wanyu/IteraTeR_v2 huggingface dataset:
Original:  Several of these problems are polynomially solvable on a hereditary graph class G {\cal  if they are so on the atoms (graphs with no clique cut-set) of G {\cal  .
Revised:  Several of these problems are polynomially solvable on a hereditary graph class {\cal if they are so on the atoms (graphs with no clique cut-set) of G {\cal .
Edit intent: fluency
Original:  Several of these problems are polynomially solvable on a hereditary graph class G {\cal  if they are so on the atoms (graphs with no clique cut-set) of G {\cal  .
Revised:  Several of these problems are polynomially solvable on a hereditary graph class G {\cal if they are so on the atoms (graphs with no clique cut-set) of {\cal .
Edit intent: fluency
Original:  Several of these problems are polynomially solvable on a hereditary graph class G {\cal  if they are so on the atoms (graphs with no clique cut-set) of G {\cal  .
Revised:  Several of these problems are polynomially solvable on a hereditary graph class G {\cal if they are so on the atoms (graphs with no clique cut-set) of G {\cal G .
Edit intent: clarity
Original:  A graph G is H-free if H is not an induced subgraph of G, and G is (H_1,H_2)-free if it is both H_1-free and H_2-free.
Revised:  A graph G is H-free if H is not an induced subgraph of G, and it is (H_1,H_2)-free if it is both H_1-free and H_2-free.
Edit intent: fluency
Original:  This is no longer true for (H_1,H_2)-free graphs  as evidenced by one known example.
Revised:  This is no longer true for (H_1,H_2)-free graphs , as evidenced by one known example.
Edit intent: fluency
Original:  Of particular importance are machine learning (ML) tasks , which are becoming ubiquitous in networked applicationsranging from content recommendation systems to intelligent vehicular communications .
Revised:  Of particular importance are machine learning (ML) tasks are becoming ubiquitous in networked applicationsranging from content recommendation systems to intelligent vehicular communications .
Edit intent: fluency
Original:  Of particular importance are machine learning (ML) tasks , which are becoming ubiquitous in networked applicationsranging from content recommendation systems to intelligent vehicular communications .
Revised:  Of particular importance are machine learning (ML) tasks , which are becoming ubiquitous in today's network applications .
Edit intent: clarity
Original:  To address this, we advocate a new learning paradigm called fog learning which will intelligently distribute ML model training across the fog, the continuum of nodes from edge devices to cloud servers.
Revised:  To address this, we advocate a new learning paradigm called fog learning which will intelligently distribute ML model training across the continuum of nodes from edge devices to cloud servers.
Edit intent: coherence
Original:  We also discuss several open research directions for fog learning.
Revised:  We also discuss several open research directions to realizing fog learning.
Edit intent: clarity
Original:  With small datasets , networks generalize poorly.
Revised:  With small datasets networks generalize poorly.
Edit intent: fluency
Original:  Generative Adversarial Networks (GANs) have been utilized to generate new data and improve CNN performance .
Revised:  Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs .
Edit intent: clarity
Original:  Nevertheless, generative models have not been used for augmenting data to improve the training of another generative model .
Revised:  Nevertheless, data augmentation techniques for training GANs are under-explored compared to CNNs .
Edit intent: clarity
Original:  In this work, we propose a new GAN architecture for semi-supervised augmentation of chest X-rays for the detection of pneumonia  .
Revised:  In this work, we propose a new GAN architecture for augmentation of chest X-rays for the detection of pneumonia .
Edit intent: clarity
Original:  In this work, we propose a new GAN architecture for semi-supervised augmentation of chest X-rays for the detection of pneumonia  .
Revised:  In this work, we propose a new GAN architecture for semi-supervised augmentation of chest X-rays for semi-supervised detection of pneumonia .
Edit intent: fluency
Original:  We show that the proposed GAN can augment data for a specific class of images (pneumonia) using images from both classes (pneumonia and normal) in an image domain ( chest X-rays ).
Revised:  We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays ).
Edit intent: clarity
Original: Stance detection on social media is an emerging opinion mining paradigm for various social and political applications wheresentiment analysis might be seen sub-optimal.
Revised: Stance detection on social media is an emerging opinion mining paradigm for various social and political applications where sentiment analysis might be seen sub-optimal.
Edit intent: fluency
Original: Stance detection on social media is an emerging opinion mining paradigm for various social and political applications wheresentiment analysis might be seen sub-optimal.
