wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Then  we propose the use of the Laplacian eigenvectors as such vector field , and we show that the method generalizes CNNs on an n-dimensional grid , and is provably more discriminative than standard GNNs regarding the Weisfeiler-Lehman 1-WL test.
Revised:  Then we propose the use of the Laplacian eigenvectors as such vector field . We show that the method generalizes CNNs on an n-dimensional grid , and is provably more discriminative than standard GNNs regarding the Weisfeiler-Lehman 1-WL test.
Edit intent: coherence
Original:  Then  we propose the use of the Laplacian eigenvectors as such vector field , and we show that the method generalizes CNNs on an n-dimensional grid , and is provably more discriminative than standard GNNs regarding the Weisfeiler-Lehman 1-WL test.
Revised:  Then we propose the use of the Laplacian eigenvectors as such vector field , and we show that the method generalizes CNNs on an n-dimensional grid and is provably more discriminative than standard GNNs regarding the Weisfeiler-Lehman 1-WL test.
Edit intent: fluency
Original:  An important outcome of this work is that it enables to translate any physical or biological problems with intrinsic directional axes into a graph network formalism with an embedded directional field.abstract 
Revised:  An important outcome of this work is that it enables to translate any physical or biological problems with intrinsic directional axes into a graph network formalism with an embedded directional field.abstract \% on the CIFAR10 graph dataset and 11\% to 32\% on the molecular ZINC dataset, and a relative increase in precision of 1.6\% on the MolPCBA dataset. An important outcome of this work is that it enables graph networks to embed directions in an unsupervised way, thus allowing a better representation of the anisotropic features in different physical or biological problems.
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Original:  Methods: We conducted a prospective cohort study using data from 1670 Danish schoolchildren measured over 5.5 years  . Coupled 4-week, uncoupled 4-week, and uncoupled 5-week ACWRs were calculated using activity frequency in the past week as the acute load  and average weekly activity frequency in the past 4 or 5 weeks as the chronic load .
Revised:  Methods: We conducted a prospective cohort study using data from 1670 Danish schoolchildren measured over 5.5 years (2008 to 2014) . Coupled 4-week, uncoupled 4-week, and uncoupled 5-week ACWRs were calculated using activity frequency in the past week as the acute load and average weekly activity frequency in the past 4 or 5 weeks as the chronic load .
Edit intent: meaning-changed
Original:  Methods: We conducted a prospective cohort study using data from 1670 Danish schoolchildren measured over 5.5 years  . Coupled 4-week, uncoupled 4-week, and uncoupled 5-week ACWRs were calculated using activity frequency in the past week as the acute load  and average weekly activity frequency in the past 4 or 5 weeks as the chronic load .
Revised:  Methods: We conducted a prospective cohort study using data from 1670 Danish schoolchildren measured over 5.5 years . Coupled 4-week, uncoupled 4-week, and uncoupled 5-week ACWRs were calculated using activity frequency in the past week as the acute load (numerator), and average weekly activity frequency in the past 4 or 5 weeks as the chronic load .
Edit intent: meaning-changed
Original:  Methods: We conducted a prospective cohort study using data from 1670 Danish schoolchildren measured over 5.5 years  . Coupled 4-week, uncoupled 4-week, and uncoupled 5-week ACWRs were calculated using activity frequency in the past week as the acute load  and average weekly activity frequency in the past 4 or 5 weeks as the chronic load . We used new onset pain as a proxy for injury, and modelled its relationship with different ACWR variations  using generalized linear and generalized additive models, with and without accounting for repeated measures.
Revised:  Methods: We conducted a prospective cohort study using data from 1670 Danish schoolchildren measured over 5.5 years . Coupled 4-week, uncoupled 4-week, and uncoupled 5-week ACWRs were calculated using activity frequency in the past week as the acute load and average weekly activity frequency in the past 4 or 5 weeks as the chronic load (denominator). We modelled the relationship between different ACWR variations using generalized linear and generalized additive models, with and without accounting for repeated measures.
