wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show - theoretically and experimentally - that fulfillment of both principles offers several benefits for evaluation of AMR-to-text systems, including the explainability of scores \mathcal{F}_\beta does not necessarily rely on gold AMRs, it may extend to other text generation tasks} .
Revised:  We show - theoretically and experimentally - that fulfillment of both principles offers several benefits for evaluation of AMR-to-text evaluation, including explainability of scores \mathcal{F}_\beta does not necessarily rely on gold AMRs, it may extend to other text generation tasks} .
Edit intent: clarity
Original:  We show - theoretically and experimentally - that fulfillment of both principles offers several benefits for evaluation of AMR-to-text systems, including the explainability of scores \mathcal{F}_\beta does not necessarily rely on gold AMRs, it may extend to other text generation tasks} .
Revised:  We show - theoretically and experimentally - that fulfillment of both principles offers several benefits for evaluation of AMR-to-text systems, including the explainability of scores . Since \mathcal{M\mathcal{F}_\beta does not necessarily rely on gold AMRs, it may extend to other text generation tasks} .
Edit intent: meaning-changed
Original:  In this paper,we address this gap by developing GraphReach, a position-aware , inductive GNN . GraphReach captures the global positions of nodes though reachability estimations with respect to a set of nodes called anchors.
Revised:  In this paper,we develop GraphReach, a position-aware , inductive GNN . GraphReach captures the global positions of nodes though reachability estimations with respect to a set of nodes called anchors.
Edit intent: coherence
Original:  In this paper,we address this gap by developing GraphReach, a position-aware , inductive GNN . GraphReach captures the global positions of nodes though reachability estimations with respect to a set of nodes called anchors.
Revised:  In this paper,we address this gap by developing GraphReach, a position-aware inductive GNN that captures the global positions of nodes though reachability estimations with respect to a set of nodes called anchors.
Edit intent: fluency
Original:  In this paper,we address this gap by developing GraphReach, a position-aware , inductive GNN . GraphReach captures the global positions of nodes though reachability estimations with respect to a set of nodes called anchors.
Revised:  In this paper,we address this gap by developing GraphReach, a position-aware , inductive GNN . GraphReach captures the global positions of nodes through reachability estimations with respect to a set of nodes called anchors.
Edit intent: fluency
Original:  In this paper,we address this gap by developing GraphReach, a position-aware , inductive GNN . GraphReach captures the global positions of nodes though reachability estimations with respect to a set of nodes called anchors. The reachability estimations compute the frequency with which a node may visit an anchor through any possible path. The anchors are strategically selected so that the reachability estimations across all  nodes are maximized.
Revised:  In this paper,we address this gap by developing GraphReach, a position-aware , inductive GNN . GraphReach captures the global positions of nodes though reachability estimations with respect to a set of anchor nodes. The anchors are strategically selected so that the reachability estimations across all nodes are maximized.
Edit intent: coherence
Original:  We show that this combinatorial anchor selection problem is NP-hard and  consequently, develop a greedy (1-1/e) approximation . An extensive experimental evaluation covering six datasets and five state-of-the-art GNN architectures reveal that GraphReach is consistently superior and provides up to 40\% relative improvement in the predictive tasks of link prediction and pairwise node classification .
Revised:  We show that this combinatorial anchor selection problem is NP-hard and consequently, develop a greedy (1-1/e) approximation heuristic. Empirical evaluation against state-of-the-art GNN architectures reveal that GraphReach is consistently superior and provides up to 40\% relative improvement in the predictive tasks of link prediction and pairwise node classification .
Edit intent: clarity
Original:  An extensive experimental evaluation covering six datasets and five state-of-the-art GNN architectures reveal that GraphReach is consistently superior and provides up to 40\% relative improvement in the predictive tasks of link prediction and pairwise node classification .
Revised:  An extensive experimental evaluation covering six datasets and five state-of-the-art GNN architectures reveal that GraphReach provides up to 40\% relative improvement in the predictive tasks of link prediction and pairwise node classification .
Edit intent: coherence
Original:  An extensive experimental evaluation covering six datasets and five state-of-the-art GNN architectures reveal that GraphReach is consistently superior and provides up to 40\% relative improvement in the predictive tasks of link prediction and pairwise node classification .
Revised:  An extensive experimental evaluation covering six datasets and five state-of-the-art GNN architectures reveal that GraphReach is consistently superior and provides up to 40\% relative improvement in accuracy .
Edit intent: clarity
Original: Semantic segmentation of neuronal structures in 3D high-resolution fluorescence microscopy imaging of the human brain cortexcan take advantage of bidimensional CNNs, which yield good resultsin neuron localization but lead to inaccurate surface reconstruction.