Revised: Stance detection on social media is an emerging opinion mining paradigm for various social and political applications wheresentiment analysis might be sub-optimal.
Edit intent: clarity
Original:  This paper surveys the work on stance detection and situates its usage withincurrent opinion mining techniques in social media.
Revised:  This paper surveys the work on stance detection and situates its usage within current opinion mining techniques in social media.
Edit intent: fluency
Original:  An exhaustive review of stance detection techniques on social media ispresented ,including the task definition, the different types of targets in stance detection, the features set used, and the variousmachine learning approaches applied.
Revised:  An exhaustive review of stance detection techniques on social media ispresented ,including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.
Edit intent: fluency
Original:  The survey reports the state-of-the-art results on the existing benchmark datasets onstance detection, and discusses the most effective approaches.
Revised:  The survey reports the state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches.
Edit intent: fluency
Original:  The study concludes by providing discussion of the gabs in the current existing research and highlighting the possible futuredirections for stance detection on social media 
Revised:  The study concludes by providing discussion of the gaps in the current existing research and highlighting the possible futuredirections for stance detection on social media 
Edit intent: fluency
Original:  The study concludes by providing discussion of the gabs in the current existing research and highlighting the possible futuredirections for stance detection on social media 
Revised:  The study concludes by providing discussion of the gabs in the current existing research and highlighting the possible future directions for stance detection on social media 
Edit intent: fluency
Original:  The study concludes by providing discussion of the gabs in the current existing research and highlighting the possible futuredirections for stance detection on social media 
Revised:  The study concludes by providing discussion of the gabs in the current existing research and highlighting the possible futuredirections for stance detection on social media .
Edit intent: fluency
Original: Stance detection on social media is an emerging opinion mining paradigm for various social and political applications where sentiment analysis might be sub-optimal.
Revised: Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal.
Edit intent: clarity
Original:  An exhaustive review of stance detection techniques on social media is presented , including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.
Revised:  It presents an exhaustive review of stance detection techniques on social media is presented , including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.
Edit intent: clarity
Original:  An exhaustive review of stance detection techniques on social media is presented , including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.
Revised:  An exhaustive review of stance detection techniques on social media , including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.
Edit intent: clarity
Original:  An exhaustive review of stance detection techniques on social media is presented , including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.
Revised:  An exhaustive review of stance detection techniques on social media is presented , including the task definition, different types of targets in stance detection, the features set used, and the various machine learning approaches applied.
Edit intent: fluency
Original:  An exhaustive review of stance detection techniques on social media is presented , including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.
Revised:  An exhaustive review of stance detection techniques on social media is presented , including the task definition, the different types of targets in stance detection, features set used, and the various machine learning approaches applied.
Edit intent: fluency
Original:  An exhaustive review of stance detection techniques on social media is presented , including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.
Revised:  An exhaustive review of stance detection techniques on social media is presented , including the task definition, the different types of targets in stance detection, the features set used, and various machine learning approaches applied.
Edit intent: fluency
Original:  The survey reports the state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches.
Revised:  The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches.
Edit intent: fluency
Original:  In addition, this study explores the emerging trends and the different applications of stance detection on social media.
Revised:  In addition, this study explores the emerging trends and different applications of stance detection on social media.
Edit intent: fluency
Original:  The study concludes by providing discussion of the gaps in the current existing research and highlighting the possible future directions for stance detection on social media.
Revised:  The study concludes by discussing the gaps in the current existing research and highlighting the possible future directions for stance detection on social media.
Edit intent: clarity
Original:  The study concludes by providing discussion of the gaps in the current existing research and highlighting the possible future directions for stance detection on social media.
Revised:  The study concludes by providing discussion of the gaps in the current existing research and highlights the possible future directions for stance detection on social media.
Edit intent: fluency
Original:  This work analyzes the use of  information from the wireless environment for the purpose of authentication and demonstrates the employment of a generative adversarial neural network  trained with received channel data to authenticate a transmitting device without prior knowledge of receiver noise .
Revised:  This work analyzes the use of information from the wireless environment and demonstrates the employment of a generative adversarial neural network trained with received channel data to authenticate a transmitting device without prior knowledge of receiver noise .
Edit intent: clarity
Original: Texture synthesis models are important to understand visual processing.
Revised: Texture synthesis models are important tools for understanding visual processing.
Edit intent: clarity
Original:  In particular, statistical approaches based on neurally relevant features have been instrumental to understanding aspects of visual perception and of neural coding.