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Original:  This model predicted an injury risk of ~3\% when activity increased by up to 50\% or decreased by up to 20\% ( 0.8 <= ACWR< = 1.5).
Revised:  It predicted an injury risk of ~3\% when activity increased by up to 50\% or decreased by up to 20\% ( 0.8 <= ACWR< = 1.5).
Edit intent: clarity
Original:  This model predicted an injury risk of ~3\% when activity increased by up to 50\% or decreased by up to 20\% ( 0.8 <= ACWR< = 1.5).
Revised:  This model predicted an injury risk of ~3\% for ACWRs between 0.8 (activity level decreased by 20\%) and 1.5 (activity level increased by 50\% or decreased by up to 20\% ( 0.8 <= ACWR< = 1.5).
Edit intent: meaning-changed
Original:  This model predicted an injury risk of ~3\% when activity increased by up to 50\% or decreased by up to 20\% ( 0.8 <= ACWR< = 1.5).
Revised:  This model predicted an injury risk of ~3\% when activity increased by up to 50\% ). When activity decreased by more than 20\% ( 0.8 <= ACWR< = 1.5).
Edit intent: meaning-changed
Original:  This model predicted an injury risk of ~3\% when activity increased by up to 50\% or decreased by up to 20\% ( 0.8 <= ACWR< = 1.5).
Revised:  This model predicted an injury risk of ~3\% when activity increased by up to 50\% or decreased by up to 20\% ( ACWR< = 1.5).
Edit intent: meaning-changed
Original:  This model predicted an injury risk of ~3\% when activity increased by up to 50\% or decreased by up to 20\% ( 0.8 <= ACWR< = 1.5). Larger decreases in activity were associated with a decreased injury risk to a minimum of 1.5\% . Larger increases in activity were associated with an increased injury risk, from 3\% up to a maximum of 6\% at ACWR = 5. Girls were at significantly higher risk of injury than boys.
Revised:  This model predicted an injury risk of ~3\% when activity increased by up to 50\% or decreased by up to 20\% ( 0.8 <= ACWR< 0.8), injury risk was lower ( minimum of 1.5\% . Larger increases in activity were associated with an increased injury risk, from 3\% up to a maximum of 6\% at ACWR = 5. Girls were at significantly higher risk of injury than boys.
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Original:  Larger decreases in activity were associated with a decreased injury risk to a minimum of 1.5\% . Larger increases in activity were associated with an increased injury risk, from 3\% up to a maximum of 6\% at ACWR = 5. Girls were at significantly higher risk of injury than boys.
Revised:  Larger decreases in activity were associated with a decreased injury risk to a minimum of 1.5\% at ACWR=0). When activity increased by more than 50\% (ACWR > 1.5), injury risk was higher ( maximum of 6\% at ACWR = 5. Girls were at significantly higher risk of injury than boys.
Edit intent: meaning-changed
Original:  Larger decreases in activity were associated with a decreased injury risk to a minimum of 1.5\% . Larger increases in activity were associated with an increased injury risk, from 3\% up to a maximum of 6\% at ACWR = 5. Girls were at significantly higher risk of injury than boys.
Revised:  Larger decreases in activity were associated with a decreased injury risk to a minimum of 1.5\% . Larger increases in activity were associated with an increased injury risk, from 3\% up to a maximum of 6\% at ACWR = 5). Girls were at significantly higher risk of injury than boys.
Edit intent: fluency
Original: Fact Extraction and VERification (FEVER) is a recently introduced task which aims to identify the veracity of a given claim based on Wikipedia documents. A lot of methods have been proposed to address this problem which consists of the subtasks of (i) retrieving the relevant documents (and sentences) from Wikipedia and (ii) validating whether the information in the documents supports or refutes a given claim.
