wanyu/IteraTeR_v2 huggingface dataset:
Original:  The present approach can be used to build a new generation of platelets functionality tests for patients, by combining in-vitro observation, mathematical modeling , Bayesian inference and high performance computing.
Revised:  The present approach can be used to build a new generation of personalized platelet functionality tests for patients, by combining in-vitro observation, mathematical modeling , Bayesian inference and high performance computing.
Edit intent: clarity
Original: Linkage maps are important for fundamental and applied genetic research.
Revised: Linkage maps are important tools for genetic research.
Edit intent: clarity
Original:  New sequencing techniques have been created opportunities to increase substantially the density of genetic markers.
Revised:  New sequencing techniques have created opportunities to increase substantially the density of genetic markers.
Edit intent: fluency
Original:  New sequencing techniques have been created opportunities to increase substantially the density of genetic markers.
Revised:  New sequencing techniques have been created opportunities to substantially increase the density of genetic markers.
Edit intent: fluency
Original:  With such revolutionary advances in technology come new challengesin methodologies and informatics.
Revised:  Such revolutionary advances in technology come new challengesin methodologies and informatics.
Edit intent: fluency
Original:  We propose to construct linkage maps using graphical models either via a sparse Gaussian copula or via a nonparanormal skeptic approach.
Revised:  We propose to construct linkage maps using graphical models either via a sparse Gaussian copula or a nonparanormal skeptic approach.
Edit intent: fluency
Original:  Linkage groups (LGs), typically chromosomes, and the order of markers in each LG is determined by revealing the conditional independence relationships among a large number of markers in the genome.
Revised:  Linkage groups (LGs), typically chromosomes, and the order of markers in each LG are determined by inferring the conditional independence relationships among a large number of markers in the genome.
Edit intent: clarity
Original:  Linkage groups (LGs), typically chromosomes, and the order of markers in each LG is determined by revealing the conditional independence relationships among a large number of markers in the genome.
Revised:  Linkage groups (LGs), typically chromosomes, and the order of markers in each LG is determined by revealing the conditional independence relationships among large numbers of markers in the genome.
Edit intent: fluency
Original:  We show that our method outperforms other available methods in terms of determining the correct number of linkage groups and ordering markers both when the data are clean and contain no missing observations and when data are noisy and incomplete.
Revised:  We show that our method outperforms other available methods , both when the data are clean and contain no missing observations and when data are noisy and incomplete.
Edit intent: clarity
Original: Linkage maps are important tools for genetic research .
Revised: Linkage maps are used to identify the location of genes responsible for traits and diseases .
Edit intent: clarity
Original:  We have implemented the method in the R package netgwas which is freely available at URL
Revised:  We have implemented the method in the R package netgwas .
Edit intent: coherence
Original:  The resulting process switches between two deterministic Lotka-Volterra systems at exponential times and therefore is a Piecewise Deterministic Markov Process (PDMP).
Revised:  The resulting process is a Piecewise Deterministic Markov Process (PDMP).
Edit intent: coherence
Original:  In the case when the equilibrium points of the two deterministic Lotka-Volterra systems coincide we show that almost surely the trajectory does not converge to the common deterministic equilibrium.
Revised:  In the case when the equilibrium points of the two deterministic Lotka--Volterra systems coincide we show that almost surely the trajectory does not converge to the common deterministic equilibrium.
Edit intent: fluency
Original:  We provide upper and lower bounds on the maximal asymptotic yield, and explore via numerical simulations how the harvesting threshold and the maximal asymptotic yield change with the growth rate, maximal harvesting rate  or the competition rate.
Revised:  We provide upper and lower bounds on the maximal asymptotic yield, and explore via numerical simulations how the harvesting threshold and the maximal asymptotic yield change with the growth rate, maximal harvesting rate , or the competition rate.
Edit intent: fluency
Original:  In the case when the yield function is  strictly concave, we prove that the optimal harvesting strategy is continuous  .
Revised:  We also show that, if the yield function is strictly concave, we prove that the optimal harvesting strategy is continuous .
