wanyu/IteraTeR_full_sent huggingface dataset:
Original:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) }  , uses O(  d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which  uses O(log^2 n) bits of space  .
Revised:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) } ) , uses O( d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which uses O(log^2 n) bits of space .
Edit intent: fluency
Original:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) }  , uses O(  d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which  uses O(log^2 n) bits of space  .
Revised:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) } , uses O( ( d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which uses O(log^2 n) bits of space .
Edit intent: fluency
Original:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) }  , uses O(  d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which  uses O(log^2 n) bits of space  .
Revised:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) } , uses O( d^2 + d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which uses O(log^2 n) bits of space .
Edit intent: fluency
Original:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) }  , uses O(  d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which  uses O(log^2 n) bits of space  .
Revised:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) } , uses O( d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which uses O(log^2 n) bits of space .
Edit intent: fluency
Original:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) }  , uses O(  d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which  uses O(log^2 n) bits of space  .
Revised:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) } , uses O( d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any natural number d . In particular, this yields a factor-O( d log n) approximation algorithm which uses O(log^2 n) bits of space .
Edit intent: clarity
Original:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) }  , uses O(  d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which  uses O(log^2 n) bits of space  .
Revised:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) } , uses O( d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( log n) approximation algorithm which uses O(log^2 n) bits of space .
Edit intent: meaning-changed
Original:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) }  , uses O(  d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which  uses O(log^2 n) bits of space  .
Revised:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) } , uses O( d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which runs in time n^{O(log n) uses O(log^2 n) bits of space .
Edit intent: meaning-changed
Original:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) }  , uses O(  d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which  uses O(log^2 n) bits of space  .
Revised:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) } , uses O( d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which uses O(log^2 n) bits of space (for constant d) .
Edit intent: clarity
Original:  We give a factor-2 approximation algorithm for Vertex Cover which runs in time n^{O(\Delta)} and uses O(\Delta log n) bits of space.
Revised:  We give a factor-2 approximation algorithm for Vertex Cover on graphs with maximum degree \Delta, and an algorithm for computing maximal independent sets which both run in time n^{O(\Delta)} and uses O(\Delta log n) bits of space.
Edit intent: meaning-changed
Original:  For Independent Set on } graphs with average degree d, we give a factor-(2d) approximation algorithm which runs in polynomial time and uses O(log n) bits of space.
Revised:  For the more general d-Hitting Set problem, we devise a factor-d approximation algorithm which runs in time n^{O(d {\delta } graphs with average degree d, we give a factor-(2d) approximation algorithm which runs in polynomial time and uses O(log n) bits of space.
Edit intent: meaning-changed
Original:  For Independent Set on } graphs with average degree d, we give a factor-(2d) approximation algorithm which runs in polynomial time and uses O(log n) bits of space.
Revised:  For Independent Set on }^2 log n) bits of space on set families where each element appears in at most \delta sets. For Independent Set restricted to graphs with average degree d, we give a factor-(2d) approximation algorithm which runs in polynomial time and uses O(log n) bits of space.
Edit intent: meaning-changed
Original:  For d-regular graphs, we observe that a known randomized algorithm which achieves an approximation ratio of O (log d)  can be derandomized and implemented to run in polynomial time and use O(log n) bits of space.
Revised:  For d-regular graphs, we show how a known randomized algorithm which achieves an approximation ratio of O (log d) can be derandomized and implemented to run in polynomial time and use O(log n) bits of space.
Edit intent: clarity
Original:  For d-regular graphs, we observe that a known randomized algorithm which achieves an approximation ratio of O (log d)  can be derandomized and implemented to run in polynomial time and use O(log n) bits of space.
Revised:  For d-regular graphs, we observe that a known randomized factor-O (log d) can be derandomized and implemented to run in polynomial time and use O(log n) bits of space.
Edit intent: clarity
Original:  For d-regular graphs, we observe that a known randomized algorithm which achieves an approximation ratio of O (log d)  can be derandomized and implemented to run in polynomial time and use O(log n) bits of space.
Revised:  For d-regular graphs, we observe that a known randomized algorithm which achieves an approximation ratio of O (log d) approximation algorithm can be derandomized and implemented to run in polynomial time and use O(log n) bits of space.
Edit intent: meaning-changed
Original:  For d-regular graphs, we observe that a known randomized algorithm which achieves an approximation ratio of O (log d)  can be derandomized and implemented to run in polynomial time and use O(log n) bits of space.
Revised:  For d-regular graphs, we observe that a known randomized algorithm which achieves an approximation ratio of O (log d) can be derandomized to run in polynomial time and use O(log n) bits of space.
Edit intent: clarity
Original:  For d-regular graphs, we observe that a known randomized algorithm which achieves an approximation ratio of O (log d)  can be derandomized and implemented to run in polynomial time and use O(log n) bits of space.
Revised:  For d-regular graphs, we observe that a known randomized algorithm which achieves an approximation ratio of O (log d) can be derandomized and implemented to run in time n^{O(1) and use O(log n) bits of space.
