wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Computing geodesic distance requires that the functional connectivity matrices being compared are invertible (or positive-definite ) . As this requirement depends on the fMRI scanning length and the parcellation used, it is not always attainable .
Revised:  Computing geodesic distance requires FCs to be positive-definite ) . As this requirement depends on the fMRI scanning length and the parcellation used, it is not always attainable .
Edit intent: clarity
Original:  Computing geodesic distance requires that the functional connectivity matrices being compared are invertible (or positive-definite ) . As this requirement depends on the fMRI scanning length and the parcellation used, it is not always attainable .
Revised:  Computing geodesic distance requires that the functional connectivity matrices being compared are invertible (or positive-definite and hence invertible matrices . As this requirement depends on the fMRI scanning length and the parcellation used, it is not always attainable .
Edit intent: meaning-changed
Original:  Computing geodesic distance requires that the functional connectivity matrices being compared are invertible (or positive-definite ) . As this requirement depends on the fMRI scanning length and the parcellation used, it is not always attainable . Hence Venkatesh and colleagues employed a simple regularization procedure to ensure that the matrices were invertible .
Revised:  Computing geodesic distance requires that the functional connectivity matrices being compared are invertible (or positive-definite ) . As this requirement depends on the fMRI scanning length and the parcellation used, it is not always attainable and sometimes a regularization procedure is required .
Edit intent: coherence
Original:  We also show that this optimal value of regularization is highly dataset-dependent, and varies as a function of condition, parcellation,  and the number of frames used to compute the functional connectomes .
Revised:  We also show that this optimal value of regularization is highly dataset-dependent, and varies as a function of condition, parcellation, and the number of frames used to compute the FCs. We demonstrate that a universally fixed regularization does not fully uncover the potential of geodesic distance on individual fingerprinting, and indeed could severely diminish it. Thus, an optimal regularization must be estimated on each dataset to uncover the most differentiable across-subject and reproducible within-subject geodesic distances between FCs. The resulting pairwise geodesic distances at the optimal regularization level constitute a very reliable quantification of differences between subjects .
Edit intent: meaning-changed
Original: We have collected a large set of 377 publicly available complete genome sequences of the COVID-19 virus, the previously known flu-causing coronaviruses , HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1 , and the deadly pathogenic P3/P4 viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Revised: Today, we are all threatened by an unprecedented pandemic: COVID-19. How different is it from other coronaviruses? Will it be attenuated or become more virulent? Which animals may be its original host? In this study, we analyzed 377 publicly available complete genome sequences of the COVID-19 virus, the previously known flu-causing coronaviruses , HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1 , and the deadly pathogenic P3/P4 viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Edit intent: meaning-changed
Original: We have collected a large set of 377 publicly available complete genome sequences of the COVID-19 virus, the previously known flu-causing coronaviruses , HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1 , and the deadly pathogenic P3/P4 viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Revised: We have collected a large set of 377 publicly available complete genome sequences for the COVID-19 virus, the previously known flu-causing coronaviruses , HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1 , and the deadly pathogenic P3/P4 viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Edit intent: clarity
Original: We have collected a large set of 377 publicly available complete genome sequences of the COVID-19 virus, the previously known flu-causing coronaviruses , HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1 , and the deadly pathogenic P3/P4 viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Revised: We have collected a large set of 377 publicly available complete genome sequences of the COVID-19 virus, the previously known flu-causing coronaviruses ( HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1 , and the deadly pathogenic P3/P4 viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Edit intent: fluency
Original: We have collected a large set of 377 publicly available complete genome sequences of the COVID-19 virus, the previously known flu-causing coronaviruses , HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1 , and the deadly pathogenic P3/P4 viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Revised: We have collected a large set of 377 publicly available complete genome sequences of the COVID-19 virus, the previously known flu-causing coronaviruses , HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1 ) and the lethal, pathogenic P3/P4 viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Edit intent: fluency
Original:  This article reports a computational study of the similarities and the evolutionary relationships of COVID-19 strains from different countries and between COVID-19 and the related viruses using our newly developed big sequence data analysis tool I-MLCS, the existing MEGA 6.0 system, and a clustering algorithm.
