wanyu/IteraTeR_v2 huggingface dataset:
Original:  Therefore, we developed a new cross-modality educed distillation (CMEDL) approach, using unpaired CT and MRI scans, whereby a teacher MRI network guides a student CT network to extract features that signal the difference between foreground and background.
Revised:  Therefore, we developed a new cross-modality educed distillation (CMEDL) approach, using unpaired CT and MRI scans, whereby an informative teacher MRI network guides a student CT network to extract features that signal the difference between foreground and background.
Edit intent: clarity
Original: Currently, in the numerical linear algebra community, it is thought that to obtain nearly-optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns  , regression, low rank approximation, maximum matching on general graphs and linear matroid union, one would need to resolve the main open question of Nelson and Nguyen (FOCS, 2013)
Revised: In the numerical linear algebra community, it is thought that to obtain nearly-optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns , regression, low rank approximation, maximum matching on general graphs and linear matroid union, one would need to resolve the main open question of Nelson and Nguyen (FOCS, 2013)
Edit intent: coherence
Original: Currently, in the numerical linear algebra community, it is thought that to obtain nearly-optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns  , regression, low rank approximation, maximum matching on general graphs and linear matroid union, one would need to resolve the main open question of Nelson and Nguyen (FOCS, 2013)
Revised: Currently, in the numerical linear algebra community, it was suggested that to obtain nearly-optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns , regression, low rank approximation, maximum matching on general graphs and linear matroid union, one would need to resolve the main open question of Nelson and Nguyen (FOCS, 2013)
Edit intent: clarity
Original: Currently, in the numerical linear algebra community, it is thought that to obtain nearly-optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns  , regression, low rank approximation, maximum matching on general graphs and linear matroid union, one would need to resolve the main open question of Nelson and Nguyen (FOCS, 2013)
Revised: Currently, in the numerical linear algebra community, it is thought that to obtain nearly optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns , regression, low rank approximation, maximum matching on general graphs and linear matroid union, one would need to resolve the main open question of Nelson and Nguyen (FOCS, 2013)
Edit intent: fluency
Original: Currently, in the numerical linear algebra community, it is thought that to obtain nearly-optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns  , regression, low rank approximation, maximum matching on general graphs and linear matroid union, one would need to resolve the main open question of Nelson and Nguyen (FOCS, 2013)
Revised: Currently, in the numerical linear algebra community, it is thought that to obtain nearly-optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns , regression, or low-rank approximation, a natural way would be to resolve the main open question of Nelson and Nguyen (FOCS, 2013)
Edit intent: clarity
Original:   regarding the logarithmic factors in the sketching dimension for existing constant factor approximation oblivious subspace embeddings  .
Revised:  . This question is regarding the logarithmic factors in the sketching dimension for existing constant factor approximation oblivious subspace embeddings .
Edit intent: coherence
Original:   regarding the logarithmic factors in the sketching dimension for existing constant factor approximation oblivious subspace embeddings  .
Revised:  regarding the logarithmic factors in the sketching dimension of existing oblivious subspace embeddings .
Edit intent: clarity
Original:  A key technique we use is an explicit mapping of Indyk based on uncertainty principles and extractors, which after first applying known oblivious subspace embeddings, allows us to quickly spread out the mass of the vector so that sampling is now effective , and we avoid a logarithmic factor  that is standard in the sketching dimension resulting from matrix Chernoff bounds .
Revised:  A key technique we use is an explicit mapping of Indyk based on uncertainty principles and extractors, which after first applying known oblivious subspace embeddings, allows us to quickly spread out the mass of the vector so that sampling is now effective . We thereby avoid a logarithmic factor that is standard in the sketching dimension resulting from matrix Chernoff bounds .
Edit intent: coherence
Original:  A key technique we use is an explicit mapping of Indyk based on uncertainty principles and extractors, which after first applying known oblivious subspace embeddings, allows us to quickly spread out the mass of the vector so that sampling is now effective , and we avoid a logarithmic factor  that is standard in the sketching dimension resulting from matrix Chernoff bounds .