Edit intent: meaning-changed
Original:  In this study, we demonstrate that it is possible to make accurate \geq80 predictions of protein fold taxonomy from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% Angstroms)\leq3A .
Revised:  In this study, we demonstrate that it is possible to make accurate (\geq80 predictions of protein fold taxonomy from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% Angstroms)\leq3A .
Edit intent: fluency
Original:  In this study, we demonstrate that it is possible to make accurate \geq80 predictions of protein fold taxonomy from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% Angstroms)\leq3A .
Revised:  In this study, we demonstrate that it is possible to make accurate \geq80\%) predictions of protein fold taxonomy from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% Angstroms)\leq3A .
Edit intent: meaning-changed
Original:  In this study, we demonstrate that it is possible to make accurate \geq80 predictions of protein fold taxonomy from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% Angstroms)\leq3A .
Revised:  In this study, we demonstrate that it is possible to make accurate \geq80 predictions of protein class and architecture from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% Angstroms)\leq3A .
Edit intent: meaning-changed
Original:  In this study, we demonstrate that it is possible to make accurate \geq80 predictions of protein fold taxonomy from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% Angstroms)\leq3A .
Revised:  In this study, we demonstrate that it is possible to make accurate \geq80 predictions of protein fold taxonomy from structures determined at low (> 3A ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% Angstroms)\leq3A .
Edit intent: fluency
Original:  In this study, we demonstrate that it is possible to make accurate \geq80 predictions of protein fold taxonomy from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% Angstroms)\leq3A .
Revised:  In this study, we demonstrate that it is possible to make accurate \geq80 predictions of protein fold taxonomy from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution %DIFDELCMD < \leq3 %%% Angstroms)\leq3A .
Edit intent: fluency
Original:  In this study, we demonstrate that it is possible to make accurate \geq80 predictions of protein fold taxonomy from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% Angstroms)\leq3A .
Revised:  In this study, we demonstrate that it is possible to make accurate \geq80 predictions of protein fold taxonomy from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% (\leq3A .
Edit intent: coherence
Original:  In this study, we demonstrate that it is possible to make accurate \geq80 predictions of protein fold taxonomy from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% Angstroms)\leq3A .
Revised:  In this study, we demonstrate that it is possible to make accurate \geq80 predictions of protein fold taxonomy from structures determined at low (> 3 Angstroms ) resolution, using a deep convolutional neural network trained on high-resolution structures (%DIFDELCMD < \leq3 %%% Angstroms)\leq3A) structures represented as 2D matrices .
Edit intent: meaning-changed
Original:  Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution . We explore the relationship between the information content of the input image and the predictive power of the model, achieving state of the art performance on homologous superfamily prediction with maps of interatomic distance.
Revised:  Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution , and a basis for extension to prediction of function. We investigate the impact of the input image and the predictive power of the model, achieving state of the art performance on homologous superfamily prediction with maps of interatomic distance.
Edit intent: meaning-changed
Original:  We explore the relationship between the information content of the input image and the predictive power of the model, achieving state of the art performance on homologous superfamily prediction with maps of interatomic distance. Our findings contribute further evidence that inclusion of both amino acid alpha and beta carbon geometry in these maps improves classification performanceover purely alpha carbon representations, and show that side-chain information may not be necessary for fine-grained structure predictions.
Revised:  We explore the relationship between the information content of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions.
Edit intent: clarity
Original:  The `` multi-granular '' model incorporates linguistic knowledge at various levels of text granularity, including word, sentence and document level syntactic, semantic and pragmatic affect features, which significantly improve model performance, compared to its language-agnostic variant.
Revised:  The " multi-granular '' model incorporates linguistic knowledge at various levels of text granularity, including word, sentence and document level syntactic, semantic and pragmatic affect features, which significantly improve model performance, compared to its language-agnostic variant.
Edit intent: fluency
Original:  The `` multi-granular '' model incorporates linguistic knowledge at various levels of text granularity, including word, sentence and document level syntactic, semantic and pragmatic affect features, which significantly improve model performance, compared to its language-agnostic variant.
Revised:  The `` multi-granular " model incorporates linguistic knowledge at various levels of text granularity, including word, sentence and document level syntactic, semantic and pragmatic affect features, which significantly improve model performance, compared to its language-agnostic variant.
Edit intent: fluency
Original:  The final model is a majority-voting ensemble which learns different propaganda class boundaries by leveraging different subsets of incorporated knowledge .%DIFDELCMD < \footnote{%%% Our final ensemble attains 4^{th} position on the test leaderboard.
Revised:  The final model is a majority-voting ensemble which learns different propaganda class boundaries by leveraging different subsets of incorporated knowledge %DIFDELCMD < \footnote{%%% Our final ensemble attains 4^{th} position on the test leaderboard.
Edit intent: fluency
Original:  The final model is a majority-voting ensemble which learns different propaganda class boundaries by leveraging different subsets of incorporated knowledge .%DIFDELCMD < \footnote{%%% Our final ensemble attains 4^{th} position on the test leaderboard.
