wanyu/IteraTeR_full_sent huggingface dataset:
Original:  GPS coordinates and other location indicators are fine-grained location indicators that are difficult to be effectively utilized by machine learning models in Geo-aware applications.
Revised: An increasing amount of location-based service (LBS) data is being accumulated and helps to study urban dynamics and human mobility. GPS coordinates and other location indicators are fine-grained location indicators that are difficult to be effectively utilized by machine learning models in Geo-aware applications.
Edit intent: meaning-changed
Original:  When it comes to the scale of the entire cities , existing approaches always suffer from extensive computational cost and signigicant information loss. An increasing amount of location-based service (LBS) data are being accumulated and released to the public and enables us to study urban dynamics and human mobility. This study learns vector representations for locations using the large-scale LBS data.
Revised:  When it comes to the scale of the entire cities , existing approaches always suffer from extensive computational cost and significant data sparsity.
Edit intent: clarity
Original:  Different from existing studies, we propose to consider both spatial connection and human mobility , and jointly learn the representations from a flow graph and a spatial graph through a GCN-aided skip-gram model named GCN-L2V.
Revised:  Different from existing studies, we propose to learn representations through a GCN-aided skip-gram model named GCN-L2V by considering both spatial connection and human mobility , and jointly learn the representations from a flow graph and a spatial graph through a GCN-aided skip-gram model named GCN-L2V.
Edit intent: coherence
Original:  Different from existing studies, we propose to consider both spatial connection and human mobility , and jointly learn the representations from a flow graph and a spatial graph through a GCN-aided skip-gram model named GCN-L2V.
Revised:  Different from existing studies, we propose to consider both spatial connection and human mobility . With a flow graph and a spatial graph through a GCN-aided skip-gram model named GCN-L2V.
Edit intent: coherence
Original:  Different from existing studies, we propose to consider both spatial connection and human mobility , and jointly learn the representations from a flow graph and a spatial graph through a GCN-aided skip-gram model named GCN-L2V. This model embeds context information in human mobility and spatial information.
Revised:  Different from existing studies, we propose to consider both spatial connection and human mobility , and jointly learn the representations from a flow graph and a spatial graph , it embeds context information in human mobility and spatial information.
Edit intent: coherence
Original:   GCN-L2V  can be applied in a complementary manner to other place embedding methods and down-streaming Geo-aware applications.
Revised:  As far as we know, this is the first study that provides a fine-grained location embedding at the city level using only LBS records. GCN-L2V can be applied in a complementary manner to other place embedding methods and down-streaming Geo-aware applications.
Edit intent: meaning-changed
Original:  This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption  while in practice the target data often come from a different distribution than the source data , known as domain shift . Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning.
Revised:  This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption on source/target data, while in practice the target data often come from a different distribution than the source data , known as domain shift . Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning.
Edit intent: meaning-changed
Original:  This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption  while in practice the target data often come from a different distribution than the source data , known as domain shift . Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning.
Revised:  This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption while in practice the target data often come from a different distribution than the source data . Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning.
Edit intent: coherence
Original:  This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption  while in practice the target data often come from a different distribution than the source data , known as domain shift . Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning.
Revised:  This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption while in practice the target data often come from a different distribution than the source data , known as domain shift . Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning.
Edit intent: clarity
Original:  Since first introduced in 2011, research in DG has undergone a decade progress. Ten years of research in this topic have led to a broad spectrum of methodologies, e.g.,  based on domain alignment, meta-learning, data augmentation, or ensemble learning, just to name a few;
Revised:  Since first introduced in 2011, research in DG has made great progresses. In particular, intensive research in this topic have led to a broad spectrum of methodologies, e.g., based on domain alignment, meta-learning, data augmentation, or ensemble learning, just to name a few;
Edit intent: clarity
Original:  Ten years of research in this topic have led to a broad spectrum of methodologies, e.g.,  based on domain alignment, meta-learning, data augmentation, or ensemble learning, just to name a few;
Revised:  Ten years of research in this topic has led to a broad spectrum of methodologies, e.g., based on domain alignment, meta-learning, data augmentation, or ensemble learning, just to name a few;
Edit intent: fluency
Original:  Ten years of research in this topic have led to a broad spectrum of methodologies, e.g.,  based on domain alignment, meta-learning, data augmentation, or ensemble learning, just to name a few;
Revised:  Ten years of research in this topic have led to a broad spectrum of methodologies, e.g., those based on domain alignment, meta-learning, data augmentation, or ensemble learning, just to name a few;
Edit intent: clarity
Original:  In this paper, for the first time, a comprehensive literature review is provided to summarize the ten-year development in DG . First, we cover the background by giving the problem definitions and discussing how DG is related to other fields like domain adaptation and transfer learning.
