wanyu/IteraTeR_full_sent huggingface dataset:
Original:  our result is the first sub-linear time algorithm having the sample size {\em   independent of both the input number of points n and dimensionality d  . Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, k-center clustering, and SVM with outliers, and therefore achieve  sub-linear time bi-criteria approximation algorithms for these problems respectively.
Revised:  our result is the first sub-linear time algorithm having the sample size {\em i.e., independent of both the input number of points n and dimensionality d . Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, k-center clustering, and SVM with outliers, and therefore achieve sub-linear time bi-criteria approximation algorithms for these problems respectively.
Edit intent: meaning-changed
Original:  our result is the first sub-linear time algorithm having the sample size {\em   independent of both the input number of points n and dimensionality d  . Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, k-center clustering, and SVM with outliers, and therefore achieve  sub-linear time bi-criteria approximation algorithms for these problems respectively.
Revised:  our result is the first sub-linear time algorithm having the sample size {\em the number of sampled points) independent of both the input number of points n and dimensionality d . Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, k-center clustering, and SVM with outliers, and therefore achieve sub-linear time bi-criteria approximation algorithms for these problems respectively.
Edit intent: meaning-changed
Original:  our result is the first sub-linear time algorithm having the sample size {\em   independent of both the input number of points n and dimensionality d  . Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, k-center clustering, and SVM with outliers, and therefore achieve  sub-linear time bi-criteria approximation algorithms for these problems respectively.
Revised:  our result is the first sub-linear time algorithm having the sample size {\em independent of both the number of input points n and dimensionality d . Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, k-center clustering, and SVM with outliers, and therefore achieve sub-linear time bi-criteria approximation algorithms for these problems respectively.
Edit intent: fluency
Original:  our result is the first sub-linear time algorithm having the sample size {\em   independent of both the input number of points n and dimensionality d  . Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, k-center clustering, and SVM with outliers, and therefore achieve  sub-linear time bi-criteria approximation algorithms for these problems respectively.
Revised:  our result is the first sub-linear time algorithm having the sample size {\em independent of both the input number of points n and dimensionality d , for MEB with outliers in high dimensions . Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, k-center clustering, and SVM with outliers, and therefore achieve sub-linear time bi-criteria approximation algorithms for these problems respectively.
Edit intent: meaning-changed
Original:  our result is the first sub-linear time algorithm having the sample size {\em   independent of both the input number of points n and dimensionality d  . Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, k-center clustering, and SVM with outliers, and therefore achieve  sub-linear time bi-criteria approximation algorithms for these problems respectively.
Revised:  our result is the first sub-linear time algorithm having the sample size {\em independent of both the input number of points n and dimensionality d . Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, k-center clustering, and SVM with outliers, and therefore achieve the sub-linear time bi-criteria approximation algorithms for these problems respectively.
Edit intent: fluency
Original:  our result is the first sub-linear time algorithm having the sample size {\em   independent of both the input number of points n and dimensionality d  . Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, k-center clustering, and SVM with outliers, and therefore achieve  sub-linear time bi-criteria approximation algorithms for these problems respectively.
Revised:  our result is the first sub-linear time algorithm having the sample size {\em independent of both the input number of points n and dimensionality d . Furthermore, we observe that these two techniques can be generalized to deal with a broader range of geometric optimization problems with outliers in high dimensions, including flat fitting, k-center clustering, and SVM with outliers, and therefore achieve sub-linear time algorithms for these problems respectively.
Edit intent: meaning-changed
Original: This paper establishes a new decomposition of optimal dynamic portfolio choice under general incomplete-market diffusion models by disentangling the fundamental impacts on optimal policy from market incompleteness and flexible wealth-dependent utilities.
Revised: This paper provides a novel five-component decomposition of optimal dynamic portfolio choice under general incomplete-market diffusion models by disentangling the fundamental impacts on optimal policy from market incompleteness and flexible wealth-dependent utilities.
