wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Triggering bacterial reductive division by abruptly switching to non-nutritious medium, we find that the cell size distribution changes in a specific manner that keeps its normalized form unchanged; in other words, scale invarianceholds. We also find that the normalized distribution depends slightly on the growth condition before starvation.
Revised:  Triggering bacterial reductive division by abruptly switching to non-nutritious medium, we find that the cell size distribution changes but satisfies scale invariance: the rescaled distribution is kept unchanged and determined by the growth condition before starvation.
Edit intent: clarity
Original:  We also formulate the problem theoretically and propose a sufficient condition for the scale invariance. Finally , we numerically show the range of validity of the scale invariance over various characteristic times of the starvation process, revealing the number of multifork replications is crucial for the scale invariance  .
Revised:  Further , we numerically show the range of validity of the scale invariance over various characteristic times of the starvation process, revealing the number of multifork replications is crucial for the scale invariance .
Edit intent: coherence
Original:  Finally , we numerically show the range of validity of the scale invariance over various characteristic times of the starvation process, revealing the number of multifork replications is crucial for the scale invariance  .
Revised:  Finally , we numerically show the range of validity of the scale invariance over various characteristic times of the starvation process, revealing the number of multifork replications is crucial for the scale invariance for slow starvation .
Edit intent: meaning-changed
Original:  We apply our approach to the analysis of an observational study, where the focus is on assessing direct and spillover causal effects of a new light rail line recently built in Florence (Italy) on the commercial vitality of the street where it was built and of the streets in the treated street's neighborhood.
Revised:  We apply our approach to the analysis of an observational study, where the focus is on assessing direct and spillover causal effects of a new light rail line recently built in Florence (Italy) on the retail density of the street where it was built and of the streets in the treated street's neighborhood.
Edit intent: clarity
Original:  We first show that the pairs bootstrap that samples cross-sectional units with replacement does not approximate well the limiting distribution of the penalized estimator. We then propose a wild residual bootstrap procedure and show that it is asymptotically valid for approximating the distribution of the penalized estimator.
Revised:  We propose a wild residual bootstrap procedure and show that it is asymptotically valid for approximating the distribution of the penalized estimator.
Edit intent: clarity
Original:  Phys. Rev. Fluids, 2 (2017), 113601] to a fast-slow system, yielding an analytically and numerically tractable equation that is equivalent to the original model in the overdamped limit.
Revised:  Phys. Rev. Fluids, 2 (2017), 113601] to a fast-slow system, yielding an analytically and numerically tractable equation equivalent to the original model in the overdamped limit.
Edit intent: clarity
Original:  We show by direct calculations that for sufficiently close vesicle-to-spine diameter ratios, there can only be unidirectional motion . The critical vesicle-to-spine diameter ratio predicted by our analysis , at which there is a transition from unidirectional to bidirectional motion, is consistent with experimental observations of vesicle trajectories in the literature.
Revised:  We show by direct calculations that for sufficiently close vesicle-to-spine diameter ratios, there can only be unidirectional motion for sufficiently close vesicle-to-spine diameter ratios. Our analysis predicts the critical vesicle-to-spine diameter ratio predicted by our analysis , at which there is a transition from unidirectional to bidirectional motion, is consistent with experimental observations of vesicle trajectories in the literature.
Edit intent: clarity
Original:  The critical vesicle-to-spine diameter ratio predicted by our analysis , at which there is a transition from unidirectional to bidirectional motion, is consistent with experimental observations of vesicle trajectories in the literature.
Revised:  The critical vesicle-to-spine diameter ratio , at which there is a transition from unidirectional to bidirectional motion, is consistent with experimental observations of vesicle trajectories in the literature.
Edit intent: clarity
Original:  The critical vesicle-to-spine diameter ratio predicted by our analysis , at which there is a transition from unidirectional to bidirectional motion, is consistent with experimental observations of vesicle trajectories in the literature.
Revised:  The critical vesicle-to-spine diameter ratio predicted by our analysis , at which there is a transition from unidirectional to bidirectional motion, consistent with experimental observations of vesicle trajectories in the literature.
Edit intent: coherence
Original: The current COVID-19 pandemic is leading experts to assess the risks posed by the disease and compare policies geared towards stalling its evolution as a global pandemic. In this setting, the virus' basic reproductive number R _0, which characterizes the average number of secondary cases generated by each primary case, takes on a significant importance in the quantification of the potential scope of the pandemic .
Revised: The correct evaluation of the reproductive number R _0, which characterizes the average number of secondary cases generated by each primary case, takes on a significant importance in the quantification of the potential scope of the pandemic .
