wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We continue by investigating MAX-IS and provide a series of deterministic dynamic approximation schemes . For uniform rectangles, we first give an algorithm that maintains a 4-approximate MAX-IS with O(1) update time. In a subsequent algorithm, we establish the trade-off between approximation quality 2 (1+\frac{1{k}) and update time O(k^2\log n) for k\in \mathbb{N}.
Revised:  We continue by investigating MAX-IS and provide a series of deterministic dynamic approximation schemes with approximation factors between 2 (1+\frac{1{k}) and update time O(k^2\log n) for k\in \mathbb{N}.
Edit intent: clarity
Original:  In a subsequent algorithm, we establish the trade-off between approximation quality 2 (1+\frac{1{k}) and update time O(k^2\log n) for k\in \mathbb{N}.
Revised:  In a subsequent algorithm, we establish the trade-off between approximation quality 2 {k}) and update time O(k^2\log n) for k\in \mathbb{N}.
Edit intent: meaning-changed
Original:  We conclude with an algorithm that maintains a 2-approximate MAX-IS for dynamic sets of uniform height and arbitrary width rectangles with O(\omega \log n) update time, where \omega is the largest number of maximal cliques stabbed by any axis-parallel line}  .
Revised:  We conclude with an algorithm that maintains a 2-approximate MAX-IS for dynamic sets of uniform height and arbitrary width rectangles with O(\omega \log n) update time, where \omega is the largest number of maximal cliques stabbed by any axis-parallel line} and 4 and corresponding running-time trade-offs .
Edit intent: meaning-changed
Original: Common reporting styles of statistical results, such as  confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects , while disregarding the magnitudes and absolute difference in the effect sizes.
Revised: Common reporting styles for statistical results, such as confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects , while disregarding the magnitudes and absolute difference in the effect sizes.
Edit intent: fluency
Original: Common reporting styles of statistical results, such as  confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects , while disregarding the magnitudes and absolute difference in the effect sizes.
Revised: Common reporting styles of statistical results, such as p-values and confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects , while disregarding the magnitudes and absolute difference in the effect sizes.
Edit intent: meaning-changed
Original: Common reporting styles of statistical results, such as  confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects , while disregarding the magnitudes and absolute difference in the effect sizes.
Revised: Common reporting styles of statistical results, such as confidence intervals (CI), have been reported to be prone to dichotomous interpretations especially on null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects , while disregarding the magnitudes and absolute difference in the effect sizes.
Edit intent: clarity
Original: Common reporting styles of statistical results, such as  confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects , while disregarding the magnitudes and absolute difference in the effect sizes.
Revised: Common reporting styles of statistical results, such as confidence intervals (CI), are prone to dichotomous interpretations , especially with respect to null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects , while disregarding the magnitudes and absolute difference in the effect sizes.
Edit intent: clarity
Original: Common reporting styles of statistical results, such as  confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects , while disregarding the magnitudes and absolute difference in the effect sizes.
Revised: Common reporting styles of statistical results, such as confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks . For example, when the p-value is small enough or the CIs of the mean effects , while disregarding the magnitudes and absolute difference in the effect sizes.
Edit intent: meaning-changed
Original: Common reporting styles of statistical results, such as  confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects , while disregarding the magnitudes and absolute difference in the effect sizes.
Revised: Common reporting styles of statistical results, such as confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute difference in the effect sizes.
Edit intent: meaning-changed
Original: Common reporting styles of statistical results, such as  confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects , while disregarding the magnitudes and absolute difference in the effect sizes.
Revised: Common reporting styles of statistical results, such as confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks , for exampleby claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects , while disregarding the magnitudes and absolute differences in the effect sizes.
Edit intent: fluency
Original:  We ran two experiments to compare several representation alternatives of confidence intervals , and used Bayesian multilevel models to estimate the effects of the representation styles on differences in subjective confidence of the results and preferences in visualization styles.
Revised:  We ran two experiments to compare several alternative representations of confidence intervals , and used Bayesian multilevel models to estimate the effects of the representation styles on differences in subjective confidence of the results and preferences in visualization styles.
Edit intent: fluency
Original:  We ran two experiments to compare several representation alternatives of confidence intervals , and used Bayesian multilevel models to estimate the effects of the representation styles on differences in subjective confidence of the results and preferences in visualization styles.
Revised:  We ran two experiments to compare several representation alternatives of confidence intervals and used Bayesian multilevel models to estimate the effects of the representation styles on differences in subjective confidence of the results and preferences in visualization styles.