Edit intent: meaning-changed
Original:  We derive explicit dynamics of the components for the optimal policy, and obtain an equation system for solving the shadow price of market incompleteness, which is found to be dependent on both market state and wealth level. We identify a new important hedge component for non-myopic investors to hedge the uncertainty in shadow price due to variation in wealth level. As an application, we establish and compare the decompositions of optimal policy under general models with the prevalent HARA and CRRA utilities. Under nonrandom but possibly time-varying interest rate, we solve in closed-form the HARA policy as a combination of a bond holding scheme and a corresponding CRRA strategy.
Revised:  The decomposition leads to implementation via either closed-form solutions or Monte Carlo simulations. With a nonrandom but time-varying interest rate, we solve in closed-form the HARA policy as a combination of a bond holding scheme and a corresponding CRRA strategy.
Edit intent: clarity
Original:  Under nonrandom but possibly time-varying interest rate, we solve in closed-form the HARA policy as a combination of a bond holding scheme and a corresponding CRRA strategy.
Revised:  Under nonrandom but possibly time-varying interest rate, we can explicitly solve for the optimal policy under the wealth-dependent HARA utility. It is a combination of a bond holding scheme and a corresponding CRRA strategy.
Edit intent: meaning-changed
Original: In Coronavirus Disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm  ') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death  despite maximal supportive care.
Revised: In Coronavirus disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm ') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death despite maximal supportive care.
Edit intent: fluency
Original: In Coronavirus Disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm  ') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death  despite maximal supportive care.
Revised: In Coronavirus Disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm ') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death despite maximal supportive care.
Edit intent: fluency
Original: In Coronavirus Disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm  ') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death  despite maximal supportive care.
Revised: In Coronavirus Disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm syndrome ') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death despite maximal supportive care.
Edit intent: fluency
Original: In Coronavirus Disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm  ') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death  despite maximal supportive care.
Revised: In Coronavirus Disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm ') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death - despite maximal supportive care.
Edit intent: fluency
Original:  We have previously demonstrated that alpha-1 adrenergic receptor ( %DIFDELCMD < \alpha1%%%  -AR) antagonists can prevent cytokine storms and resulting death in mice.
Revised:  We have previously demonstrated that alpha-1 adrenergic receptor ( %DIFDELCMD < \alpha1%%% \alpha_1 -AR) antagonists can prevent cytokine storms and resulting death in mice.
Edit intent: meaning-changed
Original:  We have previously demonstrated that alpha-1 adrenergic receptor ( %DIFDELCMD < \alpha1%%%  -AR) antagonists can prevent cytokine storms and resulting death in mice.
Revised:  We have previously demonstrated that alpha-1 adrenergic receptor ( %DIFDELCMD < \alpha1%%% -AR) antagonists can prevent cytokine storm syndrome and resulting death in mice.
Edit intent: clarity
Original:  Patients  who were taking %DIFDELCMD < \alpha1%%%  -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users , both in ARDS (adjusted OR = 0.59 , 95\% CI 0.34-0.95 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Revised:  Patients with acute respiratory distress who were taking %DIFDELCMD < \alpha1%%% -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users , both in ARDS (adjusted OR = 0.59 , 95\% CI 0.34-0.95 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Edit intent: meaning-changed
Original:  Patients  who were taking %DIFDELCMD < \alpha1%%%  -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users , both in ARDS (adjusted OR = 0.59 , 95\% CI 0.34-0.95 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Revised:  Patients who were taking %DIFDELCMD < \alpha1%%% \alpha_1 -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users , both in ARDS (adjusted OR = 0.59 , 95\% CI 0.34-0.95 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Edit intent: meaning-changed
Original:  Patients  who were taking %DIFDELCMD < \alpha1%%%  -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users , both in ARDS (adjusted OR = 0.59 , 95\% CI 0.34-0.95 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Revised:  Patients who were taking %DIFDELCMD < \alpha1%%% -AR antagonists for other conditions had a 35\% reduced risk of requiring ventilation, and a 56\% reduced risk of ventilation and death, compared to non-users , both in ARDS (adjusted OR = 0.59 , 95\% CI 0.34-0.95 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Edit intent: meaning-changed
Original:  Patients  who were taking %DIFDELCMD < \alpha1%%%  -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users , both in ARDS (adjusted OR = 0.59 , 95\% CI 0.34-0.95 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Revised:  Patients who were taking %DIFDELCMD < \alpha1%%% -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users (adjusted OR = 0.59 , 95\% CI 0.34-0.95 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Edit intent: clarity
Original:  Patients  who were taking %DIFDELCMD < \alpha1%%%  -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users , both in ARDS (adjusted OR = 0.59 , 95\% CI 0.34-0.95 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Revised:  Patients who were taking %DIFDELCMD < \alpha1%%% -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users , both in ARDS (adjusted OR = 0.41 , 95\% CI 0.34-0.95 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Edit intent: meaning-changed
Original:  Patients  who were taking %DIFDELCMD < \alpha1%%%  -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users , both in ARDS (adjusted OR = 0.59 , 95\% CI 0.34-0.95 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Revised:  Patients who were taking %DIFDELCMD < \alpha1%%% -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users , both in ARDS (adjusted OR = 0.59 , 95\% CI 0.17-0.81 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Edit intent: meaning-changed
Original:  Patients  who were taking %DIFDELCMD < \alpha1%%%  -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users , both in ARDS (adjusted OR = 0.59 , 95\% CI 0.34-0.95 , p = 0.021)and in pneumonia (adjusted OR = 0.77, 95\% CI 0.62-0.94, p = 0.007) .
