wanyu/IteraTeR_v2 huggingface dataset:
Original:  Among these groups, we find the probability of becoming infected is highest for caregivers and second highest for disabled people.
Revised:  Among these groups, we find the probability of becoming infected is highest for caregivers and second largest for disabled people.
Edit intent: clarity
Original:  Our analysis of the network structure illustrates that caregivers have the largest modal eigenvector centrality among the four groups .
Revised:  Our analysis of network structure illustrates that caregivers have the largest modal eigenvector centrality among the four groups .
Edit intent: fluency
Original:  Our analysis of the network structure illustrates that caregivers have the largest modal eigenvector centrality among the four groups .
Revised:  Our analysis of the network structure illustrates that caregivers have the largest modal eigenvector centrality .
Edit intent: clarity
Original:  We find that two interventions -- contact-limiting by all groups and mask-wearing by disabled people and caregivers -- particularly reduce cases among disabled people and caregivers.
Revised:  We find that two interventions -- contact-limiting by all groups and mask-wearing by disabled people and caregivers -- most reduce the cases among disabled people and caregivers.
Edit intent: clarity
Original:  We also test which group most effectively spreads COVID-19  by seeding infections in a subset of each group and then comparing the total number of infections as the disease spreads .
Revised:  We also test which group spreads COVID-19 by seeding infections in a subset of each group and then comparing the total number of infections as the disease spreads .
Edit intent: clarity
Original:  We also test which group most effectively spreads COVID-19  by seeding infections in a subset of each group and then comparing the total number of infections as the disease spreads .
Revised:  We also test which group most effectively spreads COVID-19 by seeding infections in a subset of each group .
Edit intent: clarity
Original:  We find that caregivers are the most effective spreaders of COVID-19 . We then test where limited vaccine doses could be used most effectively and we find that vaccinating caregivers better protects disabled people than vaccinating the general population , essential workers , or the disabled population itself .
Revised:  We find caregivers are the most effective spreaders of COVID-19 . We then test where limited vaccine doses could be used most effectively and we find that vaccinating caregivers better protects disabled people than vaccinating the general population , essential workers , or the disabled population itself .
Edit intent: fluency
Original:  We find that caregivers are the most effective spreaders of COVID-19 . We then test where limited vaccine doses could be used most effectively and we find that vaccinating caregivers better protects disabled people than vaccinating the general population , essential workers , or the disabled population itself .
Revised:  We find that caregivers are the most potent spreaders of COVID-19 . We then test where limited vaccine doses could be used most effectively and we find that vaccinating caregivers better protects disabled people than vaccinating the general population , essential workers , or the disabled population itself .
Edit intent: clarity
Original:  We find that caregivers are the most effective spreaders of COVID-19 . We then test where limited vaccine doses could be used most effectively and we find that vaccinating caregivers better protects disabled people than vaccinating the general population , essential workers , or the disabled population itself .
Revised:  We find that caregivers are the most effective spreaders of COVID-19 . We then test where limited vaccine doses most effectively and we find that vaccinating caregivers better protects disabled people than vaccinating the general population , essential workers , or the disabled population itself .
Edit intent: clarity
Original: We study the query complexity of local search and Brouwer fixed-point computation when there are k rounds of interaction with the oracle that answers queries.
Revised: We consider the query complexity of local search and Brouwer fixed-point computation when there are k rounds of interaction with the oracle that answers queries.
Edit intent: clarity
Original:  This model captures distributed settings, where each query is time consuming and can be executed by a separate processor to speed up computation time .
Revised:  This model captures distributed settings, where each query takes resources to complete and is executed on a separate processor to speed up computation time .
Edit intent: clarity
Original:  We present several new ] algorithms and lower bounds , which characterize the trade-off between the number of rounds of adaptivity and the total number of queries on local searchand Brouwer fixed-point.
Revised:  We present several new ] algorithms and lower bounds that characterize the trade-off between the number of rounds of adaptivity and the total number of queries on local searchand Brouwer fixed-point.
Edit intent: fluency
Original:  We mainly focus on studying these problems on the d-dimensional grid n%DIFDELCMD < ]%%% ^d, where d is a constant.
Revised:  We mainly focus on studying these problems on the d-dimensional grid %DIFDELCMD < ]%%% ^d, where d is a constant.
