wanyu/IteraTeR_v2 huggingface dataset:
Original:  This paper presents a  segmentation approach for multi-story stands that strips the point cloud to its canopy layers, identifies individual tree segments within each layer using a DSM-based tree identification method as a building block , and combines the segments of immediate layers in order to fix potential over-segmentation of tree crowns across the layers.
Revised:  This paper presents a tree segmentation approach for multi-story stands that strips the point cloud to its canopy layers, identifies individual tree segments within each layer using a DSM-based tree identification method as a building block , and combines the segments of immediate layers in order to fix potential over-segmentation of tree crowns across the layers.
Edit intent: fluency
Original:  This paper presents a  segmentation approach for multi-story stands that strips the point cloud to its canopy layers, identifies individual tree segments within each layer using a DSM-based tree identification method as a building block , and combines the segments of immediate layers in order to fix potential over-segmentation of tree crowns across the layers.
Revised:  This paper presents a segmentation approach for multi-story stands that strips the point cloud and segments individual tree crowns within each layer using a DSM-based tree identification method as a building block , and combines the segments of immediate layers in order to fix potential over-segmentation of tree crowns across the layers.
Edit intent: clarity
Original:  We introduce local layering that analyzes the vertical distributions of LiDAR points within their local neighborhoods in order to locally determine the height thresholds for layering the canopy .
Revised:  We introduce local layering that analyzes the vertical distributions of LiDAR points within overlapping locales in order to locally determine the height thresholds for layering the canopy .
Edit intent: clarity
Original:  We introduce local layering that analyzes the vertical distributions of LiDAR points within their local neighborhoods in order to locally determine the height thresholds for layering the canopy .
Revised:  We introduce local layering that analyzes the vertical distributions of LiDAR points within their local neighborhoods in order to determine the local height thresholds for layering the canopy .
Edit intent: fluency
Original:  We introduce local layering that analyzes the vertical distributions of LiDAR points within their local neighborhoods in order to locally determine the height thresholds for layering the canopy .
Revised:  We introduce local layering that analyzes the vertical distributions of LiDAR points within their local neighborhoods in order to locally determine the height thresholds for stripping a canopy layer .
Edit intent: clarity
Original:  Unlike the previous work that stripped stiff layers within constrained areas, the local layering method strips flexible (in thickness and elevation) and narrower canopy layers within unconstrained areas  . Statistical analyses showed that layering in general strongly improves identifying (specifically under-story ) trees for the cost of moderately increasing over-segmentation rate of the identified trees.
Revised:  Unlike the previous work that stripped stiff layers within constrained areas, the local layering method strips flexible (in thickness and elevation) and narrower canopy layers within unconstrained areas . Statistical analyses showed that layering strongly improves detecting under-story ) trees for the cost of moderately increasing over-segmentation rate of the identified trees.
Edit intent: clarity
Original:  Unlike the previous work that stripped stiff layers within constrained areas, the local layering method strips flexible (in thickness and elevation) and narrower canopy layers within unconstrained areas  . Statistical analyses showed that layering in general strongly improves identifying (specifically under-story ) trees for the cost of moderately increasing over-segmentation rate of the identified trees.
Revised:  Unlike the previous work that stripped stiff layers within constrained areas, the local layering method strips flexible (in thickness and elevation) and narrower canopy layers within unconstrained areas . Statistical analyses showed that layering in general strongly improves identifying (specifically under-story trees at the cost of moderately increasing over-segmentation rate of the identified trees.
Edit intent: fluency
Original: Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure can be derived .
Revised: Airborne LiDAR point cloud representing a forest can be processed to derive vertical stand structure can be derived .
Edit intent: clarity
Original: Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure can be derived .
Revised: Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure .
Edit intent: clarity
Original:  This paper presents a tree segmentation approach for multi-story stands that iteratively strips canopy layers off the point cloud  and segments individual tree crowns within each layer using a digital surface model based tree segmentation method as a building block.
Revised:  This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud and segments individual tree crowns within each layer using a digital surface model based tree segmentation method as a building block.
Edit intent: clarity
Original:  Statistical analyses showed that layering strongly improves detecting under-story trees  at the cost of moderately increasing over-segmentation rate of the detected under-story trees , while only slightly affecting the segmentation quality of over-story trees.
Revised:  Statistical analyses showed that layering strongly improves detecting under-story trees at the cost of introducing a fair number of over-segmented under-story trees , while only slightly affecting the segmentation quality of over-story trees.