Revised:  In this paper, for the first time, a comprehensive literature review is provided to summarize the developments in DG in the past decade. Specifically, we first cover the background by giving the problem definitions and discussing how DG is related to other fields like domain adaptation and transfer learning.
Edit intent: clarity
Original:  In this paper, for the first time, a comprehensive literature review is provided to summarize the ten-year development in DG . First, we cover the background by giving the problem definitions and discussing how DG is related to other fields like domain adaptation and transfer learning.
Revised:  In this paper, for the first time, a comprehensive literature review is provided to summarize the ten-year development in DG . First, we cover the background by formally defining DG and relating it to other research fields like domain adaptation and transfer learning.
Edit intent: clarity
Original:  In this paper, we take a major step in addressing this challenge and present a new  framework, called PRIMA , that computes precise convex approximations of arbitrary non-linear activations.
Revised:  In this paper, we take a major step in addressing this challenge and present a new framework, called PRIMA . PRIMA is both (i) general: it handles any non-linear activation function, and (ii) precise: it computes precise convex approximations of arbitrary non-linear activations.
Edit intent: meaning-changed
Original:  In this paper, we take a major step in addressing this challenge and present a new  framework, called PRIMA , that computes precise convex approximations of arbitrary non-linear activations. PRIMA is based on novel approximation algorithms that compute the convex hull of polytopes, leveraging concepts from computational geometry.
Revised:  In this paper, we take a major step in addressing this challenge and present a new framework, called PRIMA , that computes precise convex approximations involving multiple neurons via novel convex hull approximation algorithms that compute the convex hull of polytopes, leveraging concepts from computational geometry.
Edit intent: meaning-changed
Original:  Our results show that PRIMA is significantly more precise than the state-of-the-art, verifying robustness for up to 16 \%, 30\%, and 34\% more images than prior work on ReLU-, Sigmoid-, and Tanh-based networks, respectively  .
Revised:  Our results show that PRIMA is significantly more precise than state-of-the-art, verifying robustness for up to 16 \%, 30\%, and 34\% more images than prior work on ReLU-, Sigmoid-, and Tanh-based networks, respectively .
Edit intent: fluency
Original:  Our results show that PRIMA is significantly more precise than the state-of-the-art, verifying robustness for up to 16 \%, 30\%, and 34\% more images than prior work on ReLU-, Sigmoid-, and Tanh-based networks, respectively  .
Revised:  Our results show that PRIMA is significantly more precise than the state-of-the-art, verifying robustness for up to 14 \%, 30\%, and 34\% more images than prior work on ReLU-, Sigmoid-, and Tanh-based networks, respectively .
Edit intent: meaning-changed
Original:  Our results show that PRIMA is significantly more precise than the state-of-the-art, verifying robustness for up to 16 \%, 30\%, and 34\% more images than prior work on ReLU-, Sigmoid-, and Tanh-based networks, respectively  .
Revised:  Our results show that PRIMA is significantly more precise than the state-of-the-art, verifying robustness for up to 16 \%, 30\%, and 34\% more images than existing work on ReLU-, Sigmoid-, and Tanh-based networks, respectively .
Edit intent: clarity
Original:  Our results show that PRIMA is significantly more precise than the state-of-the-art, verifying robustness for up to 16 \%, 30\%, and 34\% more images than prior work on ReLU-, Sigmoid-, and Tanh-based networks, respectively  .
Revised:  Our results show that PRIMA is significantly more precise than the state-of-the-art, verifying robustness for up to 16 \%, 30\%, and 34\% more images than prior work on ReLU-, Sigmoid-, and Tanh-based networks, respectively . Further, PRIMA enables, for the first time, precise verification of a realistic neural network for autonomous driving within a few minutes .
Edit intent: meaning-changed
Original:  Using our age-stratified  model and curated epidemiological and vaccination data, we quantified at which pace European countries can lift restrictions without overwhelming their health-care systems.
Revised:  Using our age-stratified model and curated epidemiological and vaccination data, we quantified the rate (relative to vaccination progress) at which countries from the European Union (EU) can lift non-pharmaceutical interventions without overwhelming their health-care systems.
Edit intent: meaning-changed
Original:  Using our age-stratified  model and curated epidemiological and vaccination data, we quantified at which pace European countries can lift restrictions without overwhelming their health-care systems. The analysed scenarios range from immediately lifting restriction (accepting high mortality and morbidity) to reducing case numbers until test-trace-and-isolate programs facilitate control .
Revised:  Using our age-stratified model and curated epidemiological and vaccination data, we quantified at which pace European countries can lift restrictions without overwhelming their healthcare systems. We analyzed scenarios ranging from immediately lifting restriction (accepting high mortality and morbidity) to reducing case numbers until test-trace-and-isolate programs facilitate control .