Revised: Fact Extraction and VERification (FEVER) is a recently introduced task which aims to identify the veracity of a given claim . Specifically, we focus on the task of Fact Extraction and VERification (FEVER) and its accompanied dataset. The task consists of the subtasks of (i) retrieving the relevant documents (and sentences) from Wikipedia and (ii) validating whether the information in the documents supports or refutes a given claim.
Edit intent: meaning-changed
Original:  A lot of methods have been proposed to address this problem which consists of the subtasks of (i) retrieving the relevant documents (and sentences) from Wikipedia and (ii) validating whether the information in the documents supports or refutes a given claim.
Revised:  A lot of methods have been proposed to address this problem which consists of the subtasks of retrieving the relevant documents (and sentences) from Wikipedia and (ii) validating whether the information in the documents supports or refutes a given claim.
Edit intent: fluency
Original:  A lot of methods have been proposed to address this problem which consists of the subtasks of (i) retrieving the relevant documents (and sentences) from Wikipedia and (ii) validating whether the information in the documents supports or refutes a given claim.
Revised:  A lot of methods have been proposed to address this problem which consists of the subtasks of (i) retrieving the relevant documents (and sentences) from Wikipedia and validating whether the information in the documents supports or refutes a given claim.
Edit intent: fluency
Original:  In addition , we describe the proposed methods by analyzing the technical perspectives of the different approaches and discussing the performance results on the FEVER dataset  .
Revised:  In addition , we describe the proposed methods by analyzing the technical perspectives of the different approaches and discussing the performance results on the FEVER dataset , which is the most well-studied and formally structured dataset on the fact extraction and verification task. We also conduct the largest experimental study to date on identifying beneficial loss functions for the sentence retrieval component. Our analysis indicates that sampling negative sentences is important for improving the performance and decreasing the computational complexity. Finally, we describe open issues and future challenges, and we motivate future research in the task .
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Original: Prevalent approaches in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic  representations such that models trained on one language can be applied to other languages.
Revised: Recent progress in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic representations such that models trained on one language can be applied to other languages.
Edit intent: clarity
Original: Prevalent approaches in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic  representations such that models trained on one language can be applied to other languages.
Revised: Prevalent approaches in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic sentence representations such that models trained on one language can be applied to other languages.
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Original:  We compared the results with the indicators produced within the Covid-19 behavior tracker initiative by ICL and YouGov (CovidDataHub) . Preliminary results shows that social media images can produce reliable indicators for policy makers.
Revised:  We compared the results with the indicators produced within the CovidDataHub behavior tracker initiative by ICL and YouGov (CovidDataHub) . Preliminary results shows that social media images can produce reliable indicators for policy makers.
Edit intent: clarity
Original:   For general buyer valuations or a very large number of buyers, we propose computing market equilibrium using stochastic dual averaging, which finds an approximate equilibrium price vector with high probability.
Revised:  Based on our convex conic reformulation, we develop the first polynomial-time cake-cutting algorithm that achieves Pareto optimality, envy-freeness, and proportionality. For general buyer valuations or a very large number of buyers, we propose computing market equilibrium using stochastic dual averaging, which finds an approximate equilibrium price vector with high probability.
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Original: In this letter  we present a novel descriptor based on polygons derived from Urquhart tessellations on the position of trees in a forest detected from lidar scans. We present a framework that leverages these polygons to generate a signature that is used detect previously seen observations  even with partial overlap and different levels of noisewhile also inferring landmark correspondences to compute an affine transformation between observations .
Revised: In this letter we present a novel descriptor based on polygons derived from Urquhart tessellations on the position of trees in a forest . We propose a framework that leverages these polygons to generate a signature that is used detect previously seen observations even with partial overlap and different levels of noisewhile also inferring landmark correspondences to compute an affine transformation between observations .