Edit intent: clarity
Original:  Results obtained from layering the canopy suggest that acquiring denser LiDAR point clouds ( becoming affordable due to advancements of the sensor technology and platforms ) would allow segmenting under-story trees as accurately as over-story trees.
Revised:  Results obtained from layering the canopy suggest that acquiring denser LiDAR point clouds , becoming affordable due to advancements of the sensor technology and platforms ) would allow segmenting under-story trees as accurately as over-story trees.
Edit intent: fluency
Original:  Results obtained from layering the canopy suggest that acquiring denser LiDAR point clouds ( becoming affordable due to advancements of the sensor technology and platforms ) would allow segmenting under-story trees as accurately as over-story trees.
Revised:  Results obtained from layering the canopy suggest that acquiring denser LiDAR point clouds ( becoming affordable due to advancements of the sensor technology and platforms , allows more improvements in segmenting under-story trees as accurately as over-story trees.
Edit intent: clarity
Original: Airborne LiDAR point cloud representing a forest can be processed to derive vertical stand structure  .
Revised: Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure .
Edit intent: clarity
Original:  The novelty of the approach is the stratification procedure that separates the point cloud to an over-story and multiple under-story tree canopy layers by analyzing vertical distributions of LiDAR points within overlapping locales.
Revised:  The novelty of the approach is the stratification procedure that separates the point cloud to an overstory and multiple understory tree canopy layers by analyzing vertical distributions of LiDAR points within overlapping locales.
Edit intent: fluency
Original:  Unlike previous work that stripped stiff layers within a constrained area, the procedure stratifies the point cloud to flexible tree canopy layers over an unconstrained area with minimal over/under-segmentations of tree crowns across the layers.
Edit intent: coherence
Original:  The procedure does not make a priori assumptions about the shape and size of the tree crowns and can, independent of the tree segmentation method, be utilized to vertically stratify tree crowns of forest canopies with a variety of stand structures .
Revised:  The procedure does not make a priori assumptions about the shape and size of the tree crowns and can, independent of the tree segmentation method, be utilized to vertically stratify tree crowns of forest canopies .
Edit intent: clarity
Original:  We applied the proposed approach to the University of Kentucky Robinson Forest , a natural deciduous forest with complex  terrain and vegetation structure.
Revised:  We applied the proposed approach to the University of Kentucky Robinson Forest - a natural deciduous forest with complex terrain and vegetation structure.
Edit intent: fluency
Original:  The segmentation results showed that using the stratification procedure strongly improved detecting under-story trees (from 46\% to 68\%) at the cost of introducing a fair number of over-segmented under-story trees (increased from 1\% to 16\%), while barely affecting the  segmentation quality of over-story trees.
Revised:  The segmentation results showed that using the stratification procedure strongly improved detecting understory trees (from 46\% to 68\%) at the cost of introducing a fair number of over-segmented under-story trees (increased from 1\% to 16\%), while barely affecting the segmentation quality of over-story trees.
Edit intent: fluency
Original:  The segmentation results showed that using the stratification procedure strongly improved detecting under-story trees (from 46\% to 68\%) at the cost of introducing a fair number of over-segmented under-story trees (increased from 1\% to 16\%), while barely affecting the  segmentation quality of over-story trees.
Revised:  The segmentation results showed that using the stratification procedure strongly improved detecting under-story trees (from 46\% to 68\%) at the cost of introducing a fair number of over-segmented understory trees (increased from 1\% to 16\%), while barely affecting the segmentation quality of over-story trees.
Edit intent: fluency
Original:  The segmentation results showed that using the stratification procedure strongly improved detecting under-story trees (from 46\% to 68\%) at the cost of introducing a fair number of over-segmented under-story trees (increased from 1\% to 16\%), while barely affecting the  segmentation quality of over-story trees.
Revised:  The segmentation results showed that using the stratification procedure strongly improved detecting under-story trees (from 46\% to 68\%) at the cost of introducing a fair number of over-segmented under-story trees (increased from 1\% to 16\%), while barely affecting the overall segmentation quality of over-story trees.
Edit intent: clarity
Original:  The segmentation results showed that using the stratification procedure strongly improved detecting under-story trees (from 46\% to 68\%) at the cost of introducing a fair number of over-segmented under-story trees (increased from 1\% to 16\%), while barely affecting the  segmentation quality of over-story trees.