Edit intent: coherence
Original:  In the case when the yield function is  strictly concave, we prove that the optimal harvesting strategy is continuous  .
Revised:  In the case when the yield function is strictly concave, then the optimal harvesting strategy is continuous .
Edit intent: coherence
Original:  In fact , different steganography methods have been proposed for mp3  . But, only steganalysis of mp3stego has been conducted in the literature .
Revised:  Therefore , different steganography methods have been proposed for mp3 . But, only steganalysis of mp3stego has been conducted in the literature .
Edit intent: coherence
Original:  In fact , different steganography methods have been proposed for mp3  . But, only steganalysis of mp3stego has been conducted in the literature .
Revised:  In fact , different steganography methods have been proposed for mp3 hosts . But, only steganalysis of mp3stego has been conducted in the literature .
Edit intent: clarity
Original:  In fact , different steganography methods have been proposed for mp3  . But, only steganalysis of mp3stego has been conducted in the literature .
Revised:  In fact , different steganography methods have been proposed for mp3 . But, only steganalysis of mp3stego .
Edit intent: clarity
Original:  In this paper we mention some  limitations of mp3stego and argue that Undermp3cover (Ump3c) does not have them.
Revised:  In this paper we mention some of the limitations of mp3stego and argue that Undermp3cover (Ump3c) does not have them.
Edit intent: fluency
Original:  In this paper we mention some  limitations of mp3stego and argue that Undermp3cover (Ump3c) does not have them.
Revised:  In this paper we mention some limitations of mp3stego and argue that UnderMp3Cover (Ump3c) does not have them.
Edit intent: fluency
Original:  Furthermore, we show that different mp3 encoders have quite different performancesand therefore we propose a novel multi-layer architecture for its steganalysis .
Revised:  Furthermore, we show that different mp3 encoders have dissimilar performances. Consequently, a novel multi-layer architecture for its steganalysis .
Edit intent: coherence
Original:  One of advantages of this architecture is that feature extraction and feature selection can be optimized per encoder .
Revised:  One of advantages of this architecture is that feature extraction and feature selection can be optimized for each encoder separately .
Edit intent: clarity
Original:  Comparing results of the proposed method with other works show an improvement of 20.4\% in accuracy of multi-encoder scenario .
Revised:  Comparing results of the proposed method with other works shows an improvement of 20.4\% in accuracy of multi-encoder scenario .
Edit intent: fluency
Original: Graphical models provide powerful tools to model and make the statistical inference regarding complex relationships among variables in multivariate data.
Revised: Graphical models are powerful tools for modeling and making statistical inferences regarding complex relationships among variables in multivariate data.
Edit intent: clarity
Original:  They are widely used in statistics and machine learning particularly to analyze biological networks.
Revised:  They are widely used in statistics and machine learning , for example, to analyze biological networks.
Edit intent: coherence
Original:  In this paper , we introduce the R package netgwas  which is designed for accomplishing three important , and inter-related, goals in genetics: linkage map construction, reconstructing intra- and inter- chromosomal interactions and exploring high-dimensional genotype-phenotype (and genotypes-phenotypes-environments) network .
Revised:  In this paper we introduce the R package netgwas which is designed for accomplishing three important , and inter-related, goals in genetics: linkage map construction, reconstructing intra- and inter- chromosomal interactions and exploring high-dimensional genotype-phenotype (and genotypes-phenotypes-environments) network .
Edit intent: fluency
Original:  In this paper , we introduce the R package netgwas  which is designed for accomplishing three important , and inter-related, goals in genetics: linkage map construction, reconstructing intra- and inter- chromosomal interactions and exploring high-dimensional genotype-phenotype (and genotypes-phenotypes-environments) network .
Revised:  In this paper , we introduce the R package netgwas , which is designed for accomplishing three important , and inter-related, goals in genetics: linkage map construction, reconstructing intra- and inter- chromosomal interactions and exploring high-dimensional genotype-phenotype (and genotypes-phenotypes-environments) network .