Edit intent: clarity
Original:  We present a framework that leverages these polygons to generate a signature that is used detect previously seen observations  even with partial overlap and different levels of noisewhile also inferring landmark correspondences to compute an affine transformation between observations .
Revised:  We present a framework that uses these descriptors to detect previously seen observations even with partial overlap and different levels of noisewhile also inferring landmark correspondences to compute an affine transformation between observations .
Edit intent: clarity
Original:  We present a framework that leverages these polygons to generate a signature that is used detect previously seen observations  even with partial overlap and different levels of noisewhile also inferring landmark correspondences to compute an affine transformation between observations .
Revised:  We present a framework that leverages these polygons to generate a signature that is used detect previously seen observations and landmark correspondences, even with partial overlap and different levels of noisewhile also inferring landmark correspondences to compute an affine transformation between observations .
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Original:  We run loop-closure experiments in simulation and real-world data map-merging from different flights of an Unmanned Aerial Vehicle (UAV) in a pine tree forest and show that our method outperforms state-of-the-art approaches in accuracy and robustness.
Revised:  We run loop closure detection experiments in simulation and real-world data map-merging from different flights of an Unmanned Aerial Vehicle (UAV) in a pine tree forest and show that our method outperforms state-of-the-art approaches in accuracy and robustness.
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Original: When training deep neural networks for medical image classification, obtaining a sufficient number of manually annotated images is often a significant challenge. We propose to use textual findings , which are routinely written by clinicians during manual image analysis, to help overcome this problem.
Revised: When training deep neural networks for medical image classification, obtaining a sufficient number of manually labeled examples. In contrast, textual imaging reports , which are routinely written by clinicians during manual image analysis, to help overcome this problem.
Edit intent: clarity
Original:  We propose to use textual findings , which are routinely written by clinicians during manual image analysis, to help overcome this problem. The key idea is to use a contrastive loss to train image and text feature extractors to recognize if a given image-finding pair is a true match. The learned image feature extractor is then fine-tuned, in a transfer learning setting , for a supervised classification task .
Revised:  We propose to use textual findings , which are often readily available in medical records, contain rich but unstructured interpretations written by experts as part of standard clinical practice. We propose using these textual reports as a form of weak supervision to improve the image interpretation performance of a neural network without requiring additional manually labeled examples. We use an image-text matching task to train a feature extractor and then fine-tune it in a transfer learning setting , for a supervised classification task .
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Original:  The learned image feature extractor is then fine-tuned, in a transfer learning setting , for a supervised classification task .
Revised:  The learned image feature extractor is then fine-tuned, in a transfer learning setting , for a supervised task using a small labeled dataset. The end result is a neural network that automatically interprets imagery without requiring textual reports during inference. This approach can be applied to any task for which text-image pairs are readily available .
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Original:  We evaluate our method on three datasets and find consistent performance improvements . The biggest gains are realized when fewer manually labeled examples are available. In some cases, our method achieves the same performance as the baseline even when using 70%DIFDELCMD < \\%%% %DIF < --98\\% fewer labeled examples.abstract 
Revised:  We evaluate our method on three datasets and find consistent performance improvements %DIFDELCMD < \\%%% %DIF < --98\\% fewer labeled examples.abstract 
Edit intent: coherence
Original:  In some cases, our method achieves the same performance as the baseline even when using 70%DIFDELCMD < \\%%% %DIF < --98\\% fewer labeled examples.abstract 
Revised:  In some cases, our method achieves the same performance as the baseline even when using 70%DIFDELCMD < \\%%% %DIF < --98\\% fewer labeled examples.abstract , reducing the need for labeled data by 67\%-98\%.
Edit intent: meaning-changed
Original:  Specifically, a polynomial trajectory sampler is adopted to generate candidate trajectories considering high-level intentions and approximate the partition function in the maximum entropy IRL framework , and an environment model considering interactive behaviors among the ego and surrounding vehicles is built to better estimate the generated trajectories.
