wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our scheme retains the underlying methods' FDR guarantees in theory and can decrease FDR and increase power in practice .
Revised:  Our scheme retains the underlying methods' FDR guarantees in theory and simulations, and we show that it can lead to new discoveries on microbiome data from the American Gut Project. In particular, our results indicate that several phyla that have been overlooked so far are associated with obesity .
Edit intent: meaning-changed
Original:  While previous negative results showed this model to have intractably large sample complexity for label queries, we show that comparison queries make RPU-learning at worst logarithmically more expensive in the passive case, and quadratically more expensive in the active case .
Revised:  While previous negative results showed this model to have intractably large sample complexity for label queries, we show that comparison queries make RPU-learning at worst logarithmically more expensive in both the passive and active regimes .
Edit intent: clarity
Original: Functional Magnetic Resonance Imaging (fMRI) helps constructing functional brain networks by using brain activity information . Principal component analysis (PCA) and independent component analysis (ICA) are widely used methods to generate functional brain networks.
Revised: Analysis of data from functional magnetic resonance imaging (fMRI) helps constructing functional brain networks by using brain activity information . Principal component analysis (PCA) and independent component analysis (ICA) are widely used methods to generate functional brain networks.
Edit intent: clarity
Original: Functional Magnetic Resonance Imaging (fMRI) helps constructing functional brain networks by using brain activity information . Principal component analysis (PCA) and independent component analysis (ICA) are widely used methods to generate functional brain networks.
Revised: Functional Magnetic Resonance Imaging (fMRI) results in constructing functional brain networks by using brain activity information . Principal component analysis (PCA) and independent component analysis (ICA) are widely used methods to generate functional brain networks.
Edit intent: clarity
Original: Functional Magnetic Resonance Imaging (fMRI) helps constructing functional brain networks by using brain activity information . Principal component analysis (PCA) and independent component analysis (ICA) are widely used methods to generate functional brain networks.
Revised: Functional Magnetic Resonance Imaging (fMRI) helps constructing functional brain networks . Principal component analysis (PCA) and independent component analysis (ICA) are widely used methods to generate functional brain networks.
Edit intent: clarity
Original: Functional Magnetic Resonance Imaging (fMRI) helps constructing functional brain networks by using brain activity information . Principal component analysis (PCA) and independent component analysis (ICA) are widely used methods to generate functional brain networks.
Revised: Functional Magnetic Resonance Imaging (fMRI) helps constructing functional brain networks by using brain activity information . Principal component analysis (PCA) and independent component analysis (ICA) are widely used to generate functional brain networks.
Edit intent: clarity
Original: This paper considers a semiparametric generalized autoregressive conditional heteroscedastic (S-GARCH) model , which has a smooth long run component with unknown form to depict time-varying parameters, and a GARCH-type short run component to capture the temporal dependence .
Revised: This paper considers a semiparametric generalized autoregressive conditional heteroscedastic (S-GARCH) model .
Edit intent: coherence
Original:  For this S-GARCH model, we first estimate the time-varying long run component by the kernel estimator, and then estimate the non-time-varying parameters in short run component by the quasi maximum likelihood estimator (QMLE).
Revised:  For this model, we first estimate the time-varying long run component by the kernel estimator, and then estimate the non-time-varying parameters in short run component by the quasi maximum likelihood estimator (QMLE).
Edit intent: clarity
Original:  Furthermore, we construct a Lagrange multiplier (LM) test for linear parameter constraint and a portmanteau test for model diagnostic checking, and prove that both tests have the standard chi-squared limiting null distributions.
Revised:  Furthermore, we construct a Lagrange multiplier (LM) test for linear parameter constraint and a portmanteau test for model checking, and prove that both tests have the standard chi-squared limiting null distributions.