Revised:  A key technique we use is an explicit mapping of Indyk based on uncertainty principles and extractors, which after first applying known oblivious subspace embeddings, allows us to quickly spread out the mass of the vector so that sampling is now effective , and we avoid a logarithmic factor in the sketching dimension that is standard in the sketching dimension resulting from matrix Chernoff bounds .
Edit intent: fluency
Original:  A key technique we use is an explicit mapping of Indyk based on uncertainty principles and extractors, which after first applying known oblivious subspace embeddings, allows us to quickly spread out the mass of the vector so that sampling is now effective , and we avoid a logarithmic factor  that is standard in the sketching dimension resulting from matrix Chernoff bounds .
Revised:  A key technique we use is an explicit mapping of Indyk based on uncertainty principles and extractors, which after first applying known oblivious subspace embeddings, allows us to quickly spread out the mass of the vector so that sampling is now effective , and we avoid a logarithmic factor that is standard in bounds proven using the matrix Chernoff inequality .
Edit intent: clarity
Original:  For the fundamental problems of rank computation and finding a linearly independent subset of columns , our algorithms improve Cheung, Kwok, and Lau (JACM, 2013)  and are optimal to within a constant factor and a \log\log(n )-factor, respectively.
Revised:  For the fundamental problems of rank computation and finding a linearly independent subset of columns , our algorithms improve Cheung, Kwok, and Lau (JACM, 2013) , and are optimal to within a constant factor and a \log\log(n )-factor, respectively.
Edit intent: fluency
Original:  Further, for constant factor regression and low rank approximation we give the first optimal algorithms, for the current matrix multiplication exponent.
Revised:  Further, for constant-factor regression and low-rank approximation we give the first optimal algorithms, for the current matrix multiplication exponent.
Edit intent: fluency
Original: Although deep learning has demonstrated astonishing performance in many applications, there are still concerns on their dependability.
Revised: Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability.
Edit intent: fluency
Original:  Unfortunately, discrimination might be intrinsically embedded into the models due to  discrimination in the training data.
Revised:  Unfortunately, discrimination might be intrinsically embedded into the models due to the discrimination in the training data.
Edit intent: fluency
Original:  As a countermeasure, fairness testing systemically identifies discriminative samples, which can be used to retrain the model and improve its fairness.
Revised:  As a countermeasure, fairness testing systemically identifies discriminatory samples, which can be used to retrain the model and improve its fairness.
Edit intent: fluency
Original:  As a countermeasure, fairness testing systemically identifies discriminative samples, which can be used to retrain the model and improve its fairness.
Revised:  As a countermeasure, fairness testing systemically identifies discriminative samples, which can be used to retrain the model and improve the model's fairness.
Edit intent: fluency
Original:  First , they only work well on traditional machine learning models and have poor performance (e.g., effectiveness and efficiency) on deep learning models.
Revised:  Firstly , they only work well on traditional machine learning models and have poor performance (e.g., effectiveness and efficiency) on deep learning models.
Edit intent: fluency
Original:  Second , they only work on simple tabular data and are not applicable for domains such as text.
Revised:  Secondly , they only work on simple tabular data and are not applicable for domains such as text.
Edit intent: fluency
Original:  Second , they only work on simple tabular data and are not applicable for domains such as text.
Revised:  Second , they only work on simple structured (e.g., tabular) data and are not applicable for domains such as text.
Edit intent: clarity
Original:  In this work, we bridge the gap by proposing a scalable and effective approach for systematically searching for discriminative samples while extending fairness testing to address a  challenging domain, i.e., text classification.
Revised:  In this work, we bridge the gap by proposing a scalable and effective approach for systematically searching for discriminatory samples while extending fairness testing to address a challenging domain, i.e., text classification.
Edit intent: clarity
Original:  In this work, we bridge the gap by proposing a scalable and effective approach for systematically searching for discriminative samples while extending fairness testing to address a  challenging domain, i.e., text classification.
Revised:  In this work, we bridge the gap by proposing a scalable and effective approach for systematically searching for discriminative samples while extending existing fairness testing approaches to address a challenging domain, i.e., text classification.
Edit intent: clarity
Original:  In this work, we bridge the gap by proposing a scalable and effective approach for systematically searching for discriminative samples while extending fairness testing to address a  challenging domain, i.e., text classification.