Edit intent: clarity
Original:  For local search, if the number of rounds k is a constant, then we obtain a query complexity of  \Theta\bigl(n^{d^{k+1 - d^k}{d^k - 1}}\bigl)  for both deterministic and randomized algorithms.
Revised:  For local search, if the number of rounds k is constant, we show that the query complexity of \Theta\bigl(n^{d^{k+1 - d^k}{d^k - 1}}\bigl) for both deterministic and randomized algorithms.
Edit intent: clarity
Original:  For local search, if the number of rounds k is a constant, then we obtain a query complexity of  \Theta\bigl(n^{d^{k+1 - d^k}{d^k - 1}}\bigl)  for both deterministic and randomized algorithms.
Revised:  For local search, if the number of rounds k is a constant, then we obtain a query complexity of \Theta\bigl(n^{d^{k+1 - d^k}{d^k - 1}}\bigl) , for both deterministic and randomized algorithms.
Edit intent: fluency
Original:  On the other hand, when the number of rounds is polynomial, i.e. of the form n = k ^{\alpha, where \alpha is a constant with }  0 < \alpha < d/2, we obtain an upper bound of O \left(n^{ ( d-1 ) - d-2{d}\alpha}\right) and a lower bound of%DIFDELCMD < \widetilde{\Omega}\bigl(%%% \max(n^{(d-1)-\alpha, n^{\frac{d}{2}})}%DIFDELCMD < \bigr) %%% for randomized algorithms.
Revised:  When the number of rounds is polynomial, i.e. of the form n = k ^{\alpha, where \alpha is a constant with } 0 < \alpha < d/2, we obtain an upper bound of O \left(n^{ ( d-1 ) - d-2{d}\alpha}\right) and a lower bound of%DIFDELCMD < \widetilde{\Omega}\bigl(%%% \max(n^{(d-1)-\alpha, n^{\frac{d}{2}})}%DIFDELCMD < \bigr) %%% for randomized algorithms.
Edit intent: coherence
Original:  On the other hand, when the number of rounds is polynomial, i.e. of the form n = k ^{\alpha, where \alpha is a constant with }  0 < \alpha < d/2, we obtain an upper bound of O \left(n^{ ( d-1 ) - d-2{d}\alpha}\right) and a lower bound of%DIFDELCMD < \widetilde{\Omega}\bigl(%%% \max(n^{(d-1)-\alpha, n^{\frac{d}{2}})}%DIFDELCMD < \bigr) %%% for randomized algorithms.
Revised:  On the other hand, when the number of rounds is polynomial, i.e. , where \alpha is a constant with } 0 < \alpha < d/2, we obtain an upper bound of O \left(n^{ ( d-1 ) - d-2{d}\alpha}\right) and a lower bound of%DIFDELCMD < \widetilde{\Omega}\bigl(%%% \max(n^{(d-1)-\alpha, n^{\frac{d}{2}})}%DIFDELCMD < \bigr) %%% for randomized algorithms.
Edit intent: clarity
Original:  On the other hand, when the number of rounds is polynomial, i.e. of the form n = k ^{\alpha, where \alpha is a constant with }  0 < \alpha < d/2, we obtain an upper bound of O \left(n^{ ( d-1 ) - d-2{d}\alpha}\right) and a lower bound of%DIFDELCMD < \widetilde{\Omega}\bigl(%%% \max(n^{(d-1)-\alpha, n^{\frac{d}{2}})}%DIFDELCMD < \bigr) %%% for randomized algorithms.
Revised:  On the other hand, when the number of rounds is polynomial, i.e. of the form n = k ^{\alpha, where \alpha is a constant with } 0 < \alpha < d/2, we obtain an upper bound of O \left(n^{ d-1 ) - d-2{d}\alpha}\right) and a lower bound of%DIFDELCMD < \widetilde{\Omega}\bigl(%%% \max(n^{(d-1)-\alpha, n^{\frac{d}{2}})}%DIFDELCMD < \bigr) %%% for randomized algorithms.