Revised:  We found strong similarities between the current circulating COVID-19 strains from different countries and between COVID-19 and the related viruses using our newly developed big sequence data analysis tool I-MLCS, the existing MEGA 6.0 system, and a clustering algorithm.
Edit intent: clarity
Original:  This article reports a computational study of the similarities and the evolutionary relationships of COVID-19 strains from different countries and between COVID-19 and the related viruses using our newly developed big sequence data analysis tool I-MLCS, the existing MEGA 6.0 system, and a clustering algorithm.
Revised:  This article reports a computational study of the similarities and the evolutionary relationships of COVID-19 and the related viruses using our newly developed big sequence data analysis tool I-MLCS, the existing MEGA 6.0 system, and a clustering algorithm.
Edit intent: coherence
Original:  This article reports a computational study of the similarities and the evolutionary relationships of COVID-19 strains from different countries and between COVID-19 and the related viruses using our newly developed big sequence data analysis tool I-MLCS, the existing MEGA 6.0 system, and a clustering algorithm. Similarity analysis shows that two strains of the COVID-19 virus taking rhinolophine as host have very high similarities with the COVID-19 virus taking human as host, although the average similarity between the strains of the virus taking pangolin as host and taking human as host is higher.
Revised:  This article reports a computational study of the similarities and the evolutionary relationships of COVID-19 strains from different countries and between COVID-19 and SARS and MERS, as well as COVID-19 virus taking rhinolophine as host have very high similarities with the COVID-19 virus taking human as host, although the average similarity between the strains of the virus taking pangolin as host and taking human as host is higher.
Edit intent: meaning-changed
Original:  Similarity analysis shows that two strains of the COVID-19 virus taking rhinolophine as host have very high similarities with the COVID-19 virus taking human as host, although the average similarity between the strains of the virus taking pangolin as host and taking human as host is higher.
Revised:  Similarity analysis shows that two strains of the COVID-19 in rhinolophines and pangolins. On the contrary, COVID-19 virus taking human as host, although the average similarity between the strains of the virus taking pangolin as host and taking human as host is higher.
Edit intent: coherence
Original:  Similarity analysis shows that two strains of the COVID-19 virus taking rhinolophine as host have very high similarities with the COVID-19 virus taking human as host, although the average similarity between the strains of the virus taking pangolin as host and taking human as host is higher. Evolutionary relationship analysis shows that about 13 strains of the COVID-19 virus sequenced in 5 countriesare very likely to be connected to or can potentially lead researchers to find the first generation COVID-19 virus and its origin .
Revised:  Similarity analysis shows that two strains of the COVID-19 virus taking rhinolophine as host have very high similarities with the COVID-19 shares little similarity with the flu-causing coronaviruses and the other P3/P4 viruses. Strikingly, we observed divergence of COVID-19 virus sequenced in 5 countriesare very likely to be connected to or can potentially lead researchers to find the first generation COVID-19 virus and its origin .
Edit intent: meaning-changed
Original:  Evolutionary relationship analysis shows that about 13 strains of the COVID-19 virus sequenced in 5 countriesare very likely to be connected to or can potentially lead researchers to find the first generation COVID-19 virus and its origin .
Revised:  Evolutionary relationship analysis shows that about 13 strains of the COVID-19 strains isolated from human hosts has steadily increased from December 2019 to March 2020, suggesting COVID-19 virus and its origin .
Edit intent: meaning-changed
Original:  Evolutionary relationship analysis shows that about 13 strains of the COVID-19 virus sequenced in 5 countriesare very likely to be connected to or can potentially lead researchers to find the first generation COVID-19 virus and its origin .
Revised:  Evolutionary relationship analysis shows that about 13 strains of the COVID-19 virus sequenced in 5 countriesare very likely to be connected to or can potentially lead researchers to find the first generation COVID-19 is actively evolving in human hosts. From all existing human COVID-19 genome sequences, we calculated the first common model that represents the shared sequences of the human COVID-19 strains, which provides important information for vaccine and antibody development. Geographic and time-course analysis of the evolutionary trees of the human COVID-19 reveals possibly heterogeneous evolutional paths among strains from 21 countries. This finding has important implications to the management of COVID-19 and the development of vaccines .