Edit intent: fluency
Original:  In this paper , we introduce the R package netgwas  which is designed for accomplishing three important , and inter-related, goals in genetics: linkage map construction, reconstructing intra- and inter- chromosomal interactions and exploring high-dimensional genotype-phenotype (and genotypes-phenotypes-environments) network .
Revised:  In this paper , we introduce the R package netgwas which is designed to accomplish three important and interrelated goals in genetics: linkage map construction, reconstructing intra- and inter- chromosomal interactions and exploring high-dimensional genotype-phenotype (and genotypes-phenotypes-environments) network .
Edit intent: fluency
Original:  In this paper , we introduce the R package netgwas  which is designed for accomplishing three important , and inter-related, goals in genetics: linkage map construction, reconstructing intra- and inter- chromosomal interactions and exploring high-dimensional genotype-phenotype (and genotypes-phenotypes-environments) network .
Revised:  In this paper , we introduce the R package netgwas which is designed for accomplishing three important , and inter-related, goals in genetics: linkage map construction, reconstructing intra- and inter-chromosomal interactions, and exploring high-dimensional genotype-phenotype (and genotypes-phenotypes-environments) network .
Edit intent: fluency
Original:  In this paper , we introduce the R package netgwas  which is designed for accomplishing three important , and inter-related, goals in genetics: linkage map construction, reconstructing intra- and inter- chromosomal interactions and exploring high-dimensional genotype-phenotype (and genotypes-phenotypes-environments) network .
Revised:  In this paper , we introduce the R package netgwas which is designed for accomplishing three important , and inter-related, goals in genetics: linkage map construction, reconstructing intra- and inter- chromosomal interactions and exploring high-dimensional genotype-phenotype and genotype-phenotype-environment networks .
Edit intent: fluency
Original:  The netgwas package has the capability of dealing with species of any ploidy level.
Revised:  The netgwas package can deal with species of any ploidy level.
Edit intent: clarity
Original:  The package implements the recent improvements in  linkage map construction (Behrouzi and Wit, 2017a), and in inferring the conditional independence network for non-Gaussian  , discrete, and mixed  data (Behrouzi and Wit, 2017b) , which routinely occurs in genetics and genomics such as genotype data, genotype-phenotype datasets , and genotype-phenotype including environmental variables.
Revised:  The package implements recent improvements in linkage map construction (Behrouzi and Wit, 2017a), and in inferring the conditional independence network for non-Gaussian , discrete, and mixed data (Behrouzi and Wit, 2017b) , which routinely occurs in genetics and genomics such as genotype data, genotype-phenotype datasets , and genotype-phenotype including environmental variables.
Edit intent: fluency
Original:  The package implements the recent improvements in  linkage map construction (Behrouzi and Wit, 2017a), and in inferring the conditional independence network for non-Gaussian  , discrete, and mixed  data (Behrouzi and Wit, 2017b) , which routinely occurs in genetics and genomics such as genotype data, genotype-phenotype datasets , and genotype-phenotype including environmental variables.
Revised:  The package implements the recent improvements in both linkage map construction (Behrouzi and Wit, 2017a), and in inferring the conditional independence network for non-Gaussian , discrete, and mixed data (Behrouzi and Wit, 2017b) , which routinely occurs in genetics and genomics such as genotype data, genotype-phenotype datasets , and genotype-phenotype including environmental variables.
Edit intent: clarity
Original:  The package implements the recent improvements in  linkage map construction (Behrouzi and Wit, 2017a), and in inferring the conditional independence network for non-Gaussian  , discrete, and mixed  data (Behrouzi and Wit, 2017b) , which routinely occurs in genetics and genomics such as genotype data, genotype-phenotype datasets , and genotype-phenotype including environmental variables.
Revised:  The package implements the recent improvements in linkage map construction (Behrouzi and Wit, 2017a), and reconstructing conditional independence network for non-Gaussian , discrete, and mixed data (Behrouzi and Wit, 2017b) , which routinely occurs in genetics and genomics such as genotype data, genotype-phenotype datasets , and genotype-phenotype including environmental variables.
