wanyu/IteraTeR_v2 huggingface dataset:
Original:  This allows us create an embedding of the temporal network in order to describe the behaviour of individuals and collectives over time and examine the structure of conversation over multiple timescales .
Revised:  This allows us create an embedding of the temporal network in order to describe behaviour over time and examine the structure of conversation over multiple timescales .
Edit intent: clarity
Original:  This allows us create an embedding of the temporal network in order to describe the behaviour of individuals and collectives over time and examine the structure of conversation over multiple timescales .
Revised:  This allows us create an embedding of the temporal network in order to describe the behaviour of individuals and collectives over time and at different timescales. We illustrate this method using an example of digital communication data collected from an online social network .
Edit intent: clarity
Original: Recently, eigenvector localization of complex network has seen a spurt in activities due to its versatile applicability in many different areas which includes networks centrality measure , spectral partitioning, development of approximation algorithms  and disease spreading phenomenon.
Revised: Recently, eigenvector localization of complex network has applicability in many different areas which includes networks centrality measure , spectral partitioning, development of approximation algorithms and disease spreading phenomenon.
Edit intent: clarity
Original: Recently, eigenvector localization of complex network has seen a spurt in activities due to its versatile applicability in many different areas which includes networks centrality measure , spectral partitioning, development of approximation algorithms  and disease spreading phenomenon.
Revised: Recently, eigenvector localization of complex network has seen a spurt in activities due to its versatile applicability in many different areas which include networks centrality measures , spectral partitioning, development of approximation algorithms and disease spreading phenomenon.
Edit intent: fluency
Original: Recently, eigenvector localization of complex network has seen a spurt in activities due to its versatile applicability in many different areas which includes networks centrality measure , spectral partitioning, development of approximation algorithms  and disease spreading phenomenon.
Revised: Recently, eigenvector localization of complex network has seen a spurt in activities due to its versatile applicability in many different areas which includes networks centrality measure , spectral partitioning, development of approximation algorithms , and disease spreading phenomenon.
Edit intent: fluency
Original:  For  a network, an eigenvector is said to be localized when most of its components are near to zero , with few taking very high values.
Revised:  For a network, an eigenvector is said to be localized when most of its components are near to zero with few taking very high values.
Edit intent: fluency
Original:  We also experiment with replacing attention mechanisms in other state-of-the-art models with our implementation and show increased accuracy.
Revised:  We also experiment with replacing attention mechanisms in state-of-the-art models with our implementation and show increased accuracy.
Edit intent: clarity
Original:  We also experiment with replacing attention mechanisms in other state-of-the-art models with our implementation and show increased accuracy.
Revised:  We also experiment with replacing attention mechanisms in other state-of-the-art models with our recurrent attention units (RAUs) and show increased accuracy.
Edit intent: clarity
Original: Visual question answering (VQA) requires AI models to comprehend data in two domains, vision and text.
Revised: Visual Question Answering (VQA) requires AI models to comprehend data in two domains, vision and text.
Edit intent: fluency
Original:  We  introduce a baseline VQA model with visual attention and compare the performance difference between convolutional and recurrent attention on the VQA 2.0 dataset.
Revised:  We introduce a baseline VQA model with visual attention and test the performance difference between convolutional and recurrent attention on the VQA 2.0 dataset.
Edit intent: clarity
Original: In this paper, we introduce a domain-specific language (DSL) for concisely expressing localized linear algebra on finite element tensors , and its integration within a code-generation framework.
Revised: In this paper, we introduce a domain-specific language (DSL) for concisely expressing localized linear algebra on finite element tensors and its integration within a code-generation framework.
Edit intent: fluency
Original:  Additionally, we demonstrate how this DSL can be used to implement local post-processing techniques to achieve superconvergent approximation to mixed problems.
Revised:  Additionally, we demonstrate how this DSL can be used to execute local post-processing techniques to achieve superconvergent approximation to mixed problems.
Edit intent: clarity
Original:  Additionally, we demonstrate how this DSL can be used to implement local post-processing techniques to achieve superconvergent approximation to mixed problems.
Revised:  Additionally, we demonstrate how this DSL can be used to implement local post-processing procedures to construct superconvergent approximations to mixed problems.
Edit intent: clarity
Original:  We present some examples for model second-order elliptic problems , including a new hybridization preconditioner for the linearized system in a nonlinear method for a simplified atmospheric model .
Revised:  We validate our implementations within the context of model second-order elliptic problems , including a new hybridization preconditioner for the linearized system in a nonlinear method for a simplified atmospheric model .
Edit intent: clarity
Original:  This DSL is general enough to facilitate the automatic generation of element-based dense linear algebra kernels necessary for the implementation of static condensation methods and local solvers for a variety of problems.