Edit intent: fluency
Original: \end{abstract} 
Revised: \end{abstract} only high vaccine uptake can prevent further severe waves. Across EU countries, seroprevalence impacts the long-term success of vaccination campaigns more strongly than age demographics. In addition, we highlight the need for preventive measures to reduce contagion in school settings throughout the year 2021, where children might be drivers of contagion because of them remaining susceptible. Strategies that maintain low case numbers, instead of high ones, reduce infections and deaths by factors of eleven and five, respectively...
Edit intent: meaning-changed
Original:   We propose DAFAR, a feedback framework that allows deep learning models to detect  adversarial examples in high accuracy and universality . DAFAR has a relatively simple structure, which contains a target network , a plug-in feedback network and an autoencoder-based detector.
Revised:  And most of existing techniques have complicated structures or mechanisms that cause prohibitively high overhead or latency, impractical to apply on real software. We propose DAFAR, a feedback framework that allows deep learning models to detect adversarial examples in high accuracy and universality . DAFAR has a relatively simple structure, which contains a target network , a plug-in feedback network and an autoencoder-based detector.
Edit intent: meaning-changed
Original:   We propose DAFAR, a feedback framework that allows deep learning models to detect  adversarial examples in high accuracy and universality . DAFAR has a relatively simple structure, which contains a target network , a plug-in feedback network and an autoencoder-based detector.
Revised:  We propose DAFAR, a feedback framework that allows deep learning models to detect /purify adversarial examples in high accuracy and universality . DAFAR has a relatively simple structure, which contains a target network , a plug-in feedback network and an autoencoder-based detector.
Edit intent: meaning-changed
Original:   We propose DAFAR, a feedback framework that allows deep learning models to detect  adversarial examples in high accuracy and universality . DAFAR has a relatively simple structure, which contains a target network , a plug-in feedback network and an autoencoder-based detector.
Revised:  We propose DAFAR, a feedback framework that allows deep learning models to detect adversarial examples in high effectiveness and universality, with low area and time overhead . DAFAR has a relatively simple structure, which contains a target network , a plug-in feedback network and an autoencoder-based detector.
Edit intent: meaning-changed
Original:   We propose DAFAR, a feedback framework that allows deep learning models to detect  adversarial examples in high accuracy and universality . DAFAR has a relatively simple structure, which contains a target network , a plug-in feedback network and an autoencoder-based detector.
Revised:  We propose DAFAR, a feedback framework that allows deep learning models to detect adversarial examples in high accuracy and universality . DAFAR has a simple structure, which contains a target network , a plug-in feedback network and an autoencoder-based detector.
Edit intent: clarity
Original:   We propose DAFAR, a feedback framework that allows deep learning models to detect  adversarial examples in high accuracy and universality . DAFAR has a relatively simple structure, which contains a target network , a plug-in feedback network and an autoencoder-based detector.
Revised:  We propose DAFAR, a feedback framework that allows deep learning models to detect adversarial examples in high accuracy and universality . DAFAR has a relatively simple structure, containing a victim model , a plug-in feedback network and an autoencoder-based detector.
Edit intent: meaning-changed
Original:   We propose DAFAR, a feedback framework that allows deep learning models to detect  adversarial examples in high accuracy and universality . DAFAR has a relatively simple structure, which contains a target network , a plug-in feedback network and an autoencoder-based detector.
Revised:  We propose DAFAR, a feedback framework that allows deep learning models to detect adversarial examples in high accuracy and universality . DAFAR has a relatively simple structure, which contains a target network , a plug-in feedback network , and a detector.