Revised: Semantic segmentation of neuronal structures in 3D high-resolution fluorescence microscopy imaging of the human brain cortex can take advantage of bidimensional CNNs, which yield good resultsin neuron localization but lead to inaccurate surface reconstruction.
Edit intent: fluency
Original: Semantic segmentation of neuronal structures in 3D high-resolution fluorescence microscopy imaging of the human brain cortexcan take advantage of bidimensional CNNs, which yield good resultsin neuron localization but lead to inaccurate surface reconstruction.
Revised: Semantic segmentation of neuronal structures in 3D high-resolution fluorescence microscopy imaging of the human brain cortexcan take advantage of bidimensional CNNs, which yield good results in neuron localization but lead to inaccurate surface reconstruction.
Edit intent: fluency
Original:  Semi-supervised alternative strategies which make use only of sparse anno-tations suffer from longer training times and achieved models tend tohave increased capacity compared to 2D CNNs, needing more groundtruth data to attain similar results.
Revised:  Semi-supervised alternative strategies which make use only of sparse annotations suffer from longer training times and achieved models tend tohave increased capacity compared to 2D CNNs, needing more groundtruth data to attain similar results.
Edit intent: fluency
Original:  Semi-supervised alternative strategies which make use only of sparse anno-tations suffer from longer training times and achieved models tend tohave increased capacity compared to 2D CNNs, needing more groundtruth data to attain similar results.
Revised:  Semi-supervised alternative strategies which make use only of sparse anno-tations suffer from longer training times and achieved models tend to have increased capacity compared to 2D CNNs, needing more groundtruth data to attain similar results.
Edit intent: fluency
Original:  Semi-supervised alternative strategies which make use only of sparse anno-tations suffer from longer training times and achieved models tend tohave increased capacity compared to 2D CNNs, needing more groundtruth data to attain similar results.
Revised:  Semi-supervised alternative strategies which make use only of sparse anno-tations suffer from longer training times and achieved models tend tohave increased capacity compared to 2D CNNs, needing more ground truth data to attain similar results.
Edit intent: fluency
Original:  To overcome these issues we proposea two-phase strategy for training native 3D CNN models on sparse 2Dannotations where missing labels are inferred by a 2D CNN model andcombined with manual annotations in a weighted manner during losscalculation .
Revised:  To overcome these issues we propose a two-phase strategy for training native 3D CNN models on sparse 2Dannotations where missing labels are inferred by a 2D CNN model andcombined with manual annotations in a weighted manner during losscalculation .
Edit intent: fluency
Original:  To overcome these issues we proposea two-phase strategy for training native 3D CNN models on sparse 2Dannotations where missing labels are inferred by a 2D CNN model andcombined with manual annotations in a weighted manner during losscalculation .
Revised:  To overcome these issues we proposea two-phase strategy for training native 3D CNN models on sparse 2D annotations where missing labels are inferred by a 2D CNN model andcombined with manual annotations in a weighted manner during losscalculation .
Edit intent: fluency
Original:  To overcome these issues we proposea two-phase strategy for training native 3D CNN models on sparse 2Dannotations where missing labels are inferred by a 2D CNN model andcombined with manual annotations in a weighted manner during losscalculation .
Revised:  To overcome these issues we proposea two-phase strategy for training native 3D CNN models on sparse 2Dannotations where missing labels are inferred by a 2D CNN model and combined with manual annotations in a weighted manner during losscalculation .
Edit intent: fluency
Original:  To overcome these issues we proposea two-phase strategy for training native 3D CNN models on sparse 2Dannotations where missing labels are inferred by a 2D CNN model andcombined with manual annotations in a weighted manner during losscalculation .
Revised:  To overcome these issues we proposea two-phase strategy for training native 3D CNN models on sparse 2Dannotations where missing labels are inferred by a 2D CNN model andcombined with manual annotations in a weighted manner during loss calculation .
Edit intent: fluency
Original:  However, seldom has been done to improve the protein representation learning, while current approaches have manifest flaw of lacking the ability of learning amino acids long-distance interactions , which are essential for determine the proteins properties due to protein folding.
Revised:  However, seldom has been done to improve the protein representation learning, while current approaches have manifest flaw of lacking the ability of learning amino acids long-distance interactions , which are essential for characterizing the structural properties arising from the protein folding.
Edit intent: clarity
Original:  And such approach can be universally applied to nearly any deep learning models or submodels for protein representationlearning. By applying such approach on an existing CPI model, the modified version, with our proposed Protein Transformer as its protein learning module, has a significant improvement in the prediction performance . Practical tips for training Protein Transformer are also provided .
Revised:  The proposed module concerning protein representation, called Protein Transformer, with an integration with an existing CPI model, the modified version, with our proposed Protein Transformer as its protein learning module, has a significant improvement in the prediction performance . Practical tips for training Protein Transformer are also provided .