Revised:  Patients who were taking %DIFDELCMD < \alpha1%%% -AR antagonists for other conditions had lower probability of needing invasive mechanical ventilationand dying in the hospital compared to non-users , both in ARDS (adjusted OR = 0.59 , 95\% CI 0.34-0.95 , p = 0.01) .
Edit intent: meaning-changed
Original:  These results support the study of%DIFDELCMD < \alpha1%%%  -AR antagonists for  reducing mortality in pneumonia and ARDS and highlight the need for prospective trials of %DIFDELCMD < \alpha1%%%  -AR antagonists to assess their efficacy in preventing cytokine storm  and death in COVID-19.
Revised:  These results support %DIFDELCMD < \alpha1%%% -AR antagonists for reducing mortality in pneumonia and ARDS and highlight the need for prospective trials of %DIFDELCMD < \alpha1%%% -AR antagonists to assess their efficacy in preventing cytokine storm and death in COVID-19.
Edit intent: clarity
Original:  These results support the study of%DIFDELCMD < \alpha1%%%  -AR antagonists for  reducing mortality in pneumonia and ARDS and highlight the need for prospective trials of %DIFDELCMD < \alpha1%%%  -AR antagonists to assess their efficacy in preventing cytokine storm  and death in COVID-19.
Revised:  These results support the study of%DIFDELCMD < \alpha1%%% studying \alpha_1 -AR antagonists for reducing mortality in pneumonia and ARDS and highlight the need for prospective trials of %DIFDELCMD < \alpha1%%% -AR antagonists to assess their efficacy in preventing cytokine storm and death in COVID-19.
Edit intent: meaning-changed
Original:  These results support the study of%DIFDELCMD < \alpha1%%%  -AR antagonists for  reducing mortality in pneumonia and ARDS and highlight the need for prospective trials of %DIFDELCMD < \alpha1%%%  -AR antagonists to assess their efficacy in preventing cytokine storm  and death in COVID-19.
Revised:  These results support the study of%DIFDELCMD < \alpha1%%% -AR antagonists for preventing ARDS and reducing mortality in pneumonia and ARDS and highlight the need for prospective trials of %DIFDELCMD < \alpha1%%% -AR antagonists to assess their efficacy in preventing cytokine storm and death in COVID-19.
Edit intent: meaning-changed
Original:  These results support the study of%DIFDELCMD < \alpha1%%%  -AR antagonists for  reducing mortality in pneumonia and ARDS and highlight the need for prospective trials of %DIFDELCMD < \alpha1%%%  -AR antagonists to assess their efficacy in preventing cytokine storm  and death in COVID-19.
Revised:  These results support the study of%DIFDELCMD < \alpha1%%% -AR antagonists for reducing mortality in pneumonia and acute respiratory distress, as well as highlight the need for prospective trials of %DIFDELCMD < \alpha1%%% -AR antagonists to assess their efficacy in preventing cytokine storm and death in COVID-19.