Edit intent: coherence
Original:  It transforms the imperceptible-perturbation attack on the target network directly into obvious reconstruction-error attack on the feedback autoencoder . Finally the detector gives an anomaly score and determines whether the input is adversarial according to the reconstruction errors . Experiments are conducted on MNIST and CIFAR-10 data-sets . Experimental results show that DAFAR is effective against popular and arguably most advanced attacks without losing performance on legitimate samples,
Revised:  For strong attacks, it transforms the imperceptible attack on the target network directly into obvious reconstruction-error attack on the feedback autoencoder . Finally the detector gives an anomaly score and determines whether the input is adversarial according to the reconstruction errors . Experiments are conducted on MNIST and CIFAR-10 data-sets . Experimental results show that DAFAR is effective against popular and arguably most advanced attacks without losing performance on legitimate samples,
Edit intent: meaning-changed
Original: the imperceptible-perturbation attack on the target network directly into obvious reconstruction-error attack on the feedback autoencoder . Finally the detector gives an anomaly score and determines whether the input is adversarial according to the reconstruction errors . Experiments are conducted on MNIST and CIFAR-10 data-sets . Experimental results show that DAFAR is effective against popular and arguably most advanced attacks without losing performance on legitimate samples,
Revised: the imperceptible-perturbation attack on the victim model into the obvious reconstruction-error attack on the feedback autoencoder . Finally the detector gives an anomaly score and determines whether the input is adversarial according to the reconstruction errors . Experiments are conducted on MNIST and CIFAR-10 data-sets . Experimental results show that DAFAR is effective against popular and arguably most advanced attacks without losing performance on legitimate samples,
Edit intent: clarity
Original: the imperceptible-perturbation attack on the target network directly into obvious reconstruction-error attack on the feedback autoencoder . Finally the detector gives an anomaly score and determines whether the input is adversarial according to the reconstruction errors . Experiments are conducted on MNIST and CIFAR-10 data-sets . Experimental results show that DAFAR is effective against popular and arguably most advanced attacks without losing performance on legitimate samples,
Revised: the imperceptible-perturbation attack on the target network directly into obvious reconstruction-error attack on the feedback autoencoder directly, which is much easier to detect; for weak attacks, the reformation process destroys the structure of adversarial examples . Experiments are conducted on MNIST and CIFAR-10 data-sets . Experimental results show that DAFAR is effective against popular and arguably most advanced attacks without losing performance on legitimate samples,
Edit intent: meaning-changed
Original: the imperceptible-perturbation attack on the target network directly into obvious reconstruction-error attack on the feedback autoencoder . Finally the detector gives an anomaly score and determines whether the input is adversarial according to the reconstruction errors . Experiments are conducted on MNIST and CIFAR-10 data-sets . Experimental results show that DAFAR is effective against popular and arguably most advanced attacks without losing performance on legitimate samples,
Revised: the imperceptible-perturbation attack on the target network directly into obvious reconstruction-error attack on the feedback autoencoder . Finally the detector gives an anomaly score and determines whether the input is adversarial according to the reconstruction errors . Experiments are conducted on MNIST and CIFAR-10 data-sets , showing that DAFAR is effective against popular and arguably most advanced attacks without losing performance on legitimate samples,
Edit intent: clarity
Original: Heparin is administered intravenously as an anticoagulant to COVID-19 patients and via aerosol to treat other lung diseases. It has recently been found to have antiviral activity against SARS-CoV-2 as it hinders attachment of the virus to the host cell by binding to the virus spike glycoprotein .
Revised: Heparin has been found to have antiviral activity against SARS-CoV-2 as it hinders attachment of the virus to the host cell by binding to the virus spike glycoprotein .
Edit intent: coherence
Original:  mostly positively charged patches on the spike, and thereby mask the basic residues of the receptor binding domain and the basic S1/S2 site , which is unique specific to SAR-CoV-2 and is important for furin-mediated spike cleavage and the subsequent formation of its post-fusion conformation . Experiments corroborated the simulation results by showing that heparin binds the basic S1/S2 site of spike, inhibiting cleavage by furin.
Revised:  mostly positively charged patches on the spike, thereby masking the basic residues of the receptor binding domain and the basic S1/S2 site , which is unique specific to SAR-CoV-2 and is important for furin-mediated spike cleavage and the subsequent formation of its post-fusion conformation . Experiments corroborated the simulation results by showing that heparin binds the basic S1/S2 site of spike, inhibiting cleavage by furin.
Edit intent: coherence
Original:  mostly positively charged patches on the spike, and thereby mask the basic residues of the receptor binding domain and the basic S1/S2 site , which is unique specific to SAR-CoV-2 and is important for furin-mediated spike cleavage and the subsequent formation of its post-fusion conformation . Experiments corroborated the simulation results by showing that heparin binds the basic S1/S2 site of spike, inhibiting cleavage by furin.
Revised:  mostly positively charged patches on the spike, and thereby mask the basic residues of the receptor binding domain and of the S1/S2 site , which is unique specific to SAR-CoV-2 and is important for furin-mediated spike cleavage and the subsequent formation of its post-fusion conformation . Experiments corroborated the simulation results by showing that heparin binds the basic S1/S2 site of spike, inhibiting cleavage by furin.
Edit intent: clarity
Original:  mostly positively charged patches on the spike, and thereby mask the basic residues of the receptor binding domain and the basic S1/S2 site , which is unique specific to SAR-CoV-2 and is important for furin-mediated spike cleavage and the subsequent formation of its post-fusion conformation . Experiments corroborated the simulation results by showing that heparin binds the basic S1/S2 site of spike, inhibiting cleavage by furin.
Revised:  mostly positively charged patches on the spike, and thereby mask the basic residues of the receptor binding domain and the basic S1/S2 site . Experiments corroborated the simulation results by showing that heparin binds the basic S1/S2 site of spike, inhibiting cleavage by furin.
Edit intent: coherence