Edit intent: clarity
Original:  By applying such approach on an existing CPI model, the modified version, with our proposed Protein Transformer as its protein learning module, has a significant improvement in the prediction performance . Practical tips for training Protein Transformer are also provided .
Revised:  By applying such approach on an existing CPI model, the modified version, with our proposed Protein Transformer as its protein learning module, has a significant improvement in the prediction performance when compared with several existing CPI models .
Edit intent: clarity
Original: Data dependent triangulations are triangulations of point sets in the plane that are computed under consideration not only of the points' x- and y-coordinates but also of additional data (e.g. , elevation ).
Revised: Reconstructions of sea level prior to the satellite altimeter era are usually derived from tide gauge records; however most algorithms for this assume that modes of sea level variability are stationary which is not true over several decades. Here we suggest a method that is based on optimized data-dependent triangulations of the network of gauge stations. Data-dependent triangulations are triangulations of point sets in the plane that are computed under consideration not only of the points' x- and y-coordinates but also of additional data (e.g. , elevation ).
Edit intent: meaning-changed
Original: Data dependent triangulations are triangulations of point sets in the plane that are computed under consideration not only of the points' x- and y-coordinates but also of additional data (e.g. , elevation ).
Revised: Data dependent triangulations are triangulations of point sets in the plane that are computed under consideration not only of the points' x- and y-coordinates but also of additional data (e.g. elevation, anomalies ).
Edit intent: meaning-changed
Original:  In particular, min-error criteria have been suggested to compute triangulations that approximate a given surface. In this article, we show how data dependent triangulations with min-error criteria can be used to reconstruct a dynamic surface over a longer time period if height values are continuously monitored at a sparse set of stations and, additionally for few epochs or a single one an accurate reference surface is given.
Revised:  In this article, we show how data dependent triangulations with min-error criteria can be used to reconstruct a dynamic surface over a longer time period if height values are continuously monitored at a sparse set of stations and, additionally for few epochs or a single one an accurate reference surface is given.
Edit intent: clarity
Original:  In this article, we show how data dependent triangulations with min-error criteria can be used to reconstruct a dynamic surface over a longer time period if height values are continuously monitored at a sparse set of stations and, additionally for few epochs or a single one an accurate reference surface is given.
Revised:  In this article, we show how data-dependent triangulations with min-error criteria can be used to reconstruct a dynamic surface over a longer time period if height values are continuously monitored at a sparse set of stations and, additionally for few epochs or a single one an accurate reference surface is given.
Edit intent: fluency
Original:  In this article, we show how data dependent triangulations with min-error criteria can be used to reconstruct a dynamic surface over a longer time period if height values are continuously monitored at a sparse set of stations and, additionally for few epochs or a single one an accurate reference surface is given.
Revised:  In this article, we show how data dependent triangulations with min-error criteria can be used to reconstruct 2D maps of the sea surface anomaly over a longer time period if height values are continuously monitored at a sparse set of stations and, additionally for few epochs or a single one an accurate reference surface is given.
Edit intent: meaning-changed
Original:  In this article, we show how data dependent triangulations with min-error criteria can be used to reconstruct a dynamic surface over a longer time period if height values are continuously monitored at a sparse set of stations and, additionally for few epochs or a single one an accurate reference surface is given.
Revised:  In this article, we show how data dependent triangulations with min-error criteria can be used to reconstruct a dynamic surface over a longer time period , assuming that height anomalies are continuously monitored at a sparse set of stations and, additionally for few epochs or a single one an accurate reference surface is given.
Edit intent: clarity
Original:  In this article, we show how data dependent triangulations with min-error criteria can be used to reconstruct a dynamic surface over a longer time period if height values are continuously monitored at a sparse set of stations and, additionally for few epochs or a single one an accurate reference surface is given.
Revised:  In this article, we show how data dependent triangulations with min-error criteria can be used to reconstruct a dynamic surface over a longer time period if height values are continuously monitored at a sparse set of stations and, in addition, observations of a reference surface is given.
Edit intent: clarity
Original:  In this article, we show how data dependent triangulations with min-error criteria can be used to reconstruct a dynamic surface over a longer time period if height values are continuously monitored at a sparse set of stations and, additionally for few epochs or a single one an accurate reference surface is given. The basic idea is to learn an height-optimal triangulation based on the available reference data  and to use the learned triangulation  to compute piece-wise linear surface models for epochs in which only the observations of the monitoring stations are given.
Revised:  In this article, we show how data dependent triangulations with min-error criteria can be used to reconstruct a dynamic surface over a longer time period if height values are continuously monitored at a sparse set of stations and, additionally for few epochs or a single one an accurate reference surface is provided over a shorter time period. At the heart of our method is the idea to learn a min-error triangulation based on the available reference data and to use the learned triangulation to compute piece-wise linear surface models for epochs in which only the observations of the monitoring stations are given.