Edit intent: clarity
Original:  Our entire statistical inference procedure not only works for the non-stationary data , but also has three novel features: first, our QMLE and two tests are adaptive to the unknown form of the long run component;
Revised:  Our entire statistical inference procedure works for the non-stationary data , but also has three novel features: first, our QMLE and two tests are adaptive to the unknown form of the long run component;
Edit intent: coherence
Original:  Our entire statistical inference procedure not only works for the non-stationary data , but also has three novel features: first, our QMLE and two tests are adaptive to the unknown form of the long run component;
Revised:  Our entire statistical inference procedure not only works for the non-stationary data with two important features: first, our QMLE and two tests are adaptive to the unknown form of the long run component;
Edit intent: meaning-changed
Original:  second , our QMLE and two tests are easy-to-implement due to their related simple asymptotic variance expressions; third , our QMLE and two tests share the same efficiency and testing power as those in variance target method when the S-GARCH model is stationary.
Revised:  second , our QMLE and two tests share the same efficiency and testing power as those in variance target method when the S-GARCH model is stationary.
Edit intent: coherence
Original:  For this model, we first estimate the time-varying long run component  by the kernel estimator, and then estimate the non-time-varying parameters in  short run component by the quasi maximum likelihood estimator (QMLE).
Revised:  For this model, we first estimate the time-varying long run component for unconditional variance by the kernel estimator, and then estimate the non-time-varying parameters in short run component by the quasi maximum likelihood estimator (QMLE).
Edit intent: meaning-changed
Original:  For this model, we first estimate the time-varying long run component  by the kernel estimator, and then estimate the non-time-varying parameters in  short run component by the quasi maximum likelihood estimator (QMLE).
Revised:  For this model, we first estimate the time-varying long run component by the kernel estimator, and then estimate the non-time-varying parameters in GARCH-type short run component by the quasi maximum likelihood estimator (QMLE).
Edit intent: meaning-changed
Original:  More specifically, we show an deterministic algorithm that finds for any set P \subseteq R^d of n points and any k \in \{2, \dots, n\} in O(nd \log k ) time a partition of P into k subsets such that there is a ball of radius O ( \left( k{n}diam(P) ) \right) intersecting the convex hull of each subset.
Revised:  More specifically, we show an deterministic algorithm that finds for any set P \subseteq R^d of n points and any k \in \{2, \dots, n\} in O(nd \log k ) time a partition of P into k subsets such that there is a ball of radius O \left( k{n}diam(P) ) \right) intersecting the convex hull of each subset.
Edit intent: fluency
Original:  More specifically, we show an deterministic algorithm that finds for any set P \subseteq R^d of n points and any k \in \{2, \dots, n\} in O(nd \log k ) time a partition of P into k subsets such that there is a ball of radius O ( \left( k{n}diam(P) ) \right) intersecting the convex hull of each subset.
Revised:  More specifically, we show an deterministic algorithm that finds for any set P \subseteq R^d of n points and any k \in \{2, \dots, n\} in O(nd \log k ) time a partition of P into k subsets such that there is a ball of radius O ( \left( k{n}diam(P) \right) intersecting the convex hull of each subset.
Edit intent: fluency
Original:  The goal is to find a transportation map, a non-negative assignment \tau : P \times P \to R_{ \geq \ge 0} to pairs of points, so the total assignment leaving each point is equal to its supply, i.e., r \in P \tau(q, r) - p \in P \tau(p, q) = \mu(q) for all points q \in P.
Revised:  The goal is to find a transportation map, a non-negative assignment \tau : P \times P \to R_{ \ge 0} to pairs of points, so the total assignment leaving each point is equal to its supply, i.e., r \in P \tau(q, r) - p \in P \tau(p, q) = \mu(q) for all points q \in P.
Edit intent: clarity
Original:  We describe the first algorithm for this problem that returns, with high probability, a (1 + \epsilon)-approximation to the optimal transportation map in O(n \:\text{poly (1 / \epsilon) \:\text{polylog ) time.