Revised:  The segmentation results showed that using the stratification procedure strongly improved detecting under-story trees (from 46\% to 68\%) at the cost of introducing a fair number of over-segmented under-story trees (increased from 1\% to 16\%), while barely affecting the segmentation quality of overstory trees.
Edit intent: fluency
Original:  Results of vertical stratification of  canopy showed that the point density of under-story canopy layers were suboptimal for performing  reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds , becoming affordable due to advancements of the sensor technology and platforms, allows more improvements in segmenting under-story trees .
Revised:  Results of vertical stratification of the canopy showed that the point density of under-story canopy layers were suboptimal for performing reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds , becoming affordable due to advancements of the sensor technology and platforms, allows more improvements in segmenting under-story trees .
Edit intent: fluency
Original:  Results of vertical stratification of  canopy showed that the point density of under-story canopy layers were suboptimal for performing  reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds , becoming affordable due to advancements of the sensor technology and platforms, allows more improvements in segmenting under-story trees .
Revised:  Results of vertical stratification of canopy showed that the point density of understory canopy layers were suboptimal for performing reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds , becoming affordable due to advancements of the sensor technology and platforms, allows more improvements in segmenting under-story trees .
Edit intent: fluency
Original:  Results of vertical stratification of  canopy showed that the point density of under-story canopy layers were suboptimal for performing  reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds , becoming affordable due to advancements of the sensor technology and platforms, allows more improvements in segmenting under-story trees .
Revised:  Results of vertical stratification of canopy showed that the point density of under-story canopy layers were suboptimal for performing a reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds , becoming affordable due to advancements of the sensor technology and platforms, allows more improvements in segmenting under-story trees .
Edit intent: fluency
Original:  Results of vertical stratification of  canopy showed that the point density of under-story canopy layers were suboptimal for performing  reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds , becoming affordable due to advancements of the sensor technology and platforms, allows more improvements in segmenting under-story trees .
Revised:  Results of vertical stratification of canopy showed that the point density of under-story canopy layers were suboptimal for performing reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds would allow more improvements in segmenting under-story trees .
Edit intent: clarity
Original:  We cover three primary classes of incentive mechanisms , namely auctions, lotteries, and trust and reputation systems, as well as three other frameworks of promising potential , namely bargaining games, contract theory, and market-driven mechanisms.
Revised:  We cover three primary classes of incentive mechanisms : auctions, lotteries, and trust and reputation systems, as well as three other frameworks of promising potential , namely bargaining games, contract theory, and market-driven mechanisms.
Edit intent: fluency
Original:  We cover three primary classes of incentive mechanisms , namely auctions, lotteries, and trust and reputation systems, as well as three other frameworks of promising potential , namely bargaining games, contract theory, and market-driven mechanisms.
Revised:  We cover three primary classes of incentive mechanisms , namely auctions, lotteries, and trust and reputation systems, as well as three other frameworks of promising potential : bargaining games, contract theory, and market-driven mechanisms.
Edit intent: fluency
Original: We investigate the relationship between social structure and sentiment through the analysis of half a million tweets about the Irish Marriage Referendum of 2015.
Revised: We examine the relationship between social structure and sentiment through the analysis of half a million tweets about the Irish Marriage Referendum of 2015.
Edit intent: clarity
Original: We investigate the relationship between social structure and sentiment through the analysis of half a million tweets about the Irish Marriage Referendum of 2015.
Revised: We investigate the relationship between social structure and sentiment through the analysis of a large collection of tweets about the Irish Marriage Referendum of 2015.
Edit intent: clarity
Original:  We obtain the sentiment of every tweet with the hashtags #marref and #marriageref  posted in the days leading to the referendum, and construct networks to aggregate sentiment and  study the interactions among users.
Revised:  We obtain the sentiment of every tweet with the hashtags #marref and #marriageref posted in the days leading to the referendum, and construct networks to aggregate sentiment and use it to study the interactions among users.
Edit intent: clarity
Original:  The sentiment of the mention tweets that a user sends is correlated with the sentiment of the mentions received , and there are significantly more connections between users with similar sentiment scores than among users with opposite scores  .
Revised:  Our results show that the sentiment of mention tweets posted by users is correlated with the sentiment of the mentions received , and there are significantly more connections between users with similar sentiment scores than among users with opposite scores .
Edit intent: clarity
Original:  The sentiment of the mention tweets that a user sends is correlated with the sentiment of the mentions received , and there are significantly more connections between users with similar sentiment scores than among users with opposite scores  .