Edit intent: fluency
Original:  On the other hand, when the number of rounds is polynomial, i.e. of the form n = k ^{\alpha, where \alpha is a constant with }  0 < \alpha < d/2, we obtain an upper bound of O \left(n^{ ( d-1 ) - d-2{d}\alpha}\right) and a lower bound of%DIFDELCMD < \widetilde{\Omega}\bigl(%%% \max(n^{(d-1)-\alpha, n^{\frac{d}{2}})}%DIFDELCMD < \bigr) %%% for randomized algorithms.
Revised:  On the other hand, when the number of rounds is polynomial, i.e. of the form n = k ^{\alpha, where \alpha is a constant with } 0 < \alpha < d/2, we obtain an upper bound of O \left(n^{ ( d-1 - d-2{d}\alpha}\right) and a lower bound of%DIFDELCMD < \widetilde{\Omega}\bigl(%%% \max(n^{(d-1)-\alpha, n^{\frac{d}{2}})}%DIFDELCMD < \bigr) %%% for randomized algorithms.
Edit intent: fluency
Original:  On the other hand, when the number of rounds is polynomial, i.e. of the form n = k ^{\alpha, where \alpha is a constant with }  0 < \alpha < d/2, we obtain an upper bound of O \left(n^{ ( d-1 ) - d-2{d}\alpha}\right) and a lower bound of%DIFDELCMD < \widetilde{\Omega}\bigl(%%% \max(n^{(d-1)-\alpha, n^{\frac{d}{2}})}%DIFDELCMD < \bigr) %%% for randomized algorithms.
Revised:  On the other hand, when the number of rounds is polynomial, i.e. of the form n = k ^{\alpha, where \alpha is a constant with } 0 < \alpha < d/2, we obtain an upper bound of O \left(n^{ ( d-1 ) - d-2{d}\alpha}\right) %DIFDELCMD < \widetilde{\Omega}\bigl(%%% \max(n^{(d-1)-\alpha, n^{\frac{d}{2}})}%DIFDELCMD < \bigr) %%% for randomized algorithms.
Edit intent: clarity
Original:  This molecular cross-talk can cause a substantial interference on MC. Here we consider a particular scenario, where there is non-negligible concentration of interferer molecules in the channel, which have similar binding characteristics with the information molecules, and the receiver employs single type of receptors.
Revised:  This molecular cross-talk can cause a substantial interference on MC. Here we consider a particular scenario, where there is non-negligible concentration of interferer molecules in the channel, which have similar receptor-binding characteristics with the information molecules, and the receiver employs single type of receptors.
Edit intent: clarity
Original:  The performance of the introduced detection methods are evaluated in terms of bit error probability for varying strength of molecular interference, similarity between information and interferer molecules, and number of receptors  .
Revised:  The performance of the introduced detection methods are evaluated in terms of bit error probability for varying strength of molecular interference, similarity between information and interferer molecules, number of receptors .
Edit intent: fluency
Original: The Dual Matrix Algorithm  , variations of which were proposed in A.
Revised: The Dual Matrix Algorithm , variations of which were A.
Edit intent: clarity
Original:  It offers substantial flexibility and, thus, potential for further development .
Revised:  It offers substantial flexibility and, thus, potential for further developments .
Edit intent: fluency
Original:  By utilising the fact that the DSO game is a potential game, we prove that a globally optimal state is stochastically stable under the logit response dynamics, and the better/best response dynamics converges to locally optimal states .
Revised:  By utilising the fact that the DSO game is a potential game, we prove that a globally optimal state is a stochastically stable state under the logit response dynamics, and the better/best response dynamics converges to locally optimal states .
Edit intent: fluency
Original:  By utilising the fact that the DSO game is a potential game, we prove that a globally optimal state is stochastically stable under the logit response dynamics, and the better/best response dynamics converges to locally optimal states .
Revised:  By utilising the fact that the DSO game is a potential game, we prove that a globally optimal state is stochastically stable under the logit response dynamics, and the better/best response dynamics converges to a locally optimal state .
Edit intent: fluency
Original: Objective: Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value to medical researchers.
Revised:  Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value to medical researchers.
Edit intent: clarity
Original: Objective: Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value to medical researchers.
Revised: Objective: Electronic Medical Records contain clinical narrative text that is of great potential value to medical researchers.
Edit intent: clarity
Original:  This paper presents an end-to-end de-identification framework to automatically remove PHI from hospital discharge summaries.