Revised:  This compact framework is general enough to facilitate the automatic generation of element-based dense linear algebra kernels necessary for the implementation of static condensation methods and local solvers for a variety of problems.
Edit intent: clarity
Original:  This work also features high-level implementations of static condensation and hybrid-mixed methods as preconditioners conforming to PETSc's interface for solving linear systems.
Revised:  This work features high-level implementations of static condensation and hybrid-mixed methods as preconditioners conforming to PETSc's interface for solving linear systems.
Edit intent: clarity
Original:  This work also features high-level implementations of static condensation and hybrid-mixed methods as preconditioners conforming to PETSc's interface for solving linear systems.
Revised:  This work also features high-level implementations conforming to PETSc's interface for solving linear systems.
Edit intent: clarity
Original:  The first variant, called Unrestricted Minimal Episodes Inference, aims at inferring a species tree based on a model of speciation and duplication where duplications are clustered in duplication episodes.
Revised:  The first variant, called Unrestricted Minimal Episodes Inference, aims at inferring a species tree based on a model with speciation and duplication where duplications are clustered in duplication episodes.
Edit intent: fluency
Original:  The second variant, Parental Hybridization, aims at inferring a species network based on a model of speciation and reticulation.
Revised:  The second variant, Parental Hybridization, aims at inferring a species network based on a model of speciation and reticulation.
Edit intent: fluency
Original:  The second variant, Parental Hybridization, aims at inferring a species network based on a model of speciation and reticulation.
Revised:  The second variant, Parental Hybridization, aims at inferring a species network based on a model with speciation and reticulation.
Edit intent: fluency
Original:  However, we also show that methods based on these problems have to be used with care because the optimal species phylogenies always have some restricted form.
Revised:  However, we also show that methods based on these problems have to be used with care because the optimal species phylogenies always have a restricted form.
Edit intent: clarity
Original:  To overcome this problem, we introduce a new variant of Unrestricted Minimal Episodes Inference that minimizes the duplication episode depth.
Revised:  To mitigate this problem, we introduce a new variant of Unrestricted Minimal Episodes Inference that minimizes the duplication episode depth.
Edit intent: clarity
Original:  We prove that this new variant of the problem can also be solved in polynomial time .
Revised:  We prove that this new variant of the problem can also be solved in polynomial time 
Edit intent: fluency
Original:  Here we propose a framework, based on  Jensen-Shannon divergence, to map the task-rest connectivity divergence between tasks and resting-state FC.
Revised:  Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity divergence between tasks and resting-state FC.
Edit intent: clarity
Original:  Here we propose a framework, based on  Jensen-Shannon divergence, to map the task-rest connectivity divergence between tasks and resting-state FC.
Revised:  Here we propose a framework, based on Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC.
Edit intent: clarity
Original:  We study resting-state and seven tasks from the Human Connectome Project dataset to obtain the most divergent links across tasks.
Revised:  We study resting-state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks.
Edit intent: clarity
Original:  Furthermore, we show how the FC divergence with respect to resting state is shaped by structural connectivity, and to what extent this relationship depends on the task.
Revised:  Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task.
Edit intent: clarity
Original:  We first demonstrate (under the realistic assumption of fixed energy supplies) the limitations of any fixed charging range and, therefore, the need for  and power of  a dynamic selection of the charging range, by adapting to the behavior of the mobile agents which is revealed in an online manner.
Revised:  We first demonstrate (under the realistic assumption of fixed energy supplies) the limitations of any fixed charging range and, therefore, the need for ( and power of a dynamic selection of the charging range, by adapting to the behavior of the mobile agents which is revealed in an online manner.
Edit intent: fluency
Original:  We first demonstrate (under the realistic assumption of fixed energy supplies) the limitations of any fixed charging range and, therefore, the need for  and power of  a dynamic selection of the charging range, by adapting to the behavior of the mobile agents which is revealed in an online manner.
Revised:  We first demonstrate (under the realistic assumption of fixed energy supplies) the limitations of any fixed charging range and, therefore, the need for and power of ) a dynamic selection of the charging range, by adapting to the behavior of the mobile agents which is revealed in an online manner.
Edit intent: fluency
Original:  We investigate the complexity of optimizing the selection of such an adaptive charging range, by showing that a simplified offline optimization problem (closely related to the online one) is NP-hard.
Revised:  We investigate the complexity of optimizing the selection of such an adaptive charging range, by showing that two simplified offline optimization problem (closely related to the online one) is NP-hard.
Edit intent: fluency
Original:  We investigate the complexity of optimizing the selection of such an adaptive charging range, by showing that a simplified offline optimization problem (closely related to the online one) is NP-hard.