Revised:  In particular, statistical approaches based on neurally relevant features have been instrumental in understanding aspects of visual perception and of neural coding.
Edit intent: fluency
Original:  Then, we propose the natural geodesics ( i.e. the shortest path between two points) arising with the optimal transport metric to interpolate between arbitrary textures.
Revised:  Then, we propose the natural geodesics ( ie the shortest path between two points) arising with the optimal transport metric to interpolate between arbitrary textures.
Edit intent: coherence
Original:  We demonstrate our method by measuring the perceptual scale associated to the interpolation parameter in human observers, and the neural sensitivity of different areas of visual cortex in macaque monkeys.
Revised:  We apply our method by measuring the perceptual scale associated to the interpolation parameter in human observers, and the neural sensitivity of different areas of visual cortex in macaque monkeys.
Edit intent: clarity
Original:  To incorporate various aspects of human mobility, the team focused on the Social Distancing Index (SDI) that was calculated based on five basic mobility measures.
Revised:  To incorporate various aspects of human mobility, the team focused on the Social Distancing Index (SDI) which was calculated based on five basic mobility measures.
Edit intent: coherence
Original:   SDI patterns showed a plateau stage in the beginning of April that lasted for about two weeks.
Revised:  The SDI patterns showed a plateau stage in the beginning of April that lasted for about two weeks.
Edit intent: fluency
Original:  We called the latter observation : Quarantine Fatigue.
Revised:  We called the observation Quarantine Fatigue.
Edit intent: clarity
Original:  Our analysis showed that despite substantial differences between states , most states ' residents started experiencing a quarantine fatigue phenomenon in the same period.
Revised:  Our analysis showed that despite the existence of state-to-state variations , most states ' residents started experiencing a quarantine fatigue phenomenon in the same period.
Edit intent: clarity
Original:  Our analysis showed that despite substantial differences between states , most states ' residents started experiencing a quarantine fatigue phenomenon in the same period.
Revised:  Our analysis showed that despite substantial differences between states , most states started experiencing a quarantine fatigue phenomenon in the same period.
Edit intent: clarity
Original:  Our analysis showed that despite substantial differences between states , most states ' residents started experiencing a quarantine fatigue phenomenon in the same period.
Revised:  Our analysis showed that despite substantial differences between states , most states ' residents started experiencing a quarantine fatigue phenomenon during the same period.
Edit intent: clarity
Original:  This observation became more important by knowing that none of the states had officially announced the reopening until late April showing that people decide to loosen up their social distancing practices before the official reopening announcement.
Revised:  This observation became more important by knowing that none of the states had officially announced the reopening until late April showing that people decided to loosen up their social distancing practices before the official reopening announcement.
Edit intent: fluency
Original:  Moreover, our analysis indicated that official reopening led to a faster decline in SDI, raising the concern of a second wave of outbreak.
Revised:  Moreover, our analysis indicated that official reopening led to a rapid decline in SDI, raising the concern of a second wave of outbreak.
Edit intent: clarity
Original:  The synchronized trend among states also emphasizes the importance of a more nationwide decision making attitude for the future as the condition of each state depends on the behavior of other states .
Revised:  The synchronized trend among states also emphasizes the importance of a more nationwide decision-making attitude for the future as the condition of each state depends on the behavior of other states .
Edit intent: fluency
Original:  The synchronized trend among states also emphasizes the importance of a more nationwide decision making attitude for the future as the condition of each state depends on the behavior of other states .
Revised:  The synchronized trend among states also emphasizes the importance of a more nationwide decision making attitude for the future as the condition of each state depends on the nationwide behavior .
Edit intent: clarity
Original:  This result might be of independent interest to both communities .
Revised:  This result might be of independent interest .
Edit intent: clarity
Original:  3D Morphable Models (3DMMs) are powerful statistical tools for representing and modeling 3D faces .
Revised: The 3D Morphable Models (3DMMs) are powerful statistical tools for representing and modeling 3D faces .
Edit intent: style
Original:  3D Morphable Models (3DMMs) are powerful statistical tools for representing and modeling 3D faces .
Revised:  3D Morphable Model (3DMM) is a powerful statistical tool for representing 3D faces .
Edit intent: clarity
Original:  3D Morphable Models (3DMMs) are powerful statistical tools for representing and modeling 3D faces .
Revised:  3D Morphable Models (3DMMs) are powerful statistical tools for representing and modeling 3D face shapes .