Revised:  The final model is a majority-voting ensemble which learns different propaganda class boundaries by leveraging different subsets of incorporated knowledge .%DIFDELCMD < \footnote{%%% and attains 4^{th} position on the test leaderboard.
Edit intent: coherence
Original: Miles et al. 1%DIFDELCMD < ] %%% recently produced an analysis of the  costs and benefits of lockdown policies in the face of COVID-19 , focussing on the case of the U. K.
Revised:  1%DIFDELCMD < ] %%% recently produced an analysis of the costs and benefits of lockdown policies in the face of COVID-19 , focussing on the case of the U. K.
Edit intent: clarity
Original: Miles et al. 1%DIFDELCMD < ] %%% recently produced an analysis of the  costs and benefits of lockdown policies in the face of COVID-19 , focussing on the case of the U. K.
Revised: Miles et al. %DIFDELCMD < ] %%% recently produced an analysis of the costs and benefits of lockdown policies in the face of COVID-19 , focussing on the case of the U. K.
Edit intent: clarity
Original: Miles et al. 1%DIFDELCMD < ] %%% recently produced an analysis of the  costs and benefits of lockdown policies in the face of COVID-19 , focussing on the case of the U. K.
Revised: Miles et al. 1%DIFDELCMD < ] %%% I critique a recent analysis (Miles, Stedman costs and benefits of lockdown policies in the face of COVID-19 , focussing on the case of the U. K.
Edit intent: meaning-changed
Original: Miles et al. 1%DIFDELCMD < ] %%% recently produced an analysis of the  costs and benefits of lockdown policies in the face of COVID-19 , focussing on the case of the U. K.
Revised: Miles et al. 1%DIFDELCMD < ] %%% recently produced an analysis of the Heald, 2020) of COVID-19 lockdown costs and benefits of lockdown policies in the face of COVID-19 , focussing on the case of the U. K.
Edit intent: meaning-changed
Original: Miles et al. 1%DIFDELCMD < ] %%% recently produced an analysis of the  costs and benefits of lockdown policies in the face of COVID-19 , focussing on the case of the U. K.
Revised: Miles et al. 1%DIFDELCMD < ] %%% recently produced an analysis of the costs and benefits , focussing on the case of the U. K.
Edit intent: clarity
Original: Miles et al. 1%DIFDELCMD < ] %%% recently produced an analysis of the  costs and benefits of lockdown policies in the face of COVID-19 , focussing on the case of the U. K. They argue that the March- June UK lockdown was more costly than the benefit of lives saved, if the latter are evaluated using the NICE threshold of {\pounds}30000 for a quality-adjusted life year (QALY)
Revised: Miles et al. 1%DIFDELCMD < ] %%% recently produced an analysis of the costs and benefits of lockdown policies in the face of COVID-19 , focussing on the United Kingdom (UK). Miles et al. (2020) argue that the March- June UK lockdown was more costly than the benefit of lives saved, if the latter are evaluated using the NICE threshold of {\pounds}30000 for a quality-adjusted life year (QALY)
Edit intent: clarity
Original:  They argue that the March- June UK lockdown was more costly than the benefit of lives saved, if the latter are evaluated using the NICE threshold of {\pounds}30000 for a quality-adjusted life year (QALY)
Revised:  They argue that the March-June UK lockdown was more costly than the benefit of lives saved, if the latter are evaluated using the NICE threshold of {\pounds}30000 for a quality-adjusted life year (QALY)
Edit intent: fluency
Original:  They argue that the March- June UK lockdown was more costly than the benefit of lives saved, if the latter are evaluated using the NICE threshold of {\pounds}30000 for a quality-adjusted life year (QALY)
Revised:  They argue that the March- June UK lockdown was more costly than the benefit of lives saved, evaluated using the NICE threshold of {\pounds}30000 for a quality-adjusted life year (QALY)
Edit intent: clarity
Original:  . Looking forwards, they argue that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than the benefits under any plausible QALY costing, even in a scenario in which easing lockdown led to a second infection wave that caused more than 7000 deaths a week by mid-September.
Revised:  and that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than the benefits under any plausible QALY costing, even in a scenario in which easing lockdown led to a second infection wave that caused more than 7000 deaths a week by mid-September.
Edit intent: clarity
Original:  . Looking forwards, they argue that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than the benefits under any plausible QALY costing, even in a scenario in which easing lockdown led to a second infection wave that caused more than 7000 deaths a week by mid-September.
Revised:  . Looking forwards, they argue that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than any plausible estimate of the benefits, even if easing produced a second infection wave that caused more than 7000 deaths a week by mid-September.
Edit intent: clarity
Original:  . Looking forwards, they argue that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than the benefits under any plausible QALY costing, even in a scenario in which easing lockdown led to a second infection wave that caused more than 7000 deaths a week by mid-September.
Revised:  . Looking forwards, they argue that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than the benefits under any plausible QALY costing, even in a scenario in which easing lockdown led to a second infection wave causing over 7000 deaths a week by mid-September.
Edit intent: fluency