Revised:  In this work, we bridge the gap by proposing a scalable and effective approach for systematically searching for discriminative samples while extending fairness testing to address a more challenging domain, i.e., text classification.
Edit intent: clarity
Original:  Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable  . Experimental results show that on average, our approach explores the search space  more effectively (9.62 and 2.38 times more than the state-of-art methods respectively on tabular and text datasets) and generates much more individual discriminatory instances (24.95 and 2.68 times) within  reasonable time.
Revised:  Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which is significantly more scalable . Experimental results show that on average, our approach explores the search space more effectively (9.62 and 2.38 times more than the state-of-art methods respectively on tabular and text datasets) and generates much more individual discriminatory instances (24.95 and 2.68 times) within reasonable time.
Edit intent: coherence
Original:  Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable  . Experimental results show that on average, our approach explores the search space  more effectively (9.62 and 2.38 times more than the state-of-art methods respectively on tabular and text datasets) and generates much more individual discriminatory instances (24.95 and 2.68 times) within  reasonable time.
Revised:  Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable and effective . Experimental results show that on average, our approach explores the search space more effectively (9.62 and 2.38 times more than the state-of-art methods respectively on tabular and text datasets) and generates much more individual discriminatory instances (24.95 and 2.68 times) within reasonable time.
Edit intent: clarity
Original:  Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable  . Experimental results show that on average, our approach explores the search space  more effectively (9.62 and 2.38 times more than the state-of-art methods respectively on tabular and text datasets) and generates much more individual discriminatory instances (24.95 and 2.68 times) within  reasonable time.
Revised:  Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable . Experimental results show that on average, our approach explores the search space much more effectively (9.62 and 2.38 times more than the state-of-art methods respectively on tabular and text datasets) and generates much more individual discriminatory instances (24.95 and 2.68 times) within reasonable time.
Edit intent: fluency
Original:  Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable  . Experimental results show that on average, our approach explores the search space  more effectively (9.62 and 2.38 times more than the state-of-art methods respectively on tabular and text datasets) and generates much more individual discriminatory instances (24.95 and 2.68 times) within  reasonable time.
Revised:  Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable . Experimental results show that on average, our approach explores the search space more effectively (9.62 and 2.38 times more than the state-of-the-art methods respectively on tabular and text datasets) and generates much more individual discriminatory instances (24.95 and 2.68 times) within reasonable time.
Edit intent: fluency
Original:  Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable  . Experimental results show that on average, our approach explores the search space  more effectively (9.62 and 2.38 times more than the state-of-art methods respectively on tabular and text datasets) and generates much more individual discriminatory instances (24.95 and 2.68 times) within  reasonable time.
Revised:  Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable . Experimental results show that on average, our approach explores the search space more effectively (9.62 and 2.38 times more than the state-of-art methods respectively on tabular and text datasets) and generates much more discriminatory samples (24.95 and 2.68 times) within reasonable time.
Edit intent: clarity
Original:  Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable  . Experimental results show that on average, our approach explores the search space  more effectively (9.62 and 2.38 times more than the state-of-art methods respectively on tabular and text datasets) and generates much more individual discriminatory instances (24.95 and 2.68 times) within  reasonable time.
Revised:  Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable . Experimental results show that on average, our approach explores the search space more effectively (9.62 and 2.38 times more than the state-of-art methods respectively on tabular and text datasets) and generates much more individual discriminatory instances (24.95 and 2.68 times) within a same reasonable time.
Edit intent: clarity
Original:  The retrained models reduce discrimination by 57.2\% and 60.2\% respectively on average.
Revised:  Moreover, the retrained models reduce discrimination by 57.2\% and 60.2\% respectively on average.
Edit intent: coherence
Original:  Yet creating new interfaces is deeply challenging, as it requires the developer to understand the queries needed to express the desired analysis task, design the appropriate interface to express those queries for the task, and implement the interface using a combination of visualization, browser, server, and database technologies.
Revised:  However, creating new interfaces is deeply challenging, as it requires the developer to understand the queries needed to express the desired analysis task, design the appropriate interface to express those queries for the task, and implement the interface using a combination of visualization, browser, server, and database technologies.
Edit intent: coherence
Original:  PI2 analyzes queries syntactically , and represents a set of queries using a novel Difftree structure that encodes systematic variations between query abstract syntax trees.