Revised:  We describe the first algorithm for this problem that returns, with high probability, a (1 + \epsilon)-approximation to the optimal transportation map in O(n \, \mathrm{poly (1 / \epsilon) \:\text{polylog ) time.
Edit intent: meaning-changed
Original:  We describe the first algorithm for this problem that returns, with high probability, a (1 + \epsilon)-approximation to the optimal transportation map in O(n \:\text{poly (1 / \epsilon) \:\text{polylog ) time.
Revised:  We describe the first algorithm for this problem that returns, with high probability, a (1 + \epsilon)-approximation to the optimal transportation map in O(n \:\text{poly (1 / \epsilon) \, \mathrm{polylog ) time.
Edit intent: meaning-changed
Original:  It is satisfied, for instance, when the choice set variation is the result of either a search process or unobserved feasibility. Using variation of the uncovered choice sets we show how to do ordinal (nonparametric) welfare comparisons. When one is willing to make additional assumptions about preferences , we show how to nonparametrically identify the ranking over average utilities in the standard multinomial choice setting .
Revised:  It is satisfied, for instance, when the choice sets are nested, or when they form a partition. Our estimation procedure is computationally fast and uses mixed-integer optimization to recover the sparse support of choice sets. Analyzing the ready-to-eat cereal industry using a household scanner dataset, we find that ignoring the unobservability of choice sets can lead to biased estimates of preferences due to significant latent heterogeneity in choice sets .
Edit intent: meaning-changed
Original:  This consists of a statistical mechanical theory - derived from first principles - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series.
Revised:  This consists of a statistical mechanical theory - derived from the Maximum Entropy principle - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series.
Edit intent: meaning-changed
Original:  This consists of a statistical mechanical theory - derived from the Maximum Entropy principle - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series.
Revised:  This consists of a statistical mechanical approach - derived from the Maximum Entropy principle - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series.
Edit intent: clarity
Original:  This consists of a statistical mechanical theory - derived from the Maximum Entropy principle - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series.
Revised:  This consists of a statistical mechanical theory - analogous to the configuration model for networked systems - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series.
Edit intent: meaning-changed
Original: Red-green-blue (RGB) digital image-based detection is a promising alternative approach to the existing subjectivity-prone and labor-intensive plant disease quantification methods. K-means clustering (KMC) is a widely used algorithm for processing plant disease RGB images. Because the KMC algorithm clusters pixels based only on color similarity, it has limited utility for quantifying diseases that produce necrotic, coalescing, and water-soaked lesions .
Revised: Rapid and accurate phenotypic screening of rice germplasms is crucial in screening for sources of rice sheath blight resistance. However, visual and/or caliper-based estimations of coalescing, necrotic, ShB disease lesions are time-consuming, labor-intensive and exposed to human rater subjectivity .
Edit intent: coherence
Original:  Here, we present a pixel color- and coordinate-based K-Means clustering (PCC-KMC) algorithm that is capable of RGB image analysis based on color (normalized RGB values) and spatial information (column and row coordinates) of pixels for quantification of rice sheath blight (ShB) disease .
Revised:  Here, we propose the use of RGB images and image processing techniques to quantify ShB disease progression in terms of lesion height and diseased area. To be specific, we developed a pixel color- and coordinate-based K-Means clustering (PCC-KMC) algorithm that is capable of RGB image analysis based on color (normalized RGB values) and spatial information (column and row coordinates) of pixels for quantification of rice sheath blight (ShB) disease .
Edit intent: meaning-changed
Original:  Here, we present a pixel color- and coordinate-based K-Means clustering (PCC-KMC) algorithm that is capable of RGB image analysis based on color (normalized RGB values) and spatial information (column and row coordinates) of pixels for quantification of rice sheath blight (ShB) disease .
Revised:  Here, we present a pixel color- and coordinate-based K-Means Clustering (PCC-KMC) algorithm that is capable of RGB image analysis based on color (normalized RGB values) and spatial information (column and row coordinates) of pixels for quantification of rice sheath blight (ShB) disease .