Revised:  This paper presents an end-to-end de-identification framework to automatically remove PII from hospital discharge summaries.
Edit intent: clarity
Original:  Materials and Methods: Our corpus included 600 hospital discharge summaries which were extracted from the EMRs of two principal referral hospitals in Sydney, Australia.
Revised:  Our corpus included 600 hospital discharge summaries which were extracted from the EMRs of two principal referral hospitals in Sydney, Australia.
Edit intent: coherence
Original:  Our end-to-end de-identification framework consists of three components: 1) Annotation: labelling of PHI in the 600 hospital discharge summaries using five pre-defined categories: person, address, date of birth, individual identification number, phone/fax number;
Revised:  Our end-to-end de-identification framework consists of three components: 1) Annotation: labelling of PII in the hospital discharge summaries using five pre-defined categories: person, address, date of birth, individual identification number, phone/fax number;
Edit intent: clarity
Original:  2) Modelling: training and evaluating ensembles of named entity recognition (NER) models through the use of three natural language processing (NLP) toolkits (Stanza, FLAIR and spaCy) and both balanced and imbalanced datasets;
Revised:  2) Modelling: training and evaluating ensembles of named entity recognition deep learning base-models on balanced and imbalanced datasets;
Edit intent: clarity
Original:  and  3) De-identification: removing PHI from the hospital discharge summaries.
Revised:  and 3) De-identification: removing PII from the hospital discharge summaries.
Edit intent: clarity
Original: Electronic Medical Records  contain clinical narrative text that is of great potential value to medical researchers.
Revised: Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value to medical researchers.
Edit intent: clarity
Original:  Our end-to-end de-identification framework consists of three components: 1) Annotation: labelling of PII in the  hospital discharge summaries using five pre-defined categories: person, address, date of birth, individual identification number, phone /fax number;
Revised:  Our end-to-end de-identification framework consists of three components: 1) Annotation: labelling of PII in the hospital discharge summaries using five pre-defined categories: person, address, date of birth, identification number, phone /fax number;
Edit intent: clarity
Original:  Our end-to-end de-identification framework consists of three components: 1) Annotation: labelling of PII in the  hospital discharge summaries using five pre-defined categories: person, address, date of birth, individual identification number, phone /fax number;
Revised:  Our end-to-end de-identification framework consists of three components: 1) Annotation: labelling of PII in the hospital discharge summaries using five pre-defined categories: person, address, date of birth, individual identification number, phone number;
Edit intent: fluency
Original:  The end-to-end framework provides a robust solution to de-identifying clinical narrative corpuses safely.
Revised:  The framework provides a robust solution to de-identifying clinical narrative corpuses safely.
Edit intent: clarity
Original:  The end-to-end framework provides a robust solution to de-identifying clinical narrative corpuses safely.
Revised:  The end-to-end framework provides a robust solution to de-identifying clinical narrative text safely.
Edit intent: fluency
Original: The approximate basis computation of vanishing ideals has recently been extensively studied and applied both in computer algebra and data-driven applications such as machine learning.
Revised: In recent years, the approximate basis computation of vanishing ideals has recently been extensively studied and applied both in computer algebra and data-driven applications such as machine learning.
Edit intent: clarity
Original: The approximate basis computation of vanishing ideals has recently been extensively studied and applied both in computer algebra and data-driven applications such as machine learning.
Revised: The approximate basis computation of vanishing ideals has been studied extensively and adopted both in computer algebra and data-driven applications such as machine learning.
Edit intent: clarity
Original:  However, the symbolic computation and the dependency on the monomial ordering remain an essential gap between the two  fields.
Revised:  However, symbolic computation and the dependency on the monomial ordering remain an essential gap between the two fields.
Edit intent: fluency
Original:  However, the symbolic computation and the dependency on the monomial ordering remain an essential gap between the two  fields.
Revised:  However, the symbolic computation and the dependency on monomial ordering remain an essential gap between the two fields.
Edit intent: fluency
Original:  However, the symbolic computation and the dependency on the monomial ordering remain an essential gap between the two  fields.
Revised:  However, the symbolic computation and the dependency on the monomial ordering remain as essential gaps between the two fields.
Edit intent: fluency
Original:  However, the symbolic computation and the dependency on the monomial ordering remain an essential gap between the two  fields.
