help policymakers prepare and handle crises when they occur in …

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News From The Bioconductor Project, Bioconductor Core Team Dec 2021

News From The Bioconductor Project, Bioconductor Core Team

The R Journal

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor 3.14 was released on 27 October, 2021. It is compatible with R 4.1.0 and consists of 2083 software packages, 408 experiment data packages, 904 up-to-date annotation packages, and 29 workflows.

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Rpese: Risk And Performance Estimators Standard Errors With Serially Dependent Data, Anthony-Alexander Christidis, R Douglas Martin Dec 2021

Rpese: Risk And Performance Estimators Standard Errors With Serially Dependent Data, Anthony-Alexander Christidis, R Douglas Martin

The R Journal

The R package RPESE (Risk and Performance Estimators Standard Errors) implements a new method for computing accurate standard errors of risk and performance estimators when returns are serially dependent. The new method makes use of the representation of a risk or performance estimator as a summation of a time series of influence-function (IF) transformed returns, and computes estimator standard errors using a sophisticated method of estimating the spectral density at frequency zero of the time series of IF-transformed returns. Two additional packages used by RPESE are introduced, namely RPEIF which computes and provides graphical displays of the IF of risk …

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Visual Diagnostics For Constrained Optimisation With Application To Guided Tours, H Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez Dec 2021

Visual Diagnostics For Constrained Optimisation With Application To Guided Tours, H Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez

The R Journal

A guided tour helps to visualise high-dimensional data by showing low-dimensional projections along a projection pursuit optimisation path. Projection pursuit is a generalisation of principal component analysis in the sense that different indexes are used to define the interestingness of the projected data. While much work has been done in developing new indexes in the literature, less has been done on understanding the optimisation. Index functions can be noisy, might have multiple local maxima as well as an optimal maximum, and are constrained to generate orthonormal projection frames, which complicates the optimization. In addition, projection pursuit is primarily used for …

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Bssm: Bayesian Inference Of Non-Linear And Non-Gaussian State Space Models In R, Jouni Helske, Matti Vihola Dec 2021

Bssm: Bayesian Inference Of Non-Linear And Non-Gaussian State Space Models In R, Jouni Helske, Matti Vihola

The R Journal

We present an R package bssm for Bayesian non-linear/non-Gaussian state space modeling. Unlike the existing packages, bssm allows for easy-to-use approximate inference based on Gaussian approximations such as the Laplace approximation and the extended Kalman filter. The package also accommodates discretely observed latent diffusion processes. The inference is based on fully automatic, adaptive Markov chain Monte Carlo (MCMC) on the hyperparameters, with optional importance sampling post-correction to eliminate any approximation bias. The package also implements a direct pseudo-marginal MCMC and a delayed acceptance pseudo-marginal MCMC using intermediate approximations. The package offers an easy-to-use interface to define models with linear-Gaussian state …

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Passed: Calculate Power And Sample Size For Two Sample Tests, Jinpu Li, Ryan .. Knigge, Kaiyi Chen, Emily V. Leary Dec 2021

Passed: Calculate Power And Sample Size For Two Sample Tests, Jinpu Li, Ryan .. Knigge, Kaiyi Chen, Emily V. Leary

The R Journal

Power and sample size estimation are critical aspects of study design to demonstrate minimized risk for subjects and justify the allocation of time, money, and other resources. Researchers often work with response variables that take the form of various distributions. Here, we present an R package, PASSED, that allows flexibility with seven common distributions and multiple options to accommodate sample size or power analysis. The relevant statistical theory, calculations, and examples for each distribution using PASSED are discussed in this paper.

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Automatic Time Series Forecasting With Ata Method In R: Ataforecasting Package, Ali Sabri Taylan, Güçkan Yapar, Hanife Taylan Selamlar Dec 2021

Automatic Time Series Forecasting With Ata Method In R: Ataforecasting Package, Ali Sabri Taylan, Güçkan Yapar, Hanife Taylan Selamlar

The R Journal

Ata method is a new univariate time series forecasting method that provides innovative solutions to issues faced during the initialization and optimization stages of existing methods. The Ata method’s forecasting performance is superior to existing methods in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or deseasonalized time series, where the deseasonalization can be performed via any preferred decomposition method. The R package ATAforecasting was developed as a comprehensive toolkit for automatic time series forecasting. It focuses on modeling all types of time series components with any preferred Ata methods and handling seasonality patterns by …

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Robustbf: An R Package For Robust Solution To The Behrens-Fisher Problem, Gamze Güven, ŞÜkrü Acıtaş, Hatice ŞAmkar, Birdal ŞEnoğLu Dec 2021

Robustbf: An R Package For Robust Solution To The Behrens-Fisher Problem, Gamze Güven, ŞÜkrü Acıtaş, Hatice ŞAmkar, Birdal ŞEnoğLu