Revised:  We investigate the complexity of optimizing the selection of such an adaptive charging range, by showing that a simplified offline optimization problems (closely related to the online one) is NP-hard.
Edit intent: fluency
Original:  We investigate the complexity of optimizing the selection of such an adaptive charging range, by showing that a simplified offline optimization problem (closely related to the online one) is NP-hard.
Revised:  We investigate the complexity of optimizing the selection of such an adaptive charging range, by showing that a simplified offline optimization problem (closely related to the online one) are NP-hard.
Edit intent: fluency
Original: In this work, we investigate the interesting impact of mobility on the problem of efficient wireless power transfer in ad hoc networks.
Revised: We investigate the interesting impact of mobility on the problem of efficient wireless power transfer in ad hoc networks.
Edit intent: clarity
Original: Neural word embeddings have been widely used in biomedical Natural Language Processing (NLP) applications since they provide vector representations of words that capture the semantic properties of words and the linguistic relationship between words.
Revised: Neural word embeddings have been widely used in biomedical Natural Language Processing (NLP) applications as they provide vector representations of words that capture the semantic properties of words and the linguistic relationship between words.
Edit intent: coherence
Original: Neural word embeddings have been widely used in biomedical Natural Language Processing (NLP) applications since they provide vector representations of words that capture the semantic properties of words and the linguistic relationship between words.
Revised: Neural word embeddings have been widely used in biomedical Natural Language Processing (NLP) applications since they provide vector representations of words capturing the semantic properties of words and the linguistic relationship between words.
Edit intent: clarity
Original:  However, there has been little work on comprehensively evaluating the word embeddings trained from these resources.
Revised:  However, there has been little work on evaluating the word embeddings trained from these resources.
Edit intent: clarity
Original: In this study, we provide a comprehensive empirical evaluation of word embeddings trained from four different resources, namely clinical notes, biomedical publications, Wikepedia , and news.
Revised: In this study, we provide an empirical evaluation of word embeddings trained from four different resources, namely clinical notes, biomedical publications, Wikepedia , and news.
Edit intent: clarity
Original: In this study, we provide a comprehensive empirical evaluation of word embeddings trained from four different resources, namely clinical notes, biomedical publications, Wikepedia , and news.
Revised: In this study, we provide a comprehensive empirical evaluation of word embeddings trained from four different resources, namely clinical notes, biomedical publications, Wikipedia , and news.
Edit intent: fluency
Original:  We perform the evaluation qualitatively and quantitatively.
Revised:  We performed the evaluation qualitatively and quantitatively.
Edit intent: fluency
Original:  In qualitative evaluation, we manually inspect five most similar medical words to a given set of target medical words, and then analyze word embeddings through the visualization of those word embeddings.
Revised:  In qualitative evaluation, we manually inspected five most similar medical words to a given set of target medical words, and then analyze word embeddings through the visualization of those word embeddings.
Edit intent: fluency
Original:  In qualitative evaluation, we manually inspect five most similar medical words to a given set of target medical words, and then analyze word embeddings through the visualization of those word embeddings.
Revised:  In qualitative evaluation, we manually inspect five most similar medical words to a given set of target medical words, and then analyzed word embeddings through the visualization of those word embeddings.
Edit intent: fluency
Original:  Quantitative evaluationfalls into two categories: extrinsic and intrinsic evaluation.
Revised:  In quantitative evaluation, we conducted both intrinsic and extrinsic evaluation.
Edit intent: clarity
Original:  First,  EHR and PubMed can capture the semantics of medical terms better than  GloVe and Google News and find more relevant similar medical terms . Second, the medical semantic similarity captured by the word embeddings trained on EHR and PubMed are closer to human experts' judgments, compared to these trained on GloVe and Google News.
Revised:  First, the word embeddings trained on EHR and PubMed can capture the semantics of medical terms better than GloVe and Google News and find more relevant similar medical terms . Second, the medical semantic similarity captured by the word embeddings trained on EHR and PubMed are closer to human experts' judgments, compared to these trained on GloVe and Google News.
Edit intent: fluency
Original:  First,  EHR and PubMed can capture the semantics of medical terms better than  GloVe and Google News and find more relevant similar medical terms . Second, the medical semantic similarity captured by the word embeddings trained on EHR and PubMed are closer to human experts' judgments, compared to these trained on GloVe and Google News.
Revised:  First, EHR and PubMed can capture the semantics of medical terms better than those trained on GloVe and Google News and find more relevant similar medical terms . Second, the medical semantic similarity captured by the word embeddings trained on EHR and PubMed are closer to human experts' judgments, compared to these trained on GloVe and Google News.