Edit intent: fluency
Original:  We ran two experiments to compare several representation alternatives of confidence intervals , and used Bayesian multilevel models to estimate the effects of the representation styles on differences in subjective confidence of the results and preferences in visualization styles.
Revised:  We ran two experiments to compare several representation alternatives of confidence intervals , and used Bayesian multilevel models to estimate the effects of the representation styles on differences in subjective confidence in the results. We also asked the respondents' opinions and preferences in visualization styles.
Edit intent: meaning-changed
Original:  We ran two experiments to compare several representation alternatives of confidence intervals , and used Bayesian multilevel models to estimate the effects of the representation styles on differences in subjective confidence of the results and preferences in visualization styles.
Revised:  We ran two experiments to compare several representation alternatives of confidence intervals , and used Bayesian multilevel models to estimate the effects of the representation styles on differences in subjective confidence of the results and preferences in representation styles.
Edit intent: clarity
Original:  Our results suggest that adding visual information to classic CI representation can decrease the sudden drop around p-value 0.05 compared to classic CIs and textual representation of  CI with p-values.
Revised:  Our results suggest that adding visual information to classic CI representation can decrease the tendency towards dichotomous interpretations - measured as the "cliff effect": the sudden drop in confidence around p-value 0.05 compared to classic CIs and textual representation of CI with p-values.
Edit intent: meaning-changed
Original:  We show that isolating cycles in polyhedral graphs can be extended to larger ones: every isolating cycle C of length 8 \leq |E(C)| < \left \lfloor 2{3}(|V(G)|+ 3)\right \rfloor implies an isolating cycle C' of larger length that contains V(C).
Revised:  We show that isolating cycles in polyhedral graphs can be extended to larger ones: every isolating cycle C of length 6 \leq |E(C)| < \left \lfloor 2{3}(|V(G)|+ 3)\right \rfloor implies an isolating cycle C' of larger length that contains V(C).
Edit intent: meaning-changed
Original:  We show that isolating cycles in polyhedral graphs can be extended to larger ones: every isolating cycle C of length 8 \leq |E(C)| < \left \lfloor 2{3}(|V(G)|+ 3)\right \rfloor implies an isolating cycle C' of larger length that contains V(C).
Revised:  We show that isolating cycles in polyhedral graphs can be extended to larger ones: every isolating cycle C of length 8 \leq |E(C)| < \left \lfloor 2{3}(|V(G)|+ 4)\right \rfloor implies an isolating cycle C' of larger length that contains V(C).
Edit intent: meaning-changed
Original:  By ``hopping'' iteratively to such larger cycles, we obtain a powerful and very general inductive motor for proving and computing long cycles (we will give an algorithm with running time O(n^2)). This provides a method to prove  lower bounds on  Tutte cycles, as C' will be a Tutte cycle of G if C is.
Revised:  By ``hopping'' iteratively to such larger cycles, we obtain a powerful and very general inductive motor for proving and computing long cycles (we will give an algorithm with quadratic running time). This is the first step towards the so far elusive quest of finding a universal induction that captures longest cycles of polyhedral graph classes. Our motor provides also a method to prove lower bounds on Tutte cycles, as C' will be a Tutte cycle of G if C is.
Edit intent: meaning-changed
Original:  We also prove that E(C') \leq E(C)  +3 if G does not contain faces of size five, which gives a new tool for proving results about cycle spectra and evidence that these face sizes obstruct long cycles .
Revised:  We prove in addition that | E(C') \leq E(C) +3 if G does not contain faces of size five, which gives a new tool for proving results about cycle spectra and evidence that these face sizes obstruct long cycles .
Edit intent: meaning-changed
Original:  We also prove that E(C') \leq E(C)  +3 if G does not contain faces of size five, which gives a new tool for proving results about cycle spectra and evidence that these face sizes obstruct long cycles .
Revised:  We also prove that E(C') | \leq | E(C) +3 if G does not contain faces of size five, which gives a new tool for proving results about cycle spectra and evidence that these face sizes obstruct long cycles .
Edit intent: meaning-changed
Original:  We also prove that E(C') \leq E(C)  +3 if G does not contain faces of size five, which gives a new tool for proving results about cycle spectra and evidence that these face sizes obstruct long cycles .
Revised:  We also prove that E(C') \leq E(C) | +3 if G does not contain faces of size five, which gives a new tool for proving results about cycle spectra and evidence that these face sizes obstruct long cycles .
Edit intent: meaning-changed
Original:  We also prove that E(C') \leq E(C)  +3 if G does not contain faces of size five, which gives a new tool for proving results about cycle spectra and evidence that these face sizes obstruct long cycles .