Edit intent: clarity
Original:  The basic idea is to learn an height-optimal triangulation based on the available reference data  and to use the learned triangulation  to compute piece-wise linear surface models for epochs in which only the observations of the monitoring stations are given.
Revised:  The basic idea is to learn an height-optimal triangulation based on the available reference data , and to use the learned triangulation to compute piece-wise linear surface models for epochs in which only the observations of the monitoring stations are given.
Edit intent: fluency
Original:  The basic idea is to learn an height-optimal triangulation based on the available reference data  and to use the learned triangulation  to compute piece-wise linear surface models for epochs in which only the observations of the monitoring stations are given.
Revised:  The basic idea is to learn an height-optimal triangulation based on the available reference data and to use the learned triangulation subsequently to compute piece-wise linear surface models for epochs in which only the observations of the monitoring stations are given.
Edit intent: clarity
Original:  The basic idea is to learn an height-optimal triangulation based on the available reference data  and to use the learned triangulation  to compute piece-wise linear surface models for epochs in which only the observations of the monitoring stations are given.
Revised:  The basic idea is to learn an height-optimal triangulation based on the available reference data and to use the learned triangulation to compute piece-wise linear surface models for epochs in which only observations from monitoring stations are given.
Edit intent: clarity
Original:  We show how to learn a height-optimal triangulation and a  k-order height-optimal triangulation using an exact algorithm based on integer linear programming and evaluate our approaches both with respect to its running times and the quality of the reconstructions in contrast to a solution by a Delaunay triangulationwhich has earlier been used for sea-surface modeling.
Revised:  We show how to learn a min-error triangulation and a k-order height-optimal triangulation using an exact algorithm based on integer linear programming and evaluate our approaches both with respect to its running times and the quality of the reconstructions in contrast to a solution by a Delaunay triangulationwhich has earlier been used for sea-surface modeling.
Edit intent: meaning-changed
Original:  We show how to learn a height-optimal triangulation and a  k-order height-optimal triangulation using an exact algorithm based on integer linear programming and evaluate our approaches both with respect to its running times and the quality of the reconstructions in contrast to a solution by a Delaunay triangulationwhich has earlier been used for sea-surface modeling.
Revised:  We show how to learn a height-optimal triangulation and a min-error k-order height-optimal triangulation using an exact algorithm based on integer linear programming and evaluate our approaches both with respect to its running times and the quality of the reconstructions in contrast to a solution by a Delaunay triangulationwhich has earlier been used for sea-surface modeling.
Edit intent: meaning-changed
Original:  We show how to learn a height-optimal triangulation and a  k-order height-optimal triangulation using an exact algorithm based on integer linear programming and evaluate our approaches both with respect to its running times and the quality of the reconstructions in contrast to a solution by a Delaunay triangulationwhich has earlier been used for sea-surface modeling.
Revised:  We show how to learn a height-optimal triangulation and a k-order Delaunay triangulation using integer linear programming and evaluate our approaches both with respect to its running times and the quality of the reconstructions in contrast to a solution by a Delaunay triangulationwhich has earlier been used for sea-surface modeling.
Edit intent: clarity
Original:  We show how to learn a height-optimal triangulation and a  k-order height-optimal triangulation using an exact algorithm based on integer linear programming and evaluate our approaches both with respect to its running times and the quality of the reconstructions in contrast to a solution by a Delaunay triangulationwhich has earlier been used for sea-surface modeling. For existing data of the North Sea we show that we can better reconstruct the dynamic sea surface for about 156 months back in time using the height-optimal triangulation than the Delaunay triangulation and even for about 180 months using the k-order height-optimal triangulation for k=2.
Revised:  We show how to learn a height-optimal triangulation and a k-order height-optimal triangulation using an exact algorithm based on integer linear programming . We confront our reconstructions against the Delaunay triangulation. With real data for the North Sea we show that we can better reconstruct the dynamic sea surface for about 156 months back in time using the height-optimal triangulation than the Delaunay triangulation and even for about 180 months using the k-order height-optimal triangulation for k=2.
Edit intent: clarity
Original:  For existing data of the North Sea we show that we can better reconstruct the dynamic sea surface for about 156 months back in time using the height-optimal triangulation than the Delaunay triangulation and even for about 180 months using the k-order height-optimal triangulation for k=2.
Revised:  For existing data of the North Sea we show that the min-error triangulation outperforms the Delaunay method significantly for reconstructions back in time using the height-optimal triangulation than the Delaunay triangulation and even for about 180 months using the k-order height-optimal triangulation for k=2.
Edit intent: meaning-changed