Revised:  However, the symbolic computation and the dependency on the monomial ordering remain an essential gap between the two above-mentioned fields.
Edit intent: clarity
Original:  In the present paper, we propose the first efficient monomial-agnostic approximate basis computation of vanishing ideals, where polynomials are manipulated without any information of monomials , which can be implemented in a fully-numerical manner and thus desirable for data-driven applications.
Revised:  In this paper, we propose the first efficient monomial-agnostic approximate basis computation of vanishing ideals, where polynomials are manipulated without any information of monomials , which can be implemented in a fully-numerical manner and thus desirable for data-driven applications.
Edit intent: coherence
Original:  In the present paper, we propose the first efficient monomial-agnostic approximate basis computation of vanishing ideals, where polynomials are manipulated without any information of monomials , which can be implemented in a fully-numerical manner and thus desirable for data-driven applications.
Revised:  In the present paper, we propose the first efficient monomial-agnostic approximate basis computation of vanishing ideals, where polynomials are manipulated without any information of monomials ; this can be implemented in a fully-numerical manner and thus desirable for data-driven applications.
Edit intent: coherence
Original:  In the present paper, we propose the first efficient monomial-agnostic approximate basis computation of vanishing ideals, where polynomials are manipulated without any information of monomials , which can be implemented in a fully-numerical manner and thus desirable for data-driven applications.
Revised:  In the present paper, we propose the first efficient monomial-agnostic approximate basis computation of vanishing ideals, where polynomials are manipulated without any information of monomials , which can be implemented in a fully numerical manner and is thus desirable for data-driven applications.
Edit intent: coherence
Original:  In particular, we propose the gradient normalization, which achieves not only the first efficient and monomial-agnostic normalization of polynomials but also brings fascinating properties such as translation- and scaling-consistency, which are not realized by any existing basis computation algorithms.
Revised:  In particular, we propose gradient normalization, which achieves not only the first efficient and monomial-agnostic normalization of polynomials but also brings fascinating properties such as translation- and scaling-consistency, which are not realized by any existing basis computation algorithms.
Edit intent: fluency
Original:  In particular, we propose the gradient normalization, which achieves not only the first efficient and monomial-agnostic normalization of polynomials but also brings fascinating properties such as translation- and scaling-consistency, which are not realized by any existing basis computation algorithms.
Revised:  In particular, we propose the gradient normalization, which achieves not only the first efficient and monomial-agnostic normalization of polynomials but also provides significant advantages such as consistency in translation and scaling of data points, which cannot be realized by existing basis computation algorithms.
Edit intent: clarity
Original:  During the basis computation, the gradient of polynomials at  given points are proven to be efficiently and exactly obtained without performing differentiation.
Revised:  During the basis computation, the gradients of polynomials at given points are proven to be efficiently and exactly obtained without performing differentiation.
Edit intent: fluency
Original:  During the basis computation, the gradient of polynomials at  given points are proven to be efficiently and exactly obtained without performing differentiation.
Revised:  During the basis computation, the gradient of polynomials at the given points are proven to be efficiently and exactly obtained without performing differentiation.
Edit intent: fluency
Original:  By exploiting the gradient information, we further propose a basis set reduction method to remove redundant polynomials in a monomial-agnostic manner.
Revised:  By exploiting the gradient information, we further propose a basis reduction method to remove redundant polynomials in a monomial-agnostic manner.
Edit intent: clarity
Original:  A regularization method using gradient is also proposed for avoiding overfitting of the basis set to given points.
Revised:  Finally, we also propose a regularization method using gradient is also proposed for avoiding overfitting of the basis set to given points.
Edit intent: coherence
Original:  A regularization method using gradient is also proposed for avoiding overfitting of the basis set to given points.
Revised:  A regularization method using gradients to avoiding overfitting of the basis set to given points.
Edit intent: clarity
Original:  In this work, we use Graph Neural Networks to model the 11,790 URN samples across 30 cities worldwide and use its predictability to define the spatial homogeneity .
Revised:  In this study, we propose a graph-based machine-learning method to quantify the spatial homogeneity of subnetworks. We apply the method to 11,790 URN samples across 30 cities worldwide and use its predictability to define the spatial homogeneity .
Edit intent: clarity