Edit intent: meaning-changed
Original:  In this study, we analyzed 377 publicly available complete genome sequences for the COVID-19 virus, the previously known flu-causing coronaviruses (HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1) and the lethal, pathogenic P3/P4 viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Revised:  In this study, we analyzed 377 publicly available complete genome sequences for the COVID-19 virus, the previously known flu-causing coronaviruses (HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1) and the lethal, pathogenic viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Edit intent: meaning-changed
Original:  In this study, we analyzed 377 publicly available complete genome sequences for the COVID-19 virus  , the previously known flu-causing coronaviruses (HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1) and the lethal, pathogenic viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Revised:  In this study, we collected and analyzed nearly thirty thousand publicly available complete genome sequences for the COVID-19 virus , the previously known flu-causing coronaviruses (HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1) and the lethal, pathogenic viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Edit intent: meaning-changed
Original:  In this study, we analyzed 377 publicly available complete genome sequences for the COVID-19 virus  , the previously known flu-causing coronaviruses (HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1) and the lethal, pathogenic viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Revised:  In this study, we analyzed 377 publicly available complete genome sequences for COVID-19 virus , the previously known flu-causing coronaviruses (HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1) and the lethal, pathogenic viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Edit intent: fluency
Original:  In this study, we analyzed 377 publicly available complete genome sequences for the COVID-19 virus  , the previously known flu-causing coronaviruses (HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1) and the lethal, pathogenic viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Revised:  In this study, we analyzed 377 publicly available complete genome sequences for the COVID-19 virus from 79 different countries , the previously known flu-causing coronaviruses (HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1) and the lethal, pathogenic viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue.
Edit intent: meaning-changed
Original:  Strikingly, we observed  divergence of COVID-19 strains isolated from human hosts has steadily increased from December 2019 to March 2020, suggesting COVID-19 is actively evolving in human hosts.
Revised:  Strikingly, we observed that the divergence of COVID-19 strains isolated from human hosts has steadily increased from December 2019 to March 2020, suggesting COVID-19 is actively evolving in human hosts.
Edit intent: fluency
Original:  Strikingly, we observed  divergence of COVID-19 strains isolated from human hosts has steadily increased from December 2019 to March 2020, suggesting COVID-19 is actively evolving in human hosts.
Revised:  Strikingly, we observed divergence of COVID-19 strains isolated from human hosts has steadily increased from December 2019 to May 2020, suggesting COVID-19 is actively evolving in human hosts.
Edit intent: meaning-changed
Original:  From all existing human COVID-19 genome sequences, we calculated the first common model that represents the shared sequences of the human COVID-19 strains, which provides important information for vaccine and antibody development.
Revised:  In this paper, we first propose a novel MLCS algorithm NP-MLCS1 for the big sequence analysis, which can calculate the common model for COVID-19 genome sequences, we calculated the first common model that represents the shared sequences of the human COVID-19 strains, which provides important information for vaccine and antibody development.
Edit intent: meaning-changed
Original:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized Richards model to the reported number of infected cases from Jan. 19 to March 10 for the whole of China, 29 provinces in China, four severely affected countries (Japan, South Korea, Iran, Italy) and Europe as a whole .
Revised:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized Richards model to the reported number of infected cases from Jan. 19 to March 10 for the whole of China, 29 provinces in China, four severely affected countries (Japan, South Korea, Iran, Italy) and Europe as a whole .
Edit intent: fluency
Original:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized Richards model to the reported number of infected cases from Jan. 19 to March 10 for the whole of China, 29 provinces in China, four severely affected countries (Japan, South Korea, Iran, Italy) and Europe as a whole .
Revised:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized growth model to the reported number of infected cases from Jan. 19 to March 10 for the whole of China, 29 provinces in China, four severely affected countries (Japan, South Korea, Iran, Italy) and Europe as a whole .
Edit intent: clarity
Original:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized Richards model to the reported number of infected cases from Jan. 19 to March 10 for the whole of China, 29 provinces in China, four severely affected countries (Japan, South Korea, Iran, Italy) and Europe as a whole .
Revised:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized Richards model to the reported number of infected cases from for the whole of China, 29 provinces in China, four severely affected countries (Japan, South Korea, Iran, Italy) and Europe as a whole .