Edit intent: clarity
Original:  In this setting, the virus' basic reproductive number R _0, which characterizes the average number of secondary cases generated by each primary case, takes on a significant importance in the quantification of the potential scope of the pandemic .
Revised:  In this setting, the virus' basic reproductive number R for COVID-19 -- which characterizes the average number of secondary cases generated by each primary case, takes on a significant importance in the quantification of the potential scope of the pandemic .
Edit intent: meaning-changed
Original:  In this setting, the virus' basic reproductive number R _0, which characterizes the average number of secondary cases generated by each primary case, takes on a significant importance in the quantification of the potential scope of the pandemic .
Revised:  In this setting, the virus' basic reproductive number R _0, which characterizes the average number of secondary cases generated by each typical primary case -- is central in the quantification of the potential scope of the pandemic .
Edit intent: clarity
Original:  In this setting, the virus' basic reproductive number R _0, which characterizes the average number of secondary cases generated by each primary case, takes on a significant importance in the quantification of the potential scope of the pandemic . Yet, in most models, R _0 is assumed to be a universal constant for the virus across outbreak clusters and populations -- thus neglecting the inherent variability of the transmission process due to varying population densities, demographics, temporal factors, etc.
Revised:  In this setting, the virus' basic reproductive number R _0, which characterizes the average number of secondary cases generated by each primary case, takes on a significant importance in the quantification of the potential scope of the pandemic and the selection of an appropriate course of action. In most models, R _0 is assumed to be a universal constant for the virus across outbreak clusters and populations -- thus neglecting the inherent variability of the transmission process due to varying population densities, demographics, temporal factors, etc.
Edit intent: clarity
Original:  Yet, in most models, R _0 is assumed to be a universal constant for the virus across outbreak clusters and populations -- thus neglecting the inherent variability of the transmission process due to varying population densities, demographics, temporal factors, etc.
Revised:  Yet, in most models, R is modeled as a ``universal" constant for the virus across outbreak clusters and populations -- thus neglecting the inherent variability of the transmission process due to varying population densities, demographics, temporal factors, etc.
Edit intent: clarity
Original:  Yet, in most models, R _0 is assumed to be a universal constant for the virus across outbreak clusters and populations -- thus neglecting the inherent variability of the transmission process due to varying population densities, demographics, temporal factors, etc.
Revised:  Yet, in most models, R _0 is assumed to be a universal constant for the virus across outbreak clusters and individuals -- thus neglecting the inherent variability of the transmission process due to varying population densities, demographics, temporal factors, etc.
Edit intent: clarity
Original:  Yet, in most models, R _0 is assumed to be a universal constant for the virus across outbreak clusters and populations -- thus neglecting the inherent variability of the transmission process due to varying population densities, demographics, temporal factors, etc.
Revised:  Yet, in most models, R _0 is assumed to be a universal constant for the virus across outbreak clusters and populations -- effectively averaging the effect of the inherent variability of the transmission process due to varying population densities, demographics, temporal factors, etc.
Edit intent: meaning-changed
Original:  In fact, it can be shown that the reproduction number is highly variable. Considering its expected value thus leads to biased or loose results in the reported predictive scenarios, especially as these are tailored to a given country or region. The goal of this paper is the examination of the impact of the reproductive number R's variability on important output metrics, and the percolation of this variability in projected scenarios so as to provide uncertainty quantification.
Revised:  Yet, due to the exponential nature of epidemics growth, this simplification can lead to inaccurate predictions and/or risk evaluation, thus begging the question: how large is the impact of this simplification on predictions and risk estimates? How can this variability be percolated in the projections for the pandemics so as to provide uncertainty quantification.
Edit intent: clarity
Original:  The goal of this paper is the examination of the impact of the reproductive number R's variability on important output metrics, and the percolation of this variability in projected scenarios so as to provide uncertainty quantification.
Revised:  The goal of this paper is the examination of the impact of the reproductive number R's variability on important output metrics, and the percolation of this variability in projected scenarios so as to provide a more accurate uncertainty quantification?
Edit intent: clarity
Original:  In this perspective, instead of considering a single R_0, we consider a distribution of reproductive numbers R and devise a simple Bayesian hierarchical model that builds upon current methods for estimating the R to integrate its heterogeneity.
Revised:  In this perspective, instead of considering a single , fixed R, we model the reproductive number as a distribution sampled from a simple Bayesian hierarchical model that builds upon current methods for estimating the R to integrate its heterogeneity.
Edit intent: clarity
Original:  In this perspective, instead of considering a single R_0, we consider a distribution of reproductive numbers R and devise a simple Bayesian hierarchical model that builds upon current methods for estimating the R to integrate its heterogeneity. We then simulate the spread of the epidemic , and the impact of different social distancing strategies using a probabilistic framework that models hospital occupancy.