The R Journal

Welch’s two-sample t-test based on least squares (LS) estimators is generally used to test the equality of two normal means when the variances are not equal. However, this test loses its power when the underlying distribution is not normal. In this paper, two different tests are proposed to test the equality of two long-tailed symmetric (LTS) means under heterogeneous variances. Adaptive modified maximum likelihood (AMML) estimators are used in developing the proposed tests since they are highly efficient under LTS distribution. An R package called RobustBF is given to show the implementation of these tests. Simulated Type I error rates …

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Volesti: Volume Approximation And Sampling For Convex Polytopes In R, Apostolos Chalkis, Vissarion Fisikopoulos Dec 2021

Volesti: Volume Approximation And Sampling For Convex Polytopes In R, Apostolos Chalkis, Vissarion Fisikopoulos

The R Journal

Sampling from high-dimensional distributions and volume approximation of convex bodies are fundamental operations that appear in optimization, finance, engineering, artificial intelligence, and machine learning. In this paper, we present volesti, an R package that provides efficient, scalable algorithms for volume estimation, uniform, and Gaussian sampling from convex polytopes. volesti scales to hundreds of dimensions, handles efficiently three different types of polyhedra and provides non existing sampling routines to R. We demonstrate the power of volesti by solving several challenging problems using the R language.

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Drhotnet: An R Package For Detecting Differential Risk Hotspots On A Linear Network, Álvaro Briz-Redón, Francisco Martínez-Ruiz, Francisco Montes Dec 2021

Drhotnet: An R Package For Detecting Differential Risk Hotspots On A Linear Network, Álvaro Briz-Redón, Francisco Martínez-Ruiz, Francisco Montes

The R Journal

One of the most common applications of spatial data analysis is detecting zones, at a certain scale, where a point-referenced event under study is especially concentrated. The detection of such zones, which are usually referred to as hotspots, is essential in certain fields such as criminology, epidemiology, or traffic safety. Traditionally, hotspot detection procedures have been developed over areal units of analysis. Although working at this spatial scale can be suitable enough for many research or practical purposes, detecting hotspots at a more accurate level (for instance, at the road segment level) may be more convenient sometimes. Furthermore, it is …

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Maint.Data: Modelling And Analysing Interval Data In R, A Pedro Duarte Silva, Paula Brito, Peter Filzmoser, José G. Dias Dec 2021

Maint.Data: Modelling And Analysing Interval Data In R, A Pedro Duarte Silva, Paula Brito, Peter Filzmoser, José G. Dias

The R Journal

We present the CRAN R package MAINT.Data for the modelling and analysis of multivariate interval data, i.e., where units are described by variables whose values are intervals of R, representing intrinsic variability. Parametric inference methodologies based on probabilistic models for interval variables have been developed, where each interval is represented by its midpoint and log-range, for which multivariate Normal and Skew-Normal distributions are assumed. The intrinsic nature of the interval variables leads to special structures of the variance-covariance matrix, which are represented by four different possible configurations. MAINT.Data implements the proposed methodologies in the S4 object system, introducing a …

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Matchthem: Matching And Weighting After Multiple Imputation, Farhad Pishgar, Noah Greifer, Clémence Leyrat, Elizabeth Stuart Dec 2021

Matchthem: Matching And Weighting After Multiple Imputation, Farhad Pishgar, Noah Greifer, Clémence Leyrat, Elizabeth Stuart

The R Journal

Balancing the distributions of the confounders across the exposure levels in an observational study through matching or weighting is an accepted method to control for confounding due to these variables when estimating the association between an exposure and outcome and reducing the degree of dependence on certain modeling assumptions. Despite the increasing popularity in practice, these procedures cannot be immediately applied to datasets with missing values. Multiple imputation of the missing data is a popular approach to account for missing values while preserving the number of units in the dataset and accounting for the uncertainty in the missing values. However, …

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Diproperm: An R Package For The Diproperm Test, Andrew G. Allmon, J.S. Marron, Michael G. Hudgens Dec 2021

Diproperm: An R Package For The Diproperm Test, Andrew G. Allmon, J.S. Marron, Michael G. Hudgens

The R Journal

High-dimensional low sample size (HDLSS) data sets frequently emerge in many biomedical applications. The direction-projection-permutation (DiProPerm) test is a two-sample hypothesis test for comparing two high-dimensional distributions. The DiProPerm test is exact, i.e., the type I error is guaranteed to be controlled at the nominal level for any sample size, and thus is applicable in the HDLSS setting. This paper discusses the key components of the DiProPerm test, introduces the diproperm R package, and demonstrates the package on a real-world data set

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Bayessenmc: An R Package For Bayesian Sensitivity Analysis Of Misclassification, Jinhui Yang, Lifeng Lin, Haitao Chu Dec 2021

Bayessenmc: An R Package For Bayesian Sensitivity Analysis Of Misclassification, Jinhui Yang, Lifeng Lin, Haitao Chu