Edit intent: clarity
Original:  First,  EHR and PubMed can capture the semantics of medical terms better than  GloVe and Google News and find more relevant similar medical terms . Second, the medical semantic similarity captured by the word embeddings trained on EHR and PubMed are closer to human experts' judgments, compared to these trained on GloVe and Google News.
Revised:  First, EHR and PubMed can capture the semantics of medical terms better than GloVe and Google News and find more relevant similar medical terms , and are closer to human experts' judgments, compared to these trained on GloVe and Google News.
Edit intent: coherence
Original:  Third , there does not exist a consistent global ranking of word embedding quality for downstream biomedical NLP applications.
Revised:  Second , there does not exist a consistent global ranking of word embedding quality for downstream biomedical NLP applications.
Edit intent: fluency
Original:  Finally,  word embeddings trained from a similar domain corpus do not necessarily have better performance than other word embeddings for any downstream biomedical  tasks.
Revised:  Finally, the word embeddings trained from a similar domain corpus do not necessarily have better performance than other word embeddings for any downstream biomedical tasks.
Edit intent: fluency
Original:  Finally,  word embeddings trained from a similar domain corpus do not necessarily have better performance than other word embeddings for any downstream biomedical  tasks.
Revised:  Finally, word embeddings trained on biomedical domain corpora do not necessarily have better performance than other word embeddings for any downstream biomedical tasks.
Edit intent: clarity
Original: Neural word embeddings have been widely used in biomedical Natural Language Processing (NLP) applications as they provide vector representations of words capturing the semantic properties of words and the linguistic relationship between words.
Revised: Word embeddings have been widely used in biomedical Natural Language Processing (NLP) applications as they provide vector representations of words capturing the semantic properties of words and the linguistic relationship between words.
Edit intent: clarity
Original:  In qualitative evaluation, we manually inspected five most similar medical words to a given set of target medical words, and then analyzed word embeddings through the visualization of those word embeddings.
Revised:  For the qualitative evaluation, we manually inspected five most similar medical words to a given set of target medical words, and then analyzed word embeddings through the visualization of those word embeddings.
Edit intent: fluency
Original:  In quantitative evaluation, we conducted both intrinsic and extrinsic evaluation.
Revised:  For the quantitative evaluation, we conducted both intrinsic and extrinsic evaluation.
Edit intent: fluency
Original:  First, the word embeddings trained on EHR and PubMed can capture the semantics of medical terms better than those trained on GloVe and Google News and find more relevant similar medical terms, and are closer to human experts' judgments, compared to these trained on GloVe and Google News . Second, there does not exist a consistent global ranking of word embedding quality for downstream biomedical NLP applications.
Revised:  First, the word embeddings trained on clinical notes and biomedical publications can capture the semantics of medical terms better than those trained on GloVe and Google News and find more relevant similar medical terms, and are closer to human experts' judgments, compared to these trained on GloVe and Google News . Second, there does not exist a consistent global ranking of word embedding quality for downstream biomedical NLP applications.
Edit intent: clarity
Original:  First, the word embeddings trained on EHR and PubMed can capture the semantics of medical terms better than those trained on GloVe and Google News and find more relevant similar medical terms, and are closer to human experts' judgments, compared to these trained on GloVe and Google News . Second, there does not exist a consistent global ranking of word embedding quality for downstream biomedical NLP applications.
Revised:  First, the word embeddings trained on EHR and PubMed can capture the semantics of medical terms better , and find more relevant similar medical terms, and are closer to human experts' judgments, compared to these trained on GloVe and Google News . Second, there does not exist a consistent global ranking of word embedding quality for downstream biomedical NLP applications.
Edit intent: clarity
Original: Agent-based modeling has been criticized for its apparent lack of establishing causality of social phenomena.
Revised: Agent-based modeling of artificial societies offers a platform to test human-interpretable, causal explanations of human behavior that generate society-scale phenomena.
Edit intent: clarity
Original: The use of multiple drugs , termed polypharmacy, is common to treat patients with complex diseases or co-existing medical conditions.
Revised: The use of drug combinations , termed polypharmacy, is common to treat patients with complex diseases or co-existing medical conditions.
Edit intent: clarity
Original: The use of multiple drugs , termed polypharmacy, is common to treat patients with complex diseases or co-existing medical conditions.
Revised: The use of multiple drugs , termed polypharmacy, is common to treat patients with complex diseases and co-existing medical conditions.
Edit intent: fluency
Original: The use of multiple drugs , termed polypharmacy, is common to treat patients with complex diseases or co-existing medical conditions.
Revised: The use of multiple drugs , termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions.
Edit intent: clarity
Original:  However, a major consequence of polypharmacy is a much higher risk of  side effects for the patient.
Revised:  However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient.
Edit intent: clarity