Edit intent: meaning-changed
Original:  These results support the study of%DIFDELCMD < \alpha1%%%  -AR antagonists for  reducing mortality in pneumonia and ARDS and highlight the need for prospective trials of %DIFDELCMD < \alpha1%%%  -AR antagonists to assess their efficacy in preventing cytokine storm  and death in COVID-19.
Revised:  These results support the study of%DIFDELCMD < \alpha1%%% -AR antagonists for reducing mortality in pneumonia and ARDS and highlight the need for prospective trials of %DIFDELCMD < \alpha1%%% \alpha_1 -AR antagonists to assess their efficacy in preventing cytokine storm and death in COVID-19.
Edit intent: meaning-changed
Original:  These results support the study of%DIFDELCMD < \alpha1%%%  -AR antagonists for  reducing mortality in pneumonia and ARDS and highlight the need for prospective trials of %DIFDELCMD < \alpha1%%%  -AR antagonists to assess their efficacy in preventing cytokine storm  and death in COVID-19.
Revised:  These results support the study of%DIFDELCMD < \alpha1%%% -AR antagonists for reducing mortality in pneumonia and ARDS and highlight the need for prospective trials of %DIFDELCMD < \alpha1%%% -AR antagonists to assess their efficacy in preventing cytokine storm syndrome and death in COVID-19.
Edit intent: clarity
Original: In severe viral pneumonias , including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by a hyperinflammatory reaction ('cytokine storm syndrome') that leads to acute respiratory distress syndrome and death, despite maximal supportive care.
Revised: In severe viral pneumonia , including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by a hyperinflammatory reaction ('cytokine storm syndrome') that leads to acute respiratory distress syndrome and death, despite maximal supportive care.
Edit intent: fluency
Original: In severe viral pneumonias , including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by a hyperinflammatory reaction ('cytokine storm syndrome') that leads to acute respiratory distress syndrome and death, despite maximal supportive care.
Revised: In severe viral pneumonias , including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation ('cytokine storm syndrome') that leads to acute respiratory distress syndrome and death, despite maximal supportive care.
Edit intent: clarity
Original: In severe viral pneumonias , including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by a hyperinflammatory reaction ('cytokine storm syndrome') that leads to acute respiratory distress syndrome and death, despite maximal supportive care.
Revised: In severe viral pneumonias , including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by a hyperinflammatory reaction ('cytokine storm syndrome') , which can lead to acute respiratory distress syndrome and death, despite maximal supportive care.
Edit intent: coherence
Original: In severe viral pneumonias , including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by a hyperinflammatory reaction ('cytokine storm syndrome') that leads to acute respiratory distress syndrome and death, despite maximal supportive care. Preventing hyperinflammation is key to avoiding these outcomes.
Revised: In severe viral pneumonias , including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by a hyperinflammatory reaction ('cytokine storm syndrome') that leads to acute respiratory distress syndrome , URLan failure, and death.
Edit intent: clarity
Original:  Here, we conduct a retrospective analysis of patients with acute respiratory distress or pneumonia ( n= 13,125 and n= 108,956, respectively)from all causes;
Revised:  Here, we conduct a retrospective analysis of patients with acute respiratory distress or pneumonia ( n= 19,659) and three cohorts with pneumonia ( n= 108,956, respectively)from all causes;
Edit intent: meaning-changed
Original:  Here, we conduct a retrospective analysis of patients with acute respiratory distress or pneumonia ( n= 13,125 and n= 108,956, respectively)from all causes; patients who were incidentally taking \alpha-blockers had a reduced risk of requiring ventilation (by 35\% and 16\%, respectively), and a reduced risk of being ventilated and dying (by 56\% and 20\%, respectively), compared to non-users. Beta-adrenergic receptor antagonists had no significant effects .
Revised:  Here, we conduct a retrospective analysis of patients with acute respiratory distress or pneumonia ( n= 13,125 and n= 423,897). Federated across two ARD cohorts, our main result shows that patients using \alpha_1-AR antagonists, as compared to nonusers, had a 40\% relative risk reduction for ventilation and dying (p=0.014) .
Edit intent: meaning-changed