Revised:  Specifically, a polynomial trajectory sampler is adopted to generate candidate trajectories considering high-level intentions and approximate the partition function in the maximum entropy IRL framework . An environment model considering interactive behaviors among the ego and surrounding vehicles is built to better estimate the generated trajectories.
Edit intent: coherence
Original:  For the testing performance, the personalized modeling method outperforms the general modeling approach,  reducing the modeling errors in human likeness (a custom metric to gauge accuracy) by 23\% , and these two methods deliver better results compared to other baseline methods.
Revised:  For the testing performance, the personalized modeling method outperforms the general modeling approach, significantly reducing the modeling errors in human likeness (a custom metric to gauge accuracy) by 23\% , and these two methods deliver better results compared to other baseline methods.
Edit intent: meaning-changed
Original:  For the testing performance, the personalized modeling method outperforms the general modeling approach,  reducing the modeling errors in human likeness (a custom metric to gauge accuracy) by 23\% , and these two methods deliver better results compared to other baseline methods.
Revised:  For the testing performance, the personalized modeling method outperforms the general modeling approach, reducing the modeling errors in human likeness (a custom metric to gauge accuracy) , and these two methods deliver better results compared to other baseline methods.
Edit intent: coherence
Original: Theedhum Nandrum is a sentiment polarity detection system using two approaches--a Stochastic Gradient Descent (SGD) Classifier and a Recurrent Neural Network (RNN) Classifier.
Revised: Theedhum Nandrum is a sentiment polarity detection system using two approaches--a Stochastic Gradient Descent (SGD) based classifier and a Long Short-term Memory (LSTM) based Classifier.
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Original:  Our approach utilises language features like the use of emoji, choice of scripts and code mixing which appeared quite marked in the datasets specified for the Dravidian Codemix - FIRE 2020 task.
Revised:  Our approach utilises language features like use of emoji, choice of scripts and code mixing which appeared quite marked in the datasets specified for the Dravidian Codemix - FIRE 2020 task.
Edit intent: fluency
Original:  Our code is published in github at URL
Revised:  We achieved a weighted average F1 score of 0.77 for Tamil-English using a Logistic Regression based model after the task deadline. This performance betters the top ranked classifier on this dataset by a wide margin. Our use of language-specific Soundex to harmonise the spelling variants in code-mixed data appears to be a novel application of Soundex. Our complete code is published in github at URL
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Original:  The objects have varying poses and arrangements, and are imaged under different illumination conditions and camera poses. The recognition performance of our methods , which are trained using the living room images, remains relatively unaffected on the unseen warehouse images .
Revised:  On the benchmark indoor scenes dataset, sparse PI features show better recognition performance in unseen environments than the features learned using the widely used ResNetV2-56 and EfficientNet-B4 models. Further, they provide slightly higher recall and accuracy values than Faster R-CNN, an end-to-end object detection method, and its state-of-the-art variant, Domain Adaptive Faster R-CNN. The performance of our methods , which are trained using the living room images, remains relatively unaffected on the unseen warehouse images .
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Original: This paperpresents a novel neural architecture search (NAS)framework for graph neural networks (GNNs) .
Revised: In this paper, we propose a novel framework to automatically utilize task-dependent semantic information which is encoded in heterogeneous information networks (HINs). Specifically, we search for a meta graph, which can capture more complex semantic relations than a meta path, to determine how graph neural networks (GNNs) .
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Original: This paperpresents a novel neural architecture search (NAS)framework for graph neural networks (GNNs) . We design an expressive search space that focuses on a common and critical component of GNNs -- propagation model.
Revised: This paperpresents a novel neural architecture search (NAS)framework for graph neural networks (GNNs) propagate messages along different types of edges. We formalize the problem within the framework of neural architecture search (NAS) and then perform the search in a differentiable manner. We design an expressive search space that focuses on a common and critical component of GNNs -- propagation model.
Edit intent: meaning-changed