Revised:  In this perspective, instead of considering a single R_0, we consider a distribution of reproductive numbers R and devise a simple Bayesian hierarchical model . Using our fitted model, we then simulate the spread of the epidemic , and the impact of different social distancing strategies using a probabilistic framework that models hospital occupancy.
Edit intent: clarity
Original:  We emphasize that our goal is not to replace benchmark methods for estimating the basic reproductive numbers, but rather to discuss the importance of the impact of R's heterogeneity on uncertainty quantification for the current COVID-19 pandemic.
Revised:  We emphasize that our goal is not to replace benchmark methods for estimating the reproductive numbers, but rather to discuss the importance of the impact of R's heterogeneity on uncertainty quantification for the current COVID-19 pandemic.
Edit intent: clarity
Original:  We emphasize that our goal is not to replace benchmark methods for estimating the basic reproductive numbers, but rather to discuss the importance of the impact of R's heterogeneity on uncertainty quantification for the current COVID-19 pandemic.
Revised:  We emphasize that our goal is not to replace benchmark methods for estimating the basic reproductive numbers, nor to pretend to more accurate predictive scenarios. Rather, we focus solely on discussing the importance of the impact of R's heterogeneity on uncertainty quantification for the current COVID-19 pandemic.
Edit intent: meaning-changed
Original:  In most models, R is modeled as a ``universal " constant for the virus across outbreak clusters and individuals -- effectively averaging the effect of the inherent variability of the transmission process due to varying  population densities, demographics, temporal factors, etc .
Revised:  In most models, R is modeled as a universal constant for the virus across outbreak clusters and individuals -- effectively averaging the effect of the inherent variability of the transmission process due to varying population densities, demographics, temporal factors, etc .
Edit intent: fluency
Original:  In most models, R is modeled as a ``universal " constant for the virus across outbreak clusters and individuals -- effectively averaging the effect of the inherent variability of the transmission process due to varying  population densities, demographics, temporal factors, etc .
Revised:  In most models, R is modeled as a ``universal " constant for the virus across outbreak clusters and individuals -- effectively averaging out the inherent variability of the transmission process due to varying population densities, demographics, temporal factors, etc .
Edit intent: clarity
Original:  In most models, R is modeled as a ``universal " constant for the virus across outbreak clusters and individuals -- effectively averaging the effect of the inherent variability of the transmission process due to varying  population densities, demographics, temporal factors, etc .
Revised:  In most models, R is modeled as a ``universal " constant for the virus across outbreak clusters and individuals -- effectively averaging the effect of the inherent variability of the transmission process due to varying individual contact rates, population densities, demographics, temporal factors, etc .
Edit intent: meaning-changed
Original:  In most models, R is modeled as a ``universal " constant for the virus across outbreak clusters and individuals -- effectively averaging the effect of the inherent variability of the transmission process due to varying  population densities, demographics, temporal factors, etc .
Revised:  In most models, R is modeled as a ``universal " constant for the virus across outbreak clusters and individuals -- effectively averaging the effect of the inherent variability of the transmission process due to varying population densities, demographics, or temporal factors amongst many .
Edit intent: clarity
Original:  Yet, due to the exponential nature of epidemics growth, this simplification can  lead to inaccurate predictions and/or risk evaluation , thus begging the question: how large is the  impact of this simplification on predictions and risk estimates?
Revised:  Yet, due to the exponential nature of epidemic growth, the error due to this simplification can lead to inaccurate predictions and/or risk evaluation , thus begging the question: how large is the impact of this simplification on predictions and risk estimates?
Edit intent: clarity
Original:  Yet, due to the exponential nature of epidemics growth, this simplification can  lead to inaccurate predictions and/or risk evaluation , thus begging the question: how large is the  impact of this simplification on predictions and risk estimates?
Revised:  Yet, due to the exponential nature of epidemics growth, this simplification can be rapidly amplified and lead to inaccurate predictions and/or risk evaluation , thus begging the question: how large is the impact of this simplification on predictions and risk estimates?
Edit intent: meaning-changed
Original:  Yet, due to the exponential nature of epidemics growth, this simplification can  lead to inaccurate predictions and/or risk evaluation , thus begging the question: how large is the  impact of this simplification on predictions and risk estimates?
Revised:  Yet, due to the exponential nature of epidemics growth, this simplification can lead to inaccurate predictions and/or risk evaluation . From the statistical modeling perspective, the impact of this simplification on predictions and risk estimates?