Edit intent: clarity
Original:  The package implements the recent improvements in  linkage map construction (Behrouzi and Wit, 2017a), and in inferring the conditional independence network for non-Gaussian  , discrete, and mixed  data (Behrouzi and Wit, 2017b) , which routinely occurs in genetics and genomics such as genotype data, genotype-phenotype datasets , and genotype-phenotype including environmental variables.
Revised:  The package implements the recent improvements in linkage map construction (Behrouzi and Wit, 2017a), and in inferring the conditional independence network for non-Gaussian , and mixed data (Behrouzi and Wit, 2017b) , which routinely occurs in genetics and genomics such as genotype data, genotype-phenotype datasets , and genotype-phenotype including environmental variables.
Edit intent: clarity
Original:  The package implements the recent improvements in  linkage map construction (Behrouzi and Wit, 2017a), and in inferring the conditional independence network for non-Gaussian  , discrete, and mixed  data (Behrouzi and Wit, 2017b) , which routinely occurs in genetics and genomics such as genotype data, genotype-phenotype datasets , and genotype-phenotype including environmental variables.
Revised:  The package implements the recent improvements in linkage map construction (Behrouzi and Wit, 2017a), and in inferring the conditional independence network for non-Gaussian , discrete, and mixed data (Behrouzi and Wit, 2017b) . Such datasets routinely occur in genetics and genomics such as genotype data, genotype-phenotype datasets , and genotype-phenotype including environmental variables.
Edit intent: clarity
Original:  The package implements the recent improvements in  linkage map construction (Behrouzi and Wit, 2017a), and in inferring the conditional independence network for non-Gaussian  , discrete, and mixed  data (Behrouzi and Wit, 2017b) , which routinely occurs in genetics and genomics such as genotype data, genotype-phenotype datasets , and genotype-phenotype including environmental variables.
Revised:  The package implements the recent improvements in linkage map construction (Behrouzi and Wit, 2017a), and in inferring the conditional independence network for non-Gaussian , discrete, and mixed data (Behrouzi and Wit, 2017b) , which routinely occurs in genetics and genomics such as genotype data, genotype-phenotype dataset , and genotype-phenotype including environmental variables.
Edit intent: fluency
Original:  In addition, it contains several functions for simulation and visualization  as well as three multivariate example datasets are taken from the literature and that are used to illustrate the package capabilities.
Revised:  In addition, it contains several functions for simulation and visualization , as well as three multivariate example datasets are taken from the literature and that are used to illustrate the package capabilities.
Edit intent: fluency
Original:  In addition, it contains several functions for simulation and visualization  as well as three multivariate example datasets are taken from the literature and that are used to illustrate the package capabilities.
Revised:  In addition, it contains several functions for simulation and visualization as well as three multivariate example datasets taken from the literature and that are used to illustrate the package capabilities.
Edit intent: fluency
Original:  In addition, it contains several functions for simulation and visualization  as well as three multivariate example datasets are taken from the literature and that are used to illustrate the package capabilities.
Revised:  In addition, it contains several functions for simulation and visualization as well as three multivariate example datasets are taken from the literature and used to illustrate the package capabilities.
Edit intent: coherence
Original:  Furthermore, we illustrate the package functionality with real examples.
Revised:  We also illustrate the package functionality with real examples.
Edit intent: coherence
Original:  Graphical models are powerful tools for modeling and making statistical inferences regarding complex relationships among variables in multivariate data.
Revised:  Graphical models are powerful tools for modeling and making statistical inferences regarding complex associations among variables in multivariate data.
Edit intent: clarity
Original:  In this paper we introduce the R package netgwas, which is designed  to accomplish three important and interrelated goals in genetics: linkage map construction, reconstructing intra- and inter-chromosomal interactions, and exploring high-dimensional genotype-phenotype and genotype-phenotype-environment networks.