Edit intent: clarity
Original:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized Richards model to the reported number of infected cases from Jan. 19 to March 10 for the whole of China, 29 provinces in China, four severely affected countries (Japan, South Korea, Iran, Italy) and Europe as a whole .
Revised:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized Richards model to the reported number of infected cases from Jan. 19 to March 10 for the whole of China, 29 provinces in China, and 19 countries and regions that are undergoing major outbreaks .
Edit intent: meaning-changed
Original:  However for other countries, it is almost inevitable to see the continuation of the outbreak in the coming months. Japan and Italy are in serious situations with no short-term end to the outbreak to be expected. There is a significant risk concerning the upcoming July 2020 Summer Olympics in Tokyo. Iran's situation is highly uncertain with unclear and negative future scenarios, while South Korea is approaching the end of the outbreak. Both Europe and the USA are at early stages of the outbreak , posing significant health and economic risks to the world in the absence of serious measures .
Revised:  However for other countries, it is almost inevitable to see the continuation of the outbreak in other countries. We identified that Europe and US have passed the inflection point and entered into an after-peak trajectory, which is estimated longer than what a classical Logistic model predicts, in contrast to most provinces in China where the after-peak trajectory is much faster. We expect Europe to have 1.83 million final total confirmed cases (2452 per million population) and US to have 1.26 million final total confirmed cases (3851 per million population). We identified three groups of countries in different level of outbreak progress, and the USA are at early stages of the outbreak , posing significant health and economic risks to the world in the absence of serious measures .
Edit intent: meaning-changed
Original:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases from for the whole of China, 29 provinces in China, and 19 countries and regions that  are undergoing major outbreaks.
Revised:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases for the whole of China, 29 provinces in China, and 19 countries and regions that are undergoing major outbreaks.
Edit intent: fluency
Original:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases from for the whole of China, 29 provinces in China, and 19 countries and regions that  are undergoing major outbreaks.
Revised:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases from for the whole of China, 29 provinces in China, and 33 countries and regions that are undergoing major outbreaks.
Edit intent: meaning-changed
Original:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases from for the whole of China, 29 provinces in China, and 19 countries and regions that  are undergoing major outbreaks.
Revised:  We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases from for the whole of China, 29 provinces in China, and 19 countries and regions that have been or are undergoing major outbreaks.
Edit intent: clarity
Original:  We identified that Europe and US have passed the inflection point and entered into an after-peak trajectory, which is estimated longer than what a classical Logistic model predicts , in contrast to most provinces in China where the after-peak trajectory is much faster.
Revised:  We identified that outbreaks in 14 countries (mostly in western Europe) have ended, while resurgences of cases have been identified in several among them. The modeling results clearly show longer after-peak trajectory, which is estimated longer than what a classical Logistic model predicts , in contrast to most provinces in China where the after-peak trajectory is much faster.
Edit intent: meaning-changed
Original:  We identified that Europe and US have passed the inflection point and entered into an after-peak trajectory, which is estimated longer than what a classical Logistic model predicts , in contrast to most provinces in China where the after-peak trajectory is much faster.
Revised:  We identified that Europe and US have passed the inflection point and entered into an after-peak trajectories in western countries , in contrast to most provinces in China where the after-peak trajectory is much faster.
Edit intent: meaning-changed
Original:  We identified that Europe and US have passed the inflection point and entered into an after-peak trajectory, which is estimated longer than what a classical Logistic model predicts , in contrast to most provinces in China where the after-peak trajectory is much faster. We expect Europe to have 1.83 million final total confirmed cases (2452 per million population) and US to have 1.26 million final total confirmed cases (3851 per million population) .
Revised:  We identified that Europe and US have passed the inflection point and entered into an after-peak trajectory, which is estimated longer than what a classical Logistic model predicts , in contrast to most provinces in China where the after-peak trajectory is characterized by a much faster decay .
Edit intent: clarity
Original:  We consider a robust versionof this revenue-maximization pricing problem, where the seller knows the buyer's marginal distributions of values for each item  , but not the joint distribution, and wishes to maximize worst-case revenue over all possible correlation structures.
Revised:  In this robust version, the seller knows the buyer's marginal distributions of values for each item , but not the joint distribution, and wishes to maximize worst-case revenue over all possible correlation structures.
Edit intent: clarity