Edit intent: clarity
Original:  To build a 3DMM, a training set of fully registered face scans is required, and its modeling capabilities directly depend on the variability contained in the training data.
Revised:  To build a 3DMM, a training set of scans in full point-to-point correspondence is required, and its modeling capabilities directly depend on the variability contained in the training data.
Edit intent: clarity
Original:  To build a 3DMM, a training set of fully registered face scans is required, and its modeling capabilities directly depend on the variability contained in the training data.
Revised:  To build a 3DMM, a training set of fully registered face scans is required, and its modeling capabilities directly depend on the variability of the training data.
Edit intent: clarity
Original:  In this manuscript, we present an approach that leverages a 3DMM to transfer its dense semantic annotation across a large set of heterogeneous 3D faces , establishing a dense correspondence between them.
Revised:  In this manuscript, we present an approach that leverages a 3DMM to establish a dense correspondence across raw 3D faces , establishing a dense correspondence between them.
Edit intent: clarity
Original:  As main outcome of this work, we build a heterogeneous, large-scale 3DMM from more than 9,000 fully registered scans obtained  joining the three datasets together .
Revised:  As main outcome of this work, we build a heterogeneous, large-scale 3DMM from more than 9,000 fully registered scans obtained by joining the three datasets together .
Edit intent: clarity
Original:  As main outcome of this work, we build a heterogeneous, large-scale 3DMM from more than 9,000 fully registered scans obtained  joining the three datasets together .
Revised:  As main outcome of this work, we build a heterogeneous, large-scale 3DMM from more than 9,000 fully registered scans obtained joining the three datasets .
Edit intent: clarity
Original:  To build a 3DMM, a training set of  scans in full point-to-point correspondence is required, and its modeling capabilities directly depend on the variability of the training data.
Revised:  To build a 3DMM, a training set of scans in full point-to-point correspondence is required, and its modeling capabilities directly depend on the variability contained in the training data.
Edit intent: clarity
Original:  Hence , to increase the descriptive power of a 3DMM, accurately establishing dense correspondence across heterogeneous scans with sufficient diversity in terms of identities, ethnicities, or expressions becomes essential.
Revised:  Thus , to increase the descriptive power of a 3DMM, accurately establishing dense correspondence across heterogeneous scans with sufficient diversity in terms of identities, ethnicities, or expressions becomes essential.
Edit intent: fluency
Original:  Hence , to increase the descriptive power of a 3DMM, accurately establishing dense correspondence across heterogeneous scans with sufficient diversity in terms of identities, ethnicities, or expressions becomes essential.
Revised:  Hence , to increase the descriptive power of the 3DMM, accurately establishing dense correspondence across heterogeneous scans with sufficient diversity in terms of identities, ethnicities, or expressions becomes essential.
Edit intent: fluency
Original:  Hence , to increase the descriptive power of a 3DMM, accurately establishing dense correspondence across heterogeneous scans with sufficient diversity in terms of identities, ethnicities, or expressions becomes essential.
Revised:  Hence , to increase the descriptive power of a 3DMM, establishing a dense correspondence across heterogeneous scans with sufficient diversity in terms of identities, ethnicities, or expressions becomes essential.
Edit intent: clarity
Original:  In this manuscript, we present a fully automatic approach that leverages a 3DMM to establish a dense correspondence across raw 3D faces  .
Revised:  In this manuscript, we present a fully automatic approach that leverages a 3DMM to transfer its dense semantic annotation across raw 3D faces .
Edit intent: clarity
Original:  We experimented our approach on three large and diverse datasets , showing it can effectively generalize to very different samples and accurately establish a dense correspondence even in presence of complex facial expressions.
Revised:  We experimented our approach , showing it can effectively generalize to very different samples and accurately establish a dense correspondence even in presence of complex facial expressions.
Edit intent: clarity
Original:  We experimented our approach on three large and diverse datasets , showing it can effectively generalize to very different samples and accurately establish a dense correspondence even in presence of complex facial expressions.
Revised:  We experimented our approach on three large and diverse datasets , showing it can effectively generalize to highly diverse samples and accurately establish a dense correspondence even in presence of complex facial expressions.
Edit intent: clarity
Original:  The accuracy of the dense registration is demonstrated by building a heterogeneous, large-scale 3DMM from more than 9,000 fully registered scans obtained by joining the three datasets .
Revised:  The accuracy of the dense registration is demonstrated by building a heterogeneous, large-scale 3DMM from more than 9,000 fully registered scans obtained by joining three large datasets together .
Edit intent: clarity