Edit intent: meaning-changed
Original:  Yet, due to the exponential nature of epidemics growth, this simplification can  lead to inaccurate predictions and/or risk evaluation , thus begging the question: how large is the  impact of this simplification on predictions and risk estimates?
Revised:  Yet, due to the exponential nature of epidemics growth, this simplification can lead to inaccurate predictions and/or risk evaluation , thus begging the question: how large is the magnitude of the impact of this simplification on predictions and risk estimates?
Edit intent: clarity
Original:  Yet, due to the exponential nature of epidemics growth, this simplification can  lead to inaccurate predictions and/or risk evaluation , thus begging the question: how large is the  impact of this simplification on predictions and risk estimates? How can this variability be percolated in the projections for the pandemics so as to provide a more accurate uncertainty quantification ?
Revised:  Yet, due to the exponential nature of epidemics growth, this simplification can lead to inaccurate predictions and/or risk evaluation , thus begging the question: how large is the impact of this averaging remains an open question: how can this intrinsic variability be percolated in the projections for the pandemics so as to provide a more accurate uncertainty quantification ?
Edit intent: clarity
Original:  In this perspective, instead of considering a single, fixed R, we model the reproductive number as a distribution sampled from a simple Bayesian hierarchical model . Using our fitted model, we then simulate the spread of the epidemic as well as the impact of different social distancing strategies, and highlight the strong impact of this added variability on the reported results.
Revised:  In this paper, we propose to study this question through a Bayesian perspective, creating a bridge between the agent-based and compartmental approaches commonly used in the literature. After deriving a Bayesian model that captures at scale the heterogeneity of a population and environmental conditions, we simulate the spread of the epidemic as well as the impact of different social distancing strategies, and highlight the strong impact of this added variability on the reported results.
Edit intent: meaning-changed
Original:  We emphasize that our goal is not to replace benchmark methods for estimating the reproductive numbers, nor to pretend to more accurate predictive scenarios. Rather, we focus solely on discussing the importance of the impact of R's heterogeneity on uncertainty quantification for the current COVID-19 pandemic .
Revised:  We base our discussion on both synthetic experiments -- thereby quantifying of the reliability and the magnitude of the effects -- and real COVID-19 pandemic .
Edit intent: clarity
Original:  Based on this SLP, we propose a self-index data structure of O(g) words of space that can be built from a string T in O(n + g \lg g) time, retrieving the starting positions of all occurrences of a pattern P of length m in O(m + \lg m \lg n + occ \lg g) time, where n is the length of T, g is the size of the Lyndon SLP for T, and occ is the number of occurrences of P in T.
Revised:  Based on this SLP, we propose a self-index data structure of O(g) words of space that can be built from a string T in O(n \lg n) expected time, retrieving the starting positions of all occurrences of a pattern P of length m in O(m + \lg m \lg n + occ \lg g) time, where n is the length of T, g is the size of the Lyndon SLP for T, and occ is the number of occurrences of P in T.
Edit intent: clarity
Original:  Indeed, the cerebral cortex has the ability to URLanize itself through two distinct forms of plasticity: the structural plasticity that creates (sprouting) or cuts (pruning) synaptic connections between neurons, and the synaptic plasticity that modifies the synaptic connections strength. These mechanisms are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. ...
Revised:  Indeed, the cerebral cortex URLanizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. ...
Edit intent: clarity
Original:  To model such a behavior, Edelman and Damasio proposed respectively the Reentry and the Convergence Divergence Zone frameworks where bi-directional neural communications can lead to both multimodal fusion (convergence) and inter-modal activation (divergence). ...
Revised:  In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence) and inter-modal activation (divergence). ...
Edit intent: coherence
Original:  To model such a behavior, Edelman and Damasio proposed respectively the Reentry and the Convergence Divergence Zone frameworks where bi-directional neural communications can lead to both multimodal fusion (convergence) and inter-modal activation (divergence). ...
Revised:  To model such a behavior, Edelman and Damasio proposed respectively the Reentry and the Convergence Divergence Zone frameworks where bi-directional neural communications can lead to both multimodal fusion (convergence) . Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). ...
Edit intent: meaning-changed
Original:  To model such a behavior, Edelman and Damasio proposed respectively the Reentry and the Convergence Divergence Zone frameworks where bi-directional neural communications can lead to both multimodal fusion (convergence) and inter-modal activation (divergence). ...
Revised:  To model such a behavior, Edelman and Damasio proposed respectively the Reentry and the Convergence Divergence Zone frameworks where bi-directional neural communications can lead to both multimodal fusion (convergence) and inter-modal activation (divergence). 
Edit intent: fluency