Revised:  We also prove that E(C') \leq E(C) +3 if G contains no face of size five, which gives a new tool for proving results about cycle spectra and evidence that these face sizes obstruct long cycles .
Edit intent: clarity
Original:  We also prove that E(C') \leq E(C)  +3 if G does not contain faces of size five, which gives a new tool for proving results about cycle spectra and evidence that these face sizes obstruct long cycles .
Revised:  We also prove that E(C') \leq E(C) +3 if G does not contain faces of size five, which gives a new tool for results about cycle spectra and evidence that these face sizes obstruct long cycles .
Edit intent: coherence
Original:  We also prove that E(C') \leq E(C)  +3 if G does not contain faces of size five, which gives a new tool for proving results about cycle spectra and evidence that these face sizes obstruct long cycles .
Revised:  We also prove that E(C') \leq E(C) +3 if G does not contain faces of size five, which gives a new tool for proving results about cycle spectra , and provides evidence that faces of size five may obstruct long cycles .
Edit intent: clarity
Original:  We also prove that E(C') \leq E(C)  +3 if G does not contain faces of size five, which gives a new tool for proving results about cycle spectra and evidence that these face sizes obstruct long cycles . As a sample application, we test our motor on a conjecture on essentially 4-connected graphs.
Revised:  We also prove that E(C') \leq E(C) +3 if G does not contain faces of size five, which gives a new tool for proving results about cycle spectra and evidence that these face sizes obstruct long cycles in many graph classes. We test our motor on a conjecture on essentially 4-connected graphs.
Edit intent: meaning-changed
Original:  However, the  best known upper bound is given by an infinite family of such graphs in which every graph G on n vertices has no cycle longer than\left \lfloor \frac{2}{3}(n+4) \right \rfloor ;
Revised:  However, the currently best known upper bound is given by an infinite family of such graphs in which every graph G on n vertices has no cycle longer than\left \lfloor \frac{2}{3}(n+4) \right \rfloor ;
Edit intent: meaning-changed
Original:  However, the  best known upper bound is given by an infinite family of such graphs in which every graph G on n vertices has no cycle longer than\left \lfloor \frac{2}{3}(n+4) \right \rfloor ;
Revised:  However, the best known upper bound is given by an infinite family of such graphs in which no graph G contains a cycle that is longer than\left \lfloor \frac{2}{3}(n+4) \right \rfloor ;
Edit intent: clarity
Original: We study approximation algorithms for the following three string measures that are widely used in practice: edit distance  , longest common subsequence  , and longest increasing sequence .\ All three problems can be solved exactly by standard algorithms that run in polynomial time with roughly O (n) space, where n is the input length, and our goal is to design deterministic approximation algorithms that run in polynomial time with significantly smaller space.
Revised: We study approximation algorithms for the following three string measures that are widely used in practice: edit distance (ED) , longest common subsequence , and longest increasing sequence .\ All three problems can be solved exactly by standard algorithms that run in polynomial time with roughly O (n) space, where n is the input length, and our goal is to design deterministic approximation algorithms that run in polynomial time with significantly smaller space.
Edit intent: meaning-changed
Original: We study approximation algorithms for the following three string measures that are widely used in practice: edit distance  , longest common subsequence  , and longest increasing sequence .\ All three problems can be solved exactly by standard algorithms that run in polynomial time with roughly O (n) space, where n is the input length, and our goal is to design deterministic approximation algorithms that run in polynomial time with significantly smaller space.
Revised: We study approximation algorithms for the following three string measures that are widely used in practice: edit distance , longest common subsequence (LCS) , and longest increasing sequence .\ All three problems can be solved exactly by standard algorithms that run in polynomial time with roughly O (n) space, where n is the input length, and our goal is to design deterministic approximation algorithms that run in polynomial time with significantly smaller space.
Edit intent: clarity
Original: We study approximation algorithms for the following three string measures that are widely used in practice: edit distance  , longest common subsequence  , and longest increasing sequence .\ All three problems can be solved exactly by standard algorithms that run in polynomial time with roughly O (n) space, where n is the input length, and our goal is to design deterministic approximation algorithms that run in polynomial time with significantly smaller space.
Revised: We study approximation algorithms for the following three string measures that are widely used in practice: edit distance , longest common subsequence , and longest increasing sequence (LIS). All three problems can be solved exactly by standard algorithms that run in polynomial time with roughly O (n) space, where n is the input length, and our goal is to design deterministic approximation algorithms that run in polynomial time with significantly smaller space.