Edit intent: fluency
Original:  Here, we present a pixel color- and coordinate-based K-Means clustering (PCC-KMC) algorithm that is capable of RGB image analysis based on color (normalized RGB values) and spatial information (column and row coordinates) of pixels for quantification of rice sheath blight (ShB) disease .
Revised:  Here, we present a pixel color- and coordinate-based K-Means clustering (PCC-KMC) algorithm utilizing Mahalanobis metric aimed at accurate segmentation of symptomatic and non-symptomatic regions within rice stem images .
Edit intent: meaning-changed
Original:  The performance of PCC-KMC was tested using RGB images of ShB-infected stems of the rice lines Jasmine 85 , Lemont, and 301194.
Revised:  The performance of PCC-KMC was evaluated using Lin's concordance correlation coefficient by comparing its results to visual measurements of ShB lesion height and to lesion/diseased area measured using ImageJ. Low bias and high precision were observed for absolute lesion height (bias=0.93, precision=0.94) and absolute symptomatic area (bias=0.98, precision=0.97) studies. Moreover, we introduced a convolutional neural network (CNN) for the automatic annotation on clusters, termed PCC-KMC-CNN. Our CNN was trained based on 85 , Lemont, and 301194.
Edit intent: meaning-changed
Original:  The performance of PCC-KMC was tested using RGB images of ShB-infected stems of the rice lines Jasmine 85 , Lemont, and 301194. In contrast to KMC, which clustered randomly scattered pixels of similar color, PCC-KMC successfully segmented diseased regions into biologically-relevant clusters and thereby correctly indicated the pattern of ShB progression. PCC-KMC also enabled the quantification of disease in terms of the percentage of stem area with symptoms and the distance of disease progression from the inoculation site. The results indicate that the PCC-KMC algorithm can be used for the automated quantification of lesions caused by SbB on rice and of lesions caused by other pathogens on other plants .
Revised:  The performance of PCC-KMC was tested using RGB images of ShB-infected stems of the rice lines Jasmine 85 \%:15\% of composition for training and testing dataset from total 168 ShB-infected stem sample images, recording 92\% accuracy and 0.21 loss. PCC-KMC-CNN also showed high accuracy and precision for the absolute lesion height (bias=0.86, precision=0.90) and absolute diseased area (bias=0.99, precision=0.97) studies. These results demonstrate that the present methodology has great potential and promise to substitute the traditional visual-based ShB disease severity assessment .
Edit intent: meaning-changed
Original:  However, existing accelerators lack the efficient hardware acceleration for  exact pattern matching (EPM) that is an even more critical and essential function widely used in almost every step of genome analysis including assembly, alignment, annotation , and compression.
Revised:  However, existing accelerators lack the efficient hardware acceleration for the exact pattern matching (EPM) that is an even more critical and essential function widely used in almost every step of genome analysis including assembly, alignment, annotation , and compression.
Edit intent: fluency
Original:  However, existing accelerators lack the efficient hardware acceleration for  exact pattern matching (EPM) that is an even more critical and essential function widely used in almost every step of genome analysis including assembly, alignment, annotation , and compression.
Revised:  However, existing accelerators lack the efficient hardware acceleration for exact pattern matching (EPM) that is an even more critical and essential function widely used in almost every step of genome analysis including assembly, alignment, annotation and compression.
Edit intent: fluency
Original:  We propose a lattice model that roughly accounts for proteins' attraction within a layer and across the stromal gap, steric repulsion across the lumen gap, and regulation of protein densityby exchange with the stroma lamellae  Mean field analysis and computer simulation reveal rich phase behavior for this simple model, featuring a broken-symmetry stripephase that is disrupted at both high and low extremes of chemical potential.