Revised:  The sentiment of the mention tweets that a user sends is correlated with the sentiment of received mentions , and there are significantly more connections between users with similar sentiment scores than among users with opposite scores .
Edit intent: clarity
Original:  We combine the community structure of the follower and mention networks , the activity level of the users , and sentiment scores to find groups of users who support voting ' yes' or 'no' on the referendum.
Revised:  We combine the community structure of the two networks with the activity level of the users , and sentiment scores to find groups of users who support voting ' yes' or 'no' on the referendum.
Edit intent: clarity
Original:  We combine the community structure of the follower and mention networks , the activity level of the users , and sentiment scores to find groups of users who support voting ' yes' or 'no' on the referendum.
Revised:  We combine the community structure of the follower and mention networks , the activity level of the users and sentiment scores to find groups of users who support voting ' yes' or 'no' on the referendum.
Edit intent: fluency
Original:  We combine the community structure of the follower and mention networks , the activity level of the users , and sentiment scores to find groups of users who support voting ' yes' or 'no' on the referendum.
Revised:  We combine the community structure of the follower and mention networks , the activity level of the users , and sentiment scores to find groups of users who support voting ` yes' or 'no' on the referendum.
Edit intent: fluency
Original:  We combine the community structure of the follower and mention networks , the activity level of the users , and sentiment scores to find groups of users who support voting ' yes' or 'no' on the referendum.
Revised:  We combine the community structure of the follower and mention networks , the activity level of the users , and sentiment scores to find groups of users who support voting ' yes' or `no' in the referendum.
Edit intent: fluency
Original:  We find that many conversations between users on opposing sides of the debate occurred in the absence of follower connections, suggesting that there were efforts by some users to establish dialogue and debate across ideological divisions.
Revised:  There were numerous conversations between users on opposing sides of the debate occurred in the absence of follower connections, suggesting that there were efforts by some users to establish dialogue and debate across ideological divisions.
Edit intent: clarity
Original:  We find that many conversations between users on opposing sides of the debate occurred in the absence of follower connections, suggesting that there were efforts by some users to establish dialogue and debate across ideological divisions.
Revised:  We find that many conversations between users on opposing sides of the debate in the absence of follower connections, suggesting that there were efforts by some users to establish dialogue and debate across ideological divisions.
Edit intent: clarity
Original:  We find that many conversations between users on opposing sides of the debate occurred in the absence of follower connections, suggesting that there were efforts by some users to establish dialogue and debate across ideological divisions.
Revised:  We find that many conversations between users on opposing sides of the debate occurred in the absence of follower connections, which suggests that there were efforts by some users to establish dialogue and debate across ideological divisions.
Edit intent: coherence
Original:  These results show that social structures can be successfully integrated with sentiment to analyse and understand the disposition of social media users.
Revised:  Our analysis shows that social structure can be integrated successfully with sentiment to analyse and understand the disposition of social media users.
Edit intent: clarity
Original:  We discuss the implications of our work for the integration of data and meta-data , opinion dynamics, public opinion modelling  and polling.
Revised:  These results have potential applications in the integration of data and meta-data , opinion dynamics, public opinion modelling and polling.
Edit intent: clarity
Original:  We discuss the implications of our work for the integration of data and meta-data , opinion dynamics, public opinion modelling  and polling.
Revised:  We discuss the implications of our work for the integration of data and meta-data to study opinion dynamics, public opinion modelling and polling.
Edit intent: clarity
Original:  We discuss the implications of our work for the integration of data and meta-data , opinion dynamics, public opinion modelling  and polling.
Revised:  We discuss the implications of our work for the integration of data and meta-data , opinion dynamics, public opinion modelling , and polling.
Edit intent: fluency
Original: Biological cells are the preeminent active matter.
Revised: Biological cells are the prototypical example of active matter.
Edit intent: clarity
Original:  Cells sense and respond to mechanical, chemical or electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, changes in chemical uptake or secretion, cell differentiation, proliferation, death, or migration.
Revised:  Cells sense and respond to mechanical, chemical and electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, changes in chemical uptake or secretion, cell differentiation, proliferation, death, or migration.
Edit intent: fluency
Original:  Cells sense and respond to mechanical, chemical or electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, changes in chemical uptake or secretion, cell differentiation, proliferation, death, or migration.
Revised:  Cells sense and respond to mechanical, chemical or electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, chemical uptake and secretion, cell differentiation, proliferation, death, or migration.
Edit intent: clarity