Edit intent: clarity
Original: We study approximation algorithms for the following three string measures that are widely used in practice: edit distance  , longest common subsequence  , and longest increasing sequence .\ All three problems can be solved exactly by standard algorithms that run in polynomial time with roughly O (n) space, where n is the input length, and our goal is to design deterministic approximation algorithms that run in polynomial time with significantly smaller space.
Revised: We study approximation algorithms for the following three string measures that are widely used in practice: edit distance , longest common subsequence , and longest increasing sequence .\ All three problems can be solved exactly by standard algorithms that run in polynomial time with roughly \Theta (n) space, where n is the input length, and our goal is to design deterministic approximation algorithms that run in polynomial time with significantly smaller space.
Edit intent: meaning-changed
Original:  Some of our algorithms can also be adapted to work in the asymmetric streaming model \mbox{%DIFAUXCMD saks2013space\hspace{0pt}%DIFAUXCMD }  , and output the corresponding sequence.
Revised:  Some of our algorithms can also be adapted to work in the asymmetric streaming model \hspace{0pt}%DIFAUXCMD } , and output the corresponding sequence.
Edit intent: coherence
Original:  Some of our algorithms can also be adapted to work in the asymmetric streaming model \mbox{%DIFAUXCMD saks2013space\hspace{0pt}%DIFAUXCMD }  , and output the corresponding sequence.
Revised:  Some of our algorithms can also be adapted to work in the asymmetric streaming model \mbox{%DIFAUXCMD saks2013space\hspace{0pt}%DIFAUXCMD } SS13 , and output the corresponding sequence.
Edit intent: meaning-changed
Original:   ]  ]  Our algorithms are based on the idea of using recursion as in Savitch's theorem \mbox{%DIFAUXCMD \cite{Savitch70}\hspace{0pt}%DIFAUXCMD }  , and a careful modification of previous techniques to make the recursion work.
Revised:  Furthermore, our results can be used to improve a recent result by Farhadi et. al. ] ] Our algorithms are based on the idea of using recursion as in Savitch's theorem \mbox{%DIFAUXCMD \cite{Savitch70}\hspace{0pt}%DIFAUXCMD } , and a careful modification of previous techniques to make the recursion work.
Edit intent: meaning-changed
Original:   ]  ]  Our algorithms are based on the idea of using recursion as in Savitch's theorem \mbox{%DIFAUXCMD \cite{Savitch70}\hspace{0pt}%DIFAUXCMD }  , and a careful modification of previous techniques to make the recursion work.
Revised:  FHRS20] ] Our algorithms are based on the idea of using recursion as in Savitch's theorem \mbox{%DIFAUXCMD \cite{Savitch70}\hspace{0pt}%DIFAUXCMD } , and a careful modification of previous techniques to make the recursion work.
Edit intent: meaning-changed
Original:   ]  ]  Our algorithms are based on the idea of using recursion as in Savitch's theorem \mbox{%DIFAUXCMD \cite{Savitch70}\hspace{0pt}%DIFAUXCMD }  , and a careful modification of previous techniques to make the recursion work.
Revised:  ] about approximating ED in the asymmetric streaming model, reducing the running time from being exponential in ] Our algorithms are based on the idea of using recursion as in Savitch's theorem \mbox{%DIFAUXCMD \cite{Savitch70}\hspace{0pt}%DIFAUXCMD } , and a careful modification of previous techniques to make the recursion work.
Edit intent: meaning-changed
Original:   ]  ]  Our algorithms are based on the idea of using recursion as in Savitch's theorem \mbox{%DIFAUXCMD \cite{Savitch70}\hspace{0pt}%DIFAUXCMD }  , and a careful modification of previous techniques to make the recursion work.
Revised:  ] FHRS20] Our algorithms are based on the idea of using recursion as in Savitch's theorem \mbox{%DIFAUXCMD \cite{Savitch70}\hspace{0pt}%DIFAUXCMD } , and a careful modification of previous techniques to make the recursion work.
Edit intent: meaning-changed
Original:   ]  ]  Our algorithms are based on the idea of using recursion as in Savitch's theorem \mbox{%DIFAUXCMD \cite{Savitch70}\hspace{0pt}%DIFAUXCMD }  , and a careful modification of previous techniques to make the recursion work.
Revised:  ] ] to a polynomial. Our algorithms are based on the idea of using recursion as in Savitch's theorem \mbox{%DIFAUXCMD \cite{Savitch70}\hspace{0pt}%DIFAUXCMD } , and a careful modification of previous techniques to make the recursion work.
Edit intent: meaning-changed