Revised:  We propose a lattice model that roughly accounts for proteins' attraction within a layer and across the stromal gap, steric repulsion across the lumen gap, and regulation of protein density by exchange with the stroma lamellae Mean field analysis and computer simulation reveal rich phase behavior for this simple model, featuring a broken-symmetry stripephase that is disrupted at both high and low extremes of chemical potential.
Edit intent: fluency
Original:  We propose a lattice model that roughly accounts for proteins' attraction within a layer and across the stromal gap, steric repulsion across the lumen gap, and regulation of protein densityby exchange with the stroma lamellae  Mean field analysis and computer simulation reveal rich phase behavior for this simple model, featuring a broken-symmetry stripephase that is disrupted at both high and low extremes of chemical potential.
Revised:  We propose a lattice model that roughly accounts for proteins' attraction within a layer and across the stromal gap, steric repulsion across the lumen gap, and regulation of protein densityby exchange with the stroma lamellae . Mean field analysis and computer simulation reveal rich phase behavior for this simple model, featuring a broken-symmetry stripephase that is disrupted at both high and low extremes of chemical potential.
Edit intent: fluency
Original:  We propose a lattice model that roughly accounts for proteins' attraction within a layer and across the stromal gap, steric repulsion across the lumen gap, and regulation of protein densityby exchange with the stroma lamellae  Mean field analysis and computer simulation reveal rich phase behavior for this simple model, featuring a broken-symmetry stripephase that is disrupted at both high and low extremes of chemical potential.
Revised:  We propose a lattice model that roughly accounts for proteins' attraction within a layer and across the stromal gap, steric repulsion across the lumen gap, and regulation of protein densityby exchange with the stroma lamellae Mean field analysis and computer simulation reveal rich phase behavior for this simple model, featuring a broken-symmetry stripe phase that is disrupted at both high and low extremes of chemical potential.
Edit intent: fluency
Original:  We propose a lattice model that roughly accounts for proteins' attraction within a layer and across the stromal gap, steric repulsion across the lumen gap, and regulation of protein density by exchange with the stroma lamellae.
Revised:  We propose a lattice model that roughly accounts for proteins' attraction within a layer and across the stromal gap, steric repulsion across the lumenal gap, and regulation of protein density by exchange with the stroma lamellae.
Edit intent: fluency
Original:  De Luca et al. [GD'18] considered the recognition problem of stick graphs for the case that the ordering of either one of the two sets  (STICK_A) is given and for the case that the ordering of both sets is given (STICK_AB).
Revised:  De Luca et al. [GD'18] considered the recognition problem of stick graphs when no order is given (STICK), when the order of either one of the two sets (STICK_A) is given and for the case that the ordering of both sets is given (STICK_AB).
Edit intent: meaning-changed
Original:  De Luca et al. [GD'18] considered the recognition problem of stick graphs for the case that the ordering of either one of the two sets  (STICK_A) is given and for the case that the ordering of both sets is given (STICK_AB).
Revised:  De Luca et al. [GD'18] considered the recognition problem of stick graphs for the case that the ordering of either one of the two sets is given (STICK_A) is given and for the case that the ordering of both sets is given (STICK_AB).
Edit intent: fluency
Original:  De Luca et al. [GD'18] considered the recognition problem of stick graphs for the case that the ordering of either one of the two sets  (STICK_A) is given and for the case that the ordering of both sets is given (STICK_AB).
Revised:  De Luca et al. [GD'18] considered the recognition problem of stick graphs for the case that the ordering of either one of the two sets (STICK_A) , and when the order of both sets is given (STICK_AB).
Edit intent: coherence
Original:  In this paper, we improve the running times of their algorithms and consider new variants of STICK, where no ordering is given, STICK_A, and STICK_AB where the lengths of the sticks are given as input.
Revised:  In this paper, we improve the running time of their algorithm, and we solve STICK_A efficiently. Further, we consider variants of these problems where the lengths of the sticks are given as input.
Edit intent: clarity