The R Journal

In case–control studies, the odds ratio is commonly used to summarize the association between a binary exposure and a dichotomous outcome. However, exposure misclassification frequently appears in case–control studies due to inaccurate data reporting, which can produce bias in measures of association. In this article, we implement a Bayesian sensitivity analysis of misclassification to provide a full posterior inference on the corrected odds ratio under both non-differential and differential misclassification. We present an R (R Core Team, 2018) package BayesSenMC, which provides user-friendly functions for its implementation. The usage is illustrated by a real data analysis on the association between …

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Passo: An R Package For Assessing Partial Association Between Ordinal Variables, Shaobo Li, Xiaorui Zhu, Yuejie Chen, Dungang Liu Dec 2021

Passo: An R Package For Assessing Partial Association Between Ordinal Variables, Shaobo Li, Xiaorui Zhu, Yuejie Chen, Dungang Liu

The R Journal

Partial association, the dependency between variables after adjusting for a set of covariates, is an important statistical notion for scientific research. However, if the variables of interest are ordered categorical data, the development of statistical methods and software for assessing their partial association is limited. Following the framework established by Liu et al. (2021), we develop an R package PAsso for assessing Partial Associations between ordinal variables. The package provides various functions that allow users to perform a wide spectrum of assessments, including quantification, visualization, and hypothesis testing. In this paper, we discuss the implementation of PAsso in …

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Ngsseml: Non-Gaussian State Space With Exact Marginal Likelihood, Thiago R. Santos, Glaura C. Franco, Dani Gamerman Dec 2021

Ngsseml: Non-Gaussian State Space With Exact Marginal Likelihood, Thiago R. Santos, Glaura C. Franco, Dani Gamerman

The R Journal

The number of packages/software for Gaussian State Space models has increased over recent decades. However, there are very few codes available for non-Gaussian State Space (NGSS) models due to analytical intractability that prevents exact calculations. One of the few tractable exceptions is the family of NGSS with exact marginal likelihood, named NGSSEML. In this work, we present the wide range of data formats and distributions handled by NGSSEML and a package in the R language to perform classical and Bayesian inference for them. Special functions for filtering, forecasting, and smoothing procedures and the exact calculation of the marginal likelihood function …

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Dad: An R Package For Visualisation, Classification And Discrimination Of Multivariate Groups Modelled By Their Densities, Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Sabine Demotes-Mainard Dec 2021

Dad: An R Package For Visualisation, Classification And Discrimination Of Multivariate Groups Modelled By Their Densities, Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Sabine Demotes-Mainard

The R Journal

Multidimensional scaling (MDS), hierarchical cluster analysis (HCA), and discriminant analysis (DA) are classical techniques which deal with data made of n individuals and p variables. When the individuals are divided into T groups, the R package dad associates with each group a multivariate probability density function and then carries out these techniques on the densities, which are estimated by the data under consideration. These techniques are based on distance measures between densities: chi-square, Hellinger, Jeffreys, Jensen-Shannon, and Lp for discrete densities, Hellinger , Jeffreys, L2 , and 2-Wasserstein for Gaussian densities, and L2 for numeric non-Gaussian densities …

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Estimating Social Influence Effects In Networks Using A Latent Space Adjusted Approach In R, Ran Xu Dec 2021

Estimating Social Influence Effects In Networks Using A Latent Space Adjusted Approach In R, Ran Xu

The R Journal

Social influence effects have been extensively studied in various empirical network research. However, many challenges remain in estimating social influence effects in networks, as influence effects are often entangled with other factors, such as homophily in the selection process and the common social-environmental factors that individuals are embedded in. Methods currently available either do not solve these problems or require stringent assumptions. Recent works by Xu (2018) and others have shown that a latent space adjusted approach based on the latent space model has the potential to disentangle the influence effects from other processes, and the simulation evidence has shown …

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G2f As A Novel Tool To Find And Fill Gaps In Metabolic Networks, Daniel Osorio, Kelly Botero, Andrés Pinzón Velasco, Nicolás Mendoza-Mejía, Felipe Rojas-Rodríguez, George Barreto, Janneth González Dec 2021

G2f As A Novel Tool To Find And Fill Gaps In Metabolic Networks, Daniel Osorio, Kelly Botero, Andrés Pinzón Velasco, Nicolás Mendoza-Mejía, Felipe Rojas-Rodríguez, George Barreto, Janneth González

The R Journal

During the building of a genome-scale metabolic model, there are several dead-end metabolites and substrates which cannot be imported, produced, nor used by any reaction incorporated in the network. The presence of these dead-end metabolites can block out the net flux of the objective function when it is evaluated through Flux Balance Analysis (FBA), and when it is not blocked, bias in the biological conclusions increase. In this aspect, the refinement to restore the connectivity of the network can be carried out manually or using computational algorithms. The g2f package was designed as a tool to find the gaps from …

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