Revised:  In this paper we introduce the R package netgwas, which is designed to accomplish three important and interrelated goals in genetics: linkage map construction, reconstructing intra- and inter-chromosomal interactions, and exploring high-dimensional genotype-phenotype networks.
Edit intent: clarity
Original:  The package implements recent improvements in both linkage map construction (Behrouzi and Wit, 2017a), and reconstructing conditional independence network for non-Gaussian data, discrete data, and mixed discrete and continues data(Behrouzi and Wit, 2017b) .
Revised:  It implements recent improvements in both linkage map construction (Behrouzi and Wit, 2017a), and reconstructing conditional independence network for non-Gaussian data, discrete data, and mixed discrete and continues data(Behrouzi and Wit, 2017b) .
Edit intent: clarity
Original:  The package implements recent improvements in both linkage map construction (Behrouzi and Wit, 2017a), and reconstructing conditional independence network for non-Gaussian data, discrete data, and mixed discrete and continues data(Behrouzi and Wit, 2017b) .
Revised:  The package implements recent improvements in both linkage map construction , and reconstructing conditional independence network for non-Gaussian data, discrete data, and mixed discrete and continues data(Behrouzi and Wit, 2017b) .
Edit intent: fluency
Original:  The package implements recent improvements in both linkage map construction (Behrouzi and Wit, 2017a), and reconstructing conditional independence network for non-Gaussian data, discrete data, and mixed discrete and continues data(Behrouzi and Wit, 2017b) .
Revised:  The package implements recent improvements in both linkage map construction (Behrouzi and Wit, 2017a), and reconstructing conditional independence network for non-Gaussian data, discrete data, and mixed discrete-and-continuous data .
Edit intent: coherence
Original:  They are capable of adjusting the effect of all other variables while measuring the pairwise associations.
Edit intent: coherence
Original:  In this paper we introduce the R package netgwas, which is designed based on  graphical models to accomplish three important and interrelated goals in genetics: constructing linkage map, reconstructing linkage disequilibrium  networks from multi-loci genotype data, and detecting high-dimensional genotype-phenotype networks.
Revised:  In this paper we introduce the R package netgwas, which is designed based on graphical models to accomplish three important and interrelated goals in genetics: constructing linkage map, reconstructing linkage disequilibrium (LD) networks from multi-loci genotype data, and detecting high-dimensional genotype-phenotype networks.
Edit intent: clarity
Original:  The netgwas package can deal with species of any ploidy level .
Revised:  The netgwas package deals with species with any chromosome copy number in a unified way, unlike other software .
Edit intent: clarity
Original:  Such datasets routinely occur in genetics and genomics such as genotype data,  genotype-phenotype data , and multi-loci multi-trait multi-environment data .
Revised:  Such datasets routinely occur in genetics and genomics such as genotype data, and genotype-phenotype data , and multi-loci multi-trait multi-environment data .
Edit intent: coherence
Original:  Such datasets routinely occur in genetics and genomics such as genotype data,  genotype-phenotype data , and multi-loci multi-trait multi-environment data .
Revised:  Such datasets routinely occur in genetics and genomics such as genotype data, genotype-phenotype data .
Edit intent: clarity
Original:  We demonstrate the value of our package functionality by applying it to five multivariate example datasets taken from the literature.
Revised:  We demonstrate the value of our package functionality by applying it to various multivariate example datasets taken from the literature.
Edit intent: clarity
Original: In this paper, we give two new deterministic interpolation algorithms for black-box multivariate polynomials.
Revised: In this paper, we propose two new deterministic interpolation algorithms for black-box multivariate polynomials.
Edit intent: clarity
Original:  Our first result demonstrates that when agents only know local information about the resources they can select, a system operator is forced to manage an inherent trade-off between the  price of anarchy and price of stability , i. e., improving the quality of the worst equilibria comes at the expenses of the best equilibria .
Revised:  Our first result demonstrates that any agent-based algorithm relying solely on local information induces a fundamental trade-off between the price of anarchy and price of stability , i. e., improving the quality of the worst equilibria comes at the expenses of the best equilibria .
Edit intent: clarity