Revised:  PI2 analyzes queries syntactically and represents a set of queries using a novel Difftree structure that encodes systematic variations between query abstract syntax trees.
Edit intent: fluency
Original:  We show that PI2 can express data-oriented interactions in existing visualization interaction taxonomies, can reproduce or improve several real-world visual analysis interfaces, generates interfaces in 2-19s (median 6s), and scales linearly with the number of queries.
Revised:  We show that PI2 can express data-oriented interactions in existing visualization interaction taxonomies, reproduce or improve several real-world visual analysis interfaces, generates interfaces in 2-19s (median 6s), and scales linearly with the number of queries.
Edit intent: coherence
Original:  We show that PI2 can express data-oriented interactions in existing visualization interaction taxonomies, can reproduce or improve several real-world visual analysis interfaces, generates interfaces in 2-19s (median 6s), and scales linearly with the number of queries.
Revised:  We show that PI2 can express data-oriented interactions in existing visualization interaction taxonomies, can reproduce or improve several real-world visual analysis interfaces, generate interfaces in 2-19s (median 6s), and scales linearly with the number of queries.
Edit intent: fluency
Original:  We show that PI2 can express data-oriented interactions in existing visualization interaction taxonomies, can reproduce or improve several real-world visual analysis interfaces, generates interfaces in 2-19s (median 6s), and scales linearly with the number of queries.
Revised:  We show that PI2 can express data-oriented interactions in existing visualization interaction taxonomies, can reproduce or improve several real-world visual analysis interfaces, generates interfaces in 2-19s (median 6s), and scale linearly with the number of queries.
Edit intent: fluency
Original:  The equilibrium constraints corresponding to the schedule-based hyperpath flow is adapted from the literature with some modifications to fit it into an estimation problem.
Revised:  The equilibrium constraints corresponding to the schedule-based hyperpath flow are modified from the literature with some modifications to fit it into an estimation problem.
Edit intent: coherence
Original:  The equilibrium constraints corresponding to the schedule-based hyperpath flow is adapted from the literature with some modifications to fit it into an estimation problem.
Revised:  The equilibrium constraints corresponding to the schedule-based hyperpath flow is adapted from the literature to fit into an estimation problem.
Edit intent: clarity
Original:  Computational experiments are conducted first to verify the methodology with two synthetic data sets (one of which is Sioux Falls), followed by a validation of the method using bus data from Qingpu District in Shanghai, China from July 1, 2016 , with 4 bus lines, 120 segments, 55 bus stops, and 120 one-minute intervals ;
Revised:  Computational experiments are conducted first to verify the methodology with two synthetic data sets (one of which is Sioux Falls), followed by a validation of the method using bus data from Qingpu District in Shanghai, China , with 4 bus lines, 120 segments, 55 bus stops, and 120 one-minute intervals ;
Edit intent: coherence
Original:  relative errors among segments are 42.5\% , which compares well with OD estimation methods applied to real data in the literature (e.g. Irvine network, Brescia motorway) .
Revised:  relative errors among segments are 42.5\% .
Edit intent: coherence
Original: Developing controllers for obstacle avoidance between polytopes is a challenging and necessary problem for navigation in a tight space .
Revised: Developing controllers for obstacle avoidance between polytopes is a challenging and necessary problem for navigation in tight spaces .
Edit intent: fluency
Original:  We use a cyclical approach involving two stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, (2) generating pseudo labels or low confidence labels on the  unlabeled test dataset, and then,  combining the generated labels with the previously available high confidence labeled dataset.
Revised:  We use a cyclical approach involving two stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, generating pseudo labels or low confidence labels on the unlabeled test dataset, and then, combining the generated labels with the previously available high confidence labeled dataset.
Edit intent: coherence
Original:  We use a cyclical approach involving two stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, (2) generating pseudo labels or low confidence labels on the  unlabeled test dataset, and then,  combining the generated labels with the previously available high confidence labeled dataset.
Revised:  We use a cyclical approach involving two stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, (2) generating pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, combining the generated labels with the previously available high confidence labeled dataset.
Edit intent: clarity
Original:  Our method, which we release with all the code including trained models, can also be used as an open science benchmark for the Sentinel-1 released dataset on GitHub.
Edit intent: coherence