wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Each MD trajectory was divided into two parts - one to pass from an unfolded protein state to a native-like state by overcoming a free energy barrier that separates these states, and the other to explore the native-like states until the native state is attained .
Revised:  Each MD trajectory was divided into two parts - one to pass from an unfolded protein state to a native-like state by overcoming a free energy barrier that separates these states, and the other to explore the native-like states affects the folding kinetics .
Edit intent: meaning-changed
Original:  It has been found that the distributions of first-passage times (FPTs) for both segments of the trajectories are essentially single-exponential  .
Revised:  It has been found that the distributions of first-passage times (FPTs) for both segments of the trajectories are essentially single-exponential not only for the times to overcome the free energy barrier that separates unfolded and native-like states but also for the times to find the native state among the native-like ones .
Edit intent: meaning-changed
Original:  If the protein explores the native-like states for a time much longer than the time to overcome the free energy barrier,  the resulting FPT distribution may be approximately single-exponential, as is expected for two-state folders, but the mean FPT (MFPT) to reach the native state will be determined by the time to find the native state among the native-like ones.
Revised:  If the protein explores native-like states for a time much longer than the time to overcome the free energy barrier, the resulting FPT distribution may be approximately single-exponential, as is expected for two-state folders, but the mean FPT (MFPT) to reach the native state will be determined by the time to find the native state among the native-like ones.
Edit intent: fluency
Original:  If the protein explores the native-like states for a time much longer than the time to overcome the free energy barrier,  the resulting FPT distribution may be approximately single-exponential, as is expected for two-state folders, but the mean FPT (MFPT) to reach the native state will be determined by the time to find the native state among the native-like ones.
Revised:  If the protein explores the native-like states for a time much longer than the time to overcome the free energy barrier, which was found to be characteristic of high temperatures, the resulting FPT distribution may be approximately single-exponential, as is expected for two-state folders, but the mean FPT (MFPT) to reach the native state will be determined by the time to find the native state among the native-like ones.
Edit intent: meaning-changed
Original:  If the protein explores the native-like states for a time much longer than the time to overcome the free energy barrier,  the resulting FPT distribution may be approximately single-exponential, as is expected for two-state folders, but the mean FPT (MFPT) to reach the native state will be determined by the time to find the native state among the native-like ones.
Revised:  If the protein explores the native-like states for a time much longer than the time to overcome the free energy barrier, the resulting FPT distribution to reach the native state remains close to exponential but the mean FPT (MFPT) to reach the native state will be determined by the time to find the native state among the native-like ones.
Edit intent: clarity
Original:  If the protein explores the native-like states for a time much longer than the time to overcome the free energy barrier,  the resulting FPT distribution may be approximately single-exponential, as is expected for two-state folders, but the mean FPT (MFPT) to reach the native state will be determined by the time to find the native state among the native-like ones.
Revised:  If the protein explores the native-like states for a time much longer than the time to overcome the free energy barrier, the resulting FPT distribution may be approximately single-exponential, as is expected for two-state folders, but the mean FPT (MFPT) is determined not by the height of the free energy barrier but by the time to explore native-like ones.
Edit intent: clarity
Original:  If the protein explores the native-like states for a time much longer than the time to overcome the free energy barrier,  the resulting FPT distribution may be approximately single-exponential, as is expected for two-state folders, but the mean FPT (MFPT) to reach the native state will be determined by the time to find the native state among the native-like ones. The Kramers rate formula to estimate the transition times from the unfolded to the native-like states shows that these times are in reasonable agreement with the corresponding times obtained in the simulations and may be far shorter than the MFPTs to reach the native state  .
Revised:  If the protein explores the native-like states for a time much longer than the time to overcome the free energy barrier, the resulting FPT distribution may be approximately single-exponential, as is expected for two-state folders, but the mean FPT (MFPT) to reach the native state will be determined by the time to find the native state among the native-like states. The mean time to overcome the free energy barrier is found to be in reasonable agreement with the corresponding times obtained in the simulations and may be far shorter than the MFPTs to reach the native state .
Edit intent: clarity
Original:  The Kramers rate formula to estimate the transition times from the unfolded to the native-like states shows that these times are in reasonable agreement with the corresponding times obtained in the simulations and may be far shorter than the MFPTs to reach the native state  .
Revised:  The Kramers rate formula to estimate the transition times from the unfolded to the native-like states shows that these times are in reasonable agreement with the Kramers rate formula and generally far shorter than the MFPTs to reach the native state .
Edit intent: clarity
Original:  The Kramers rate formula to estimate the transition times from the unfolded to the native-like states shows that these times are in reasonable agreement with the corresponding times obtained in the simulations and may be far shorter than the MFPTs to reach the native state  .
Revised:  The Kramers rate formula to estimate the transition times from the unfolded to the native-like states shows that these times are in reasonable agreement with the corresponding times obtained in the simulations and may be far shorter than the MFPT to reach the native state .
Edit intent: fluency
Original:  The Kramers rate formula to estimate the transition times from the unfolded to the native-like states shows that these times are in reasonable agreement with the corresponding times obtained in the simulations and may be far shorter than the MFPTs to reach the native state  .
Revised:  The Kramers rate formula to estimate the transition times from the unfolded to the native-like states shows that these times are in reasonable agreement with the corresponding times obtained in the simulations and may be far shorter than the MFPTs to reach the native state . The time to find the native state among native-like ones increases with temperature, which explains the known U-shape dependence of the MFPTs on temperature .
Edit intent: meaning-changed
Original:  By varying the starting date of the policy intervention in the simulated scenarios, we find that the most effective policy intervention occurs around the time when the number of active infections is growing at its highest rate  .
Revised:  By varying the starting date of the policy intervention in the simulated scenarios, we find that the most effective policy intervention occurs around the time when the number of active infections is growing at its highest rate -- that is, the results suggest that the most dramatic measures should only be implemented when the disease is sufficiently spread .
Edit intent: meaning-changed
Original:  By varying the starting date of the policy intervention in the simulated scenarios, we find that the most effective policy intervention occurs around the time when the number of active infections is growing at its highest rate -- that is, the results suggest that the most dramatic measures should only be implemented when the disease is sufficiently spread.
Revised:  By varying the starting date of the policy intervention in the simulated scenarios, we find that the most effective policy intervention occurs around the time when the number of active infections is growing at its highest rate -- that is, the results suggest that the most severe measures should only be implemented when the disease is sufficiently spread.
Edit intent: clarity
Original:  The degree of the intervention, above a certain threshold, does not appear to have a great impact on the outcomes in our simulations, due to the strongly concave relationship we assume between production shortfall and reduction in the infection rate .
Revised:  The intensity of the intervention, above a certain threshold, does not appear to have a great impact on the outcomes in our simulations, due to the strongly concave relationship we assume between production shortfall and reduction in the infection rate .
Edit intent: clarity
Original:  The degree of the intervention, above a certain threshold, does not appear to have a great impact on the outcomes in our simulations, due to the strongly concave relationship we assume between production shortfall and reduction in the infection rate .
Revised:  The degree of the intervention, above a certain threshold, does not appear to have a great impact on the outcomes in our simulations, due to the strongly concave relationship that we identify between production shortfall and reduction in the infection rate .
Edit intent: clarity
Original:  The degree of the intervention, above a certain threshold, does not appear to have a great impact on the outcomes in our simulations, due to the strongly concave relationship we assume between production shortfall and reduction in the infection rate .
Revised:  The degree of the intervention, above a certain threshold, does not appear to have a great impact on the outcomes in our simulations, due to the strongly concave relationship we assume between production shortfall and infection rate reductions .
Edit intent: clarity
Original:  The model and its implementation, along with the general insights from our policy experiments, may help policymakers design effective emergency policy responses in the face  a serious pandemic, and contribute to our understanding of the relationship between the economic growth and the spread of infectious diseases.
Revised:  The model and its implementation, along with the general insights from our policy experiments, may help policymakers design effective emergency policy responses in the face of a serious pandemic, and contribute to our understanding of the relationship between the economic growth and the spread of infectious diseases.
Edit intent: fluency
Original:  Under the specific condition on model parameters , we show that the value function is a C^2 solution of the HJB equation and derive the optimal investment strategy in terms of second-order ordinary differential equations.
Revised:  We derive the optimal investment strategy under the specific condition on model parameters , we show that the value function is a C^2 solution of the HJB equation and derive the optimal investment strategy in terms of second-order ordinary differential equations.
Edit intent: fluency
Original: One of the most crucial challenges in questionanswering (QA) is the scarcity of labeled data, since it is costly to obtain question-answer(QA) pairs for a target text domain with human annotation.
Revised: One of the most crucial challenges in question answering (QA) is the scarcity of labeled data, since it is costly to obtain question-answer(QA) pairs for a target text domain with human annotation.
Edit intent: fluency
Original:  In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizingthe mutual information between generated QApairs to ensure their consistency.
Revised:  In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizing the mutual information between generated QApairs to ensure their consistency.
Edit intent: fluency
Original:  In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizingthe mutual information between generated QApairs to ensure their consistency.
Revised:  In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizingthe mutual information between generated QA pairs to ensure their consistency.
Edit intent: fluency
Original:  We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models.
Revised:  We validate our Information Maximizing Hierarchical Conditional Variational AutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models.
Edit intent: fluency
Original:  We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models.
Revised:  We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets by evaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models.
Edit intent: fluency
Original:  We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models.
Revised:  We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QA pairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models.
Edit intent: fluency
Original:  We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models.
Revised:  We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using both the generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models.
Edit intent: fluency
Original:  Our results indicate that large cities are proportionally more affected by COVID-19  , such that every 1\% rise in population is associated with 0.57\% increase in the number of cases per capita and 0.25\% in the number of deaths per capita .
Revised:  Our results indicate small towns are proportionally more affected by COVID-19 , such that every 1\% rise in population is associated with 0.57\% increase in the number of cases per capita and 0.25\% in the number of deaths per capita .
Edit intent: meaning-changed
Original:  Our results indicate that large cities are proportionally more affected by COVID-19  , such that every 1\% rise in population is associated with 0.57\% increase in the number of cases per capita and 0.25\% in the number of deaths per capita .
Revised:  Our results indicate that large cities are proportionally more affected by COVID-19 during the initial spread of the disease , such that every 1\% rise in population is associated with 0.57\% increase in the number of cases per capita and 0.25\% in the number of deaths per capita .
Edit intent: meaning-changed
Original:  Our results indicate that large cities are proportionally more affected by COVID-19  , such that every 1\% rise in population is associated with 0.57\% increase in the number of cases per capita and 0.25\% in the number of deaths per capita .
Revised:  Our results indicate that large cities are proportionally more affected by COVID-19 , such that the cumulative numbers of cases and deaths per capita .
Edit intent: clarity
Original:  Our results indicate that large cities are proportionally more affected by COVID-19  , such that every 1\% rise in population is associated with 0.57\% increase in the number of cases per capita and 0.25\% in the number of deaths per capita . The difference between the scaling of cases and deaths indicates the case fatality rate decreases with city size.
Revised:  Our results indicate that large cities are proportionally more affected by COVID-19 , such that every 1\% rise in population is associated with 0.57\% increase in the number of cases per capita and 0.25\% in the number of deaths per capita initially decrease with population size. However, during the long-term course of the pandemic, this urban advantage vanishes and large cities start to exhibit higher incidence of cases and deaths indicates the case fatality rate decreases with city size.
Edit intent: meaning-changed
Original:  The difference between the scaling of cases and deaths indicates the case fatality rate decreases with city size. The latest estimates show that a 1\% increase in population associates with a 0.14\% reduction in the case fatality rate of COVID-19;
Revised:  The difference between the scaling of cases and deaths , such that every 1\% increase in population associates with a 0.14\% reduction in the case fatality rate of COVID-19;
Edit intent: clarity
Original:  The latest estimates show that a 1\% increase in population associates with a 0.14\% reduction in the case fatality rate of COVID-19; however, this urban advantage has decreased over time. We interpret this to be due to the existence of proportionally more health infrastructure in the largest cities and a lower proportion of older adults in large urban areas.
Revised:  The latest estimates show that a 1\% increase in population associates with a 0.14\% increase in the number of fatalities per capita after about four months since the first two daily deaths. We argue that these patterns may be related to the existence of proportionally more health infrastructure in the largest cities and a lower proportion of older adults in large urban areas.
Edit intent: meaning-changed
Original: We perform a numerical analysis of randomly perturbed Poisson systems } } . For the considered It\^o perturbation of Poisson differential equations , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Revised: We perform a numerical analysis of a class of randomly perturbed } } . For the considered It\^o perturbation of Poisson differential equations , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Edit intent: clarity
Original: We perform a numerical analysis of randomly perturbed Poisson systems } } . For the considered It\^o perturbation of Poisson differential equations , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Revised: We perform a numerical analysis of randomly perturbed Poisson systems H} } . For the considered It\^o perturbation of Poisson differential equations , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Edit intent: meaning-changed
Original: We perform a numerical analysis of randomly perturbed Poisson systems } } . For the considered It\^o perturbation of Poisson differential equations , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Revised: We perform a numerical analysis of randomly perturbed Poisson systems }amiltonian systems and } . For the considered It\^o perturbation of Poisson differential equations , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Edit intent: meaning-changed
Original: We perform a numerical analysis of randomly perturbed Poisson systems } } . For the considered It\^o perturbation of Poisson differential equations , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Revised: We perform a numerical analysis of randomly perturbed Poisson systems } P} . For the considered It\^o perturbation of Poisson differential equations , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Edit intent: meaning-changed
Original: We perform a numerical analysis of randomly perturbed Poisson systems } } . For the considered It\^o perturbation of Poisson differential equations , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Revised: We perform a numerical analysis of randomly perturbed Poisson systems } }oisson systems . For the considered It\^o perturbation of Poisson differential equations , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Edit intent: meaning-changed
Original: We perform a numerical analysis of randomly perturbed Poisson systems } } . For the considered It\^o perturbation of Poisson differential equations , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Revised: We perform a numerical analysis of randomly perturbed Poisson systems } } . For the considered additive noise perturbation of such systems , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Edit intent: meaning-changed
Original: We perform a numerical analysis of randomly perturbed Poisson systems } } . For the considered It\^o perturbation of Poisson differential equations , we show the longtime behavior of the energy and quadratic Casimirs for the exact solution.
Revised: We perform a numerical analysis of randomly perturbed Poisson systems } } . For the considered It\^o perturbation of Poisson differential equations , we show the long time behavior of the energy and quadratic Casimirs for the exact solution.
Edit intent: meaning-changed
Original:  By evolving compact symbolic expressions we ensure the discovered plasticity rules are amenable to intuitive understanding . This is fundamental for successful communication and human-guided generalization , for example to different network architectures or task domains.
Revised:  By evolving compact symbolic expressions we ensure the discovered plasticity rules are amenable to intuitive understanding , fundamental for successful communication and human-guided generalization , for example to different network architectures